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ISBN 978-952-62-2218-9 (Paperback)ISBN 978-952-62-2219-6 (PDF)ISSN 0355-323X (Print)ISSN 1796-2242 (Online)
U N I V E R S I TAT I S O U L U E N S I SACTAE
SCIENTIAE RERUM SOCIALIUM
E 184
AC
TAH
éctor Javier Pijeira D
íaz
OULU 2019
E 184
Héctor Javier Pijeira Díaz
ELECTRODERMAL ACTIVITY AND SYMPATHETIC AROUSAL DURING COLLABORATIVE LEARNING
UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF EDUCATION
ACTA UNIVERS ITAT I S OULUENS I SE S c i e n t i a e R e r u m S o c i a l i u m 1 8 4
HÉCTOR JAVIER PIJEIRA DÍAZ
ELECTRODERMAL ACTIVITY AND SYMPATHETIC AROUSAL DURING COLLABORATIVE LEARNING
Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Human Sciences ofthe University of Oulu for public defence in the OPauditorium (L10), Linnanmaa, on 3 May 2019, at 12 noon
UNIVERSITY OF OULU, OULU 2019
Copyright © 2019Acta Univ. Oul. E 184, 2019
Supervised byProfessor Sanna JärveläProfessor Paul A. KirschnerProfessor Hendrik Drachsler
Reviewed byProfessor Petri NokelainenAssociate Professor Daniel Spikol
ISBN 978-952-62-2218-9 (Paperback)ISBN 978-952-62-2219-6 (PDF)
ISSN 0355-323X (Printed)ISSN 1796-2242 (Online)
Cover DesignRaimo Ahonen
JUVENES PRINTTAMPERE 2019
OpponentProfessor Timo Tobias Ley
Pijeira Díaz, Héctor Javier, Electrodermal activity and sympathetic arousal duringcollaborative learning. University of Oulu Graduate School; University of Oulu, Faculty of EducationActa Univ. Oul. E 184, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland
Abstract
This dissertation investigates high school students’ individual and interpersonal physiology ofelectrodermal activity (EDA) during collaborative learning in naturalistic settings. EDA is anindex of sympathetic arousal, which is concomitant with cognitive and affective processes.
Two data collections were organized with students working collaboratively in triads. The firstone took place during the performance of a science task, and the second during two runs of a six-week advanced physics course. The data collected included EDA (measured unobtrusively usingEmpatica® E3 and E4 wristbands), performance measures (pre- and post-tests, task solutions, andcourse exam), and questionnaires on cognitive, affective, and collaborative aspects of learning.The work was reported in three articles.
The results indicate that, on average, students spent more than half (60%) of the class at a lowarousal level, possibly signaling relaxation, disengagement, or boredom. Most of the time(≈60–95% of the lesson), triad members were at a different arousal level, which might indicatethat students took turns (alternating task-doers) in executing the task or applied some division oflabor rather than truly collaborating. In terms of achievement, sympathetic arousal during theexam was a predictor of the exam grades, and pairwise directional agreement of EDA waspositively and highly correlated to the dual learning gain. Arousal contagion could have occurredin up to 41% of the high arousal intervals found. The possible arousal contagion cases took placemostly on a 1:1 basis (71.3%), indicating that interactions in a collaborative learning triad seem tooccur mainly between two members rather than among the three.
The findings provide an ecologically-valid picture of the students’ EDA responses in theclassroom, both individually and collaboratively, benefiting from the connection of arousal tocognitive and affective processes to increase the saliency of otherwise elusive phenomena.Methodologically, the study contributes to the exploration and exploitation ofpsychophysiological approaches for collaborative learning research. On a practical level, itprovides physiological indices that could be incorporated into learning analytics dashboards tosupport students’ awareness and reflection, and teachers’ pedagogical practices.
Keywords: collaborative learning, electrodermal activity, interpersonal physiology,psychophysiology, sympathetic arousal
Pijeira Díaz, Héctor Javier, Elektrodermaalinen aktiivisuus ja sympaattinenvireystila yhteisöllisen oppimisen aikana. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Kasvatustieteiden tiedekuntaActa Univ. Oul. E 184, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto
Tiivistelmä
Tässä väitöstutkimuksessa tarkastellaan elektrodermaalista aktiivisuutta (EDA) ja tästä johdet-tua sympaattista vireystilaa ja fysiologisia indeksejä, samanaikaisesti yksilöiden ja yksilöidenvälisten kognitiivisten ja affektiivisten prosessien kanssa.
Tutkimusaineisto kerättiin yhteisöllisen oppimisen tilanteista, joissa oppilaat työskentelivätkolmen hengen ryhmissä. Ensimmäinen osa aineistosta kerättiin oppilaiden suorittaessa luon-nontieteiden alan tehtävää ja toinen kahden fysiikan syventävän kurssin aikana. Aineistoon sisäl-tyi EDA (Empatica® E3- ja E4-rannekkeista), oppimisen mittaukset (alku- ja lopputestit, tehtä-vien ratkaisut ja kurssikokeet) sekä kyselylomakkeet oppimisen kognitiivisista, affektiivisista jayhteisöllisen työskentelyn näkökulmista. Tutkimus on raportoitu kolmessa artikkelissa.
Tulokset osoittavat, että opiskelijoiden sympaattisen hermoston vireystila oli keskimäärin ylipuolet (60 %) luokkatyöskentelystä alhainen, mikä viittaa mahdolliseen rentoutumiseen, osallis-tumisen puutteeseen tai tylsistymiseen. Ryhmänjäsenet olivat suurimman osan ajasta (≈60-95 %)eri vireystilan tasoilla, mikä voi tarkoittaa, että he suorittivat tehtävää vuorotellen (tehtävän suo-rittajaa vaihdellen) tai jonkinlaista työnjakoa käyttäen, yhteisöllisen työskentelyn sijaan. Sym-paattinen vireystila kurssikokeessa ennusti kokeen arvosanoja. Lisäksi oppilasparien EDA:nsamansuuntaisuus korreloi vahvasti oppimistulosten kanssa. Yksilöiden välillä tapahtuvaa sym-paattisen vireystilan ”tarttumista” on voinut esiintyä jopa 41 prosentissa todetuista korkean vire-ystilan intervalleista. Mahdolliset ”tarttumiset” ilmenivät enimmäkseen (71,3 %) 1:1 suhteessa,mikä viittaa siihen, että vuorovaikutus yhteisöllisessä oppimisessa näyttäisi tapahtuvan pääasias-sa kahden yksilön välillä kaikkien kolmen sijaan.
Tulokset tarjoavat ekologisesti validin kuvan opiskelijoiden EDA-reaktioista luokkahuonees-sa sekä yksilöllisesti että yhteisöllisesti tarkasteltuna, selventäen samalla kuvaa sympaattisenvireystilan yhteydestä kognitiivisiin ja affektiivisiin prosesseihin. Menetelmällisesti tutkimuskartoittaa psykofysiologisen lähestymistavan mahdollisuuksia yhteisöllisen oppimisen tutkimuk-sessa. Se esittelee fysiologisia indeksejä, jotka voitaisiin visualisoida oppimisen analytiikansovelluksissa opiskelijoiden tietoisuuden ja reflektion sekä opettajien pedagogisten käytäntöjentukemiseksi.
Asiasanat: elektrodermaalinen aktiivisuus, ihmistenvälinen fysiologia, psykofysiologia,sympaattinen vireystila, yhteisöllinen oppiminen
9
Acknowledgements It was the early spring of 2015 when I started this journey to the Ph.D. Along the
way, I have felt privileged to count on the supervision of eminent professors such
as Sanna Järvelä (University of Oulu), Paul A. Kirschner (Open University of the
Netherlands), and Hendrik Drachsler (Goethe University Frankfurt), to whom I am
profoundly grateful. I am especially indebted to Sanna Järvelä, my principal
supervisor, for her support in so many ways throughout these years. This Ph.D.
would simply not have been possible without Sanna’s involvement and leadership,
and her innate capacity to motivate and inspire people with her kindness, result-
orientation, and passion for excellence. Thank you, Sanna, for being always
available when I needed it, no matter how busy you were; for all the expert guidance
of a natural leader like you, and for embracing multidisciplinarity to advance the
learning sciences. To Paul A. Kirschner I am grateful for the insightful scientific
discussions, for his honest commentary on my work, and for his efforts in shaping
my scientific writing skills with a focus on structure, clarity, line of argument, and
coherence. From his keen remarks as an experienced editor, I have learned much
of what I now about academic writing formalities and the APA style, for example.
Thank you, Paul, also for always coming to my conference presentations despite
your tight agenda as a sought-after expert in those events, and for the feedback
afterwards. To Hendrik Drachsler I am thankful for his dedication to getting me
started with the Ph.D. endeavor, for helping me to pave the way for independent
work to advance learning analytics, for introducing me to valuable tools and
research methods, and in general, for the much-appreciated guidance and
opportunities along these four years.
I would like to express my gratitude to Professor Timo Tobias Ley (Tallinn
University), an internationally recognized expert in learning analytics and
educational innovation, who is highly experienced in multidisciplinary work, for
accepting to act as opponent at the public defense of this dissertation. For their
meticulous work as pre-examiners of this dissertation, through which they provided
me with insightful, actionable, and critically constructive feedback, I want to thank
Engineering Pedagogy Professor Petri Nokelainen (Tampere University of
Technology) and Associate Professor Daniel Spikol (Malmö University). Similarly,
I am deeply grateful to the Academy of Finland post-doctoral research fellows Piia
Näykki and Sonja Lutovac, for their elaborate, highly-detailed, and thorough
commentary on a previous version of this dissertation, as part of the corresponding
pre-defense seminar at the Faculty of Education.
10
During my doctoral studies, annual meetings with my follow-up group helped
me to evaluate my progress and to keep my research plan up-to-date. I would like
to show my appreciation to the chair of this group, Adjunct Professor Arto Hautala
(Cardiovascular Research Group, Oulu University Hospital), as well as to the other
two members: Dr. Miki Kallio (Unit for Strategy and Science Policy) and Dr. Arttu
Mykkänen (Learning and Educational Technology research group; LET). I am
thankful to this multidisciplinary group for their diligent work in this role,
constructive feedback, experiences shared, and useful suggestions.
The University of Oulu Graduate School (UniOGS) does a great job to, as
indicated in their goal statement, “provide the framework and conditions for high-
quality, research-driven doctoral education.” I would like to acknowledge their
work in helping us doctoral candidates to acquire general and transferable skills
through the several courses they organize on ethics, scientific communication,
intellectual property rights, and university pedagogy, just to name a few. Special
thanks to Anthony Heape and Titta Kallio-Seppä for their guidance on procedures
and paperwork.
The final revision of this dissertation in terms of (APA) style, formatting, and
conventions was carried out by the series editor, University Lecturer Veli-Matti
Ulvinen. I would like to show my appreciation for his rigorousness, precision,
guidance, quick reaction, and clear and didactic suggestions.
Although the work on a dissertation is independent, my Ph.D. studies have
benefited from the frequent scientific activities organized at the LET research group,
including, but not limited to, reading circles, writing Wednesdays, seminars,
presentations by renowned international experts, workshops, strategy days, and
theoretical discussions. For that, and for an unparalleled work atmosphere, I thank
each and every one at the LET team. You have been welcoming and supportive. I
am lucky to have you all as colleagues. Special thanks to Márta Sobocinski, Eetu
Haataja, and my lunch partner in crime Muhterem Dindar. Together with Márta and
Eetu, I started the immersion in the promising world of psychophysiology and
interpersonal physiology. We became avid readers on the topic and were often
sharing and discussing what we learned and what it meant for our respective
research interests. Theories, methods, findings, unsolved questions, challenges...
Our discussions were illuminating on both the possibilities and the difficulties. In
addition, I thank Márta, the first person I talked to after my arrival at Oulu Airport,
for her generosity and invaluable practical support for this adventurer to settle down
in his new home and workplace. To Muhterem, I am grateful for countless affable
discussions over lunch on the nature and practicalities of science and scientific
11
lifestyle, as well as on more mundane topics; and most importantly, for his
friendship.
In the first year of my Ph.D. studies, I had a special collaborator from the
Center of Wireless Communications, M.Sc. Abdul Moiz. Together, we set up our
own instance of the Open edX platform on an Ubuntu Server to be used in the
second data collection of this dissertation, explored possibilities for real-time
biosensor-data collection, and designed and populated a MySQL database, among
other engineering tasks. During the months we were closely working together, we
faced technical challenges, made design decisions, and in general, worked hard to
get the system up-and-running. It was a practical and hands-on work. I would like
to thank Moiz for his commitment to the project, hard work, contagious motivation,
for fruitful discussions in design meetings, and for so many productive
disagreements which led to the best solution for the project. Beyond work, his
dependable friendship made a difference and is one of the highlights of my stay in
Oulu. Thank you, Moiz.
This dissertation has been supported by the Academy of Finland via the SLAM
project (grant number 275440), and the Faculty of Education. I express my
gratitude to both institutions. In addition, I consider myself fortunate to have been
part of the Faculty of Education these years, a pleasant and supportive working
environment, packed with friendly people. I want to thank the Faculty
administration and technical staff for their work to support research. I would also
like to acknowledge my two long-term office mates, Markus Packalen and Laura
Palmgren-Neuvonen. It was a pleasure sharing office with them, and our short
conversations on science, everyday life, sports, languages, Finnish culture, travel,
and so on, provided an excellent break from periods of intense concentration. I
definitely enjoyed my time at KTK328, my research home for most of the Ph.D.
studies.
My first scientific research experience comes from the time when I was
working on my master’s thesis under the supervision of Associate Professor Pedro
J. Muñoz Merino (Universidad Carlos III de Madrid). The work on learning
analytics visualizations from a computer science perspective paved the way for my
discovery of the learning sciences and the research field of technology-enhanced
learning. I am thankful to Pedro for his exemplary work ethics, which inspired me,
and for introducing me to this opportunity for doing my Ph.D. studies in a
multidisciplinary, international environment.
Scientific writing is a skill that develops over time, and (in)formative,
professional commentary directly on our writing equip us with valuable knowledge
12
of formalities, formatting, conventions, word choice, and clarity, among other
aspects. In this sense, I would like to show my appreciation for the work of
Scribendi’s editor 1081.
A Ph.D. journey is a roller-coaster ride spanning practically all the spectrum of
mental states and emotions: excitement, confusion, enjoyment, frustration,
engagement, anxiety, flow, disgust, hope, disappointment, pride, fear, surprise,
gratitude, and the list goes on. Therefore, beyond the academic setting, friends and
family play an indispensable role in helping us regulate those emotions, keeping
the motivation high, putting things in perspective, and strengthening our resilience.
In Oulu, I have found an amazing gang from the four corners of the earth, who, in
one way or another, have contributed to this journey. There are so many of them
that it is virtually impossible to name them all, but huge thanks to each and every
one of them for their kindness, time, company, and generosity. In addition to those
aforementioned, I would like to offer my special appreciation to my YOK18 partner
in crime and close friend Uzair Khan, Laura Jalkanen (to whom I am also indebted
for the initial version of the Finnish abstract of this dissertation), Sonia Saher, Lisi
Herrera, Onel Alcaraz López, and Ilaria Gabbatore. Friendship blossoms anywhere.
Naturally, due to geographical reasons, I have spent more time during the Ph.D.
with my friends in Oulu. However, my gratitude also goes to my dear friends from
Spain and Cuba, and family therein. Special mention to Alejandro Reyes
Bascuñana, Marielena García López, Guillermo López Lagomasino, Carmen
Gutiérrez Rodríguez, and Lucía Hidalgo Sánchez. In Oulu, volleyball has been my
favorite hobby during these Ph.D. years. Playing volleyball several times a week
has proved an effective strategy to maintaining a positive attitude and my signature
smile on my face, especially on moments of setback and despair. Accordingly, I
would like to acknowledge the host of people with whom I have played volleyball
through these years. I am glad I discovered this healthy hobby early enough in my
Ph.D. studies.
Last but not least, my whole life would not be enough to thank the two persons
this dissertation is dedicated to, my parents. You have stimulated my passion for
learning since my early days. You have supported me in every possible way, and
you have given your all for my education, not only in terms of knowledge, but also
in values and principles. I owe you everything. Thanks for the gift of life, the
unconditional love, and for being there through thick and thin. This is for you both.
March 14th, 2019 Héctor J. Pijeira Díaz
13
Abbreviations EDA electrodermal activity
GSR galvanic skin response
MSLQ motivated strategies for learning questionnaire
PCI physiological coupling index
PGR psychogalvanic response
RQ research question
SCL skin conductance level
SCR skin conductance response
15
List of original publications This thesis is based on the following publications, which are referred throughout
the text by their Roman numerals:
I Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 64–73). New York, NY: ACM. https://doi.org/10.1145/2883851.2883897
II Pijeira-Díaz, H. J., Drachsler, H., Kirschner, P. A., & Järvelä, S. (2018). Profiling sympathetic arousal in a physics course: How active are students? Journal of Computer Assisted Learning, 34(4), 397–408. https://doi.org/10.1111/jcal.12271
III Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2019). Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members? Computers in Human Behavior, 92(March), 188–197. https://doi.org/10.1016/j.chb.2018.11.008
The articles are co-authored with the three supervisors of this dissertation, who
offered guidance during the process. The author of this dissertation, first author in
all three publications, was responsible for empirical data collection, data analysis,
theoretical grounding and writing.
17
Contents Abstract
Tiivistelmä
Acknowledgements 9 Abbreviations 13 List of original publications 15 Contents 17 1 Introduction 19 2 Theoretical framework 27
2.1 Collaborative learning ............................................................................. 27 2.2 Electrodermal activity ............................................................................. 29 2.3 Arousal .................................................................................................... 33
2.3.1 Cognition, affect, and arousal ....................................................... 34 2.3.2 Arousal and performance ............................................................. 38 2.3.3 Arousal in the classroom .............................................................. 39
2.4 Interpersonal physiology ......................................................................... 40 2.4.1 Previous work on interpersonal physiology in
collaborative learning ................................................................... 42 2.4.2 Arousal contagion ......................................................................... 43 2.4.3 Physiological coupling indices ..................................................... 44
2.5 Research gap ........................................................................................... 46 3 Aims 49 4 Methods 51
4.1 Participants .............................................................................................. 51 4.2 Materials ................................................................................................. 52
4.2.1 The Empatica® E3 and E4 wristbands ......................................... 52 4.2.2 Performance instruments .............................................................. 54 4.2.3 Online learning environments ...................................................... 54 4.2.4 Questionnaires .............................................................................. 55
4.3 Procedure ................................................................................................ 56 4.3.1 Data collection I ........................................................................... 56 4.3.2 Data collection II .......................................................................... 57
4.4 Data analysis ........................................................................................... 58 4.4.1 Electrodermal activity .................................................................. 58 4.4.2 Performance .................................................................................. 63 4.4.3 Questionnaires .............................................................................. 64
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4.5 Research evaluation ................................................................................ 64 4.6 Ethics ....................................................................................................... 67
5 Overview of the articles 69 5.1 Article I: Investigating collaborative learning success with
physiological coupling indices based on electrodermal activity ............. 69 5.2 Article II: Profiling sympathetic arousal in a physics course:
How active are students? ......................................................................... 70 5.3 Article III: Sympathetic arousal commonalities and arousal
contagion during collaborative learning: How attuned are triad
members? ................................................................................................ 71 6 Main findings and discussion 73
6.1 Triad arousal levels in collaborative learning .......................................... 73 6.2 Arousal during exams and achievement .................................................. 75 6.3 Pairwise directional agreement and dual learning gain ........................... 76 6.4 Within-triad high arousal contagion in collaborative learning ................ 77
7 Conclusions 79 List of references 83 Original publications 99
19
1 Introduction Successful collaborative learning endeavors have long been promoted and pursued
by educational institutions at national and international levels as a way to attend to
students’ academic needs and future employability as well as to the practical
consideration of sharing scarce resources (e.g., computers and laboratory
equipment; M. Baker, 2015). Collaborative learning has been used in instruction to
a greater or lesser extent since at least the second half of the 19th century (Johnson
& Johnson, 2002), as it is widely believed that individual outcomes (e.g., learning
gain) will be favored by mutual intellectual engagement (Kuhn, 2015).
Collaborative learning has been actively and extensively studied since it entered
the educational research agenda in the 1970s (M. Baker, 2015), spurred by
awareness of societal relevance and embraced by a vibrant community of learning
scientists (Hoadley, 2018). Apart from the desired higher order thinking, an early
review (Slavin, 1980) reported other benefits of collaborative learning such as the
development of prosocial behavior. Furthermore, efficient collaboration is among
those skills beyond domain knowledge that are permanently in high demand in
today’s extremely specialized labor market. Specialization translates into
distributed expertise that, when synergistically combined, constitutes a competitive
advantage for organizations. Thus, organizations need expert collaborators or team
players to thrive in a highly competitive global ecosystem. Such orientation to
collaboration is expected to have already been developed as students advance
through their academic years. Therefore, the development of collaborative learning
has been embraced and promoted by educational stakeholders including
practitioners, organizational administrators, and policy-makers.
Shaped by theoretical and/or technological advances, as efforts to capitalize on
the affordances of collaborative learning, a variety of methods has been applied
across the decades in schools such as jigsaw (Aronson, Blaney, Sikes, & Snapp,
1978; Aronson & Patnoe, 2011), structured academic controversy, reciprocal
teaching, student teams achievement division, and others cited by Koschmann
(1996), such as expeditionary learning, group investigation, problem-based
learning, and project-based learning, as well as several forms of small-group
learning (A. L. Brown, 1992; Slavin, 1980; Webb, 1991). Based on societal needs,
it is no surprise not only that collaborative learning pedagogies have thrived, but
also that they are increasingly emphasized in educational systems through the
design of curricula and instruction (OECD, 2017).
20
Before going any further, it is important to acknowledge that the terms
collaborative and cooperative learning have sometimes been used interchangeably,
while at other times an explicit distinction is made between them (M. Baker, 2015;
Dillenbourg, Baker, Blaye, & O’Malley, 1996). This dissertation follows the
distinction posed by Dillenbourg (1999), in which cooperation is seen as labor
division (i.e., partners split the work, solve sub-tasks individually, and then
assemble the partial results into the final output), while in collaboration, partners
do the work together. Aligned with that distinction, this dissertation adopts the most
widely used definition of collaboration (Sanna Järvelä et al., 2015), that of
Roschelle and Teasley (1995, p. 70, emphasis added), which states that
“collaboration is a coordinated, synchronous activity that is the result of a
continued attempt to construct and maintain a shared conception of a problem.” It
is acknowledged also that in real-life scenarios, collaboration and cooperation are
hardly separable, given the nature of group work, which often requires a
combination of the two (Jeong & Hmelo-Silver, 2016). Nonetheless, the focus here
is on the joint work of learners and, therefore, on collaborative learning, understood
as previously defined.
In terms of learning, the process of collaboration is as important as the outcome,
since the aim is often not only that students reach a correct problem solution, but
also that they acquire the skills to tackle such problems more efficiently in the
future, whether together or alone, in virtue of the appropriation of a co-elaborated,
deeper task-domain conceptual understanding (M. Baker, 2015). The success of the
collaborative endeavor, however, cannot be taken for granted. Neutral and negative
effects of collaborative learning have also been reported in the scientific literature
(P. A. Kirschner, Sweller, Kirschner, & Zambrano, 2018; Kuhn, 2015; Mullins,
Rummel, & Spada, 2011). On the negative side, for example, it has been found that
low achievers progressively become passive when collaborating with high
achievers (Dillenbourg et al., 1996), that groups sometimes engage in destructive
interactions (Cohen, 1994), including interpersonal aggression (M. Baker, 2015)
and blaming each other for making mistakes (Miyake & Kirschner, 2014), and that
collaboration may even lead to a decline in thinking quality due to overconfidence
produced by the group interaction (Kuhn, 2015). The complexity of the outcomes
of collaborative learning, ranging from the positive and highly desirable to the
negative and detrimental for learning, has naturally attracted the interest of
educational researchers, who want to understand the phenomenon so that,
ultimately, collaboration can be properly structured and adequate interventions can
be designed.
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Three paradigms have been used to study collaborative learning: the effect
paradigm, the conditions paradigm, and the interactions paradigm (see Dillenbourg
et al., 1996). In the effect paradigm, the question is whether collaborative learning
is more efficient than learning alone. Positive outcomes have largely dominated the
results, but some of the negative effects previously mentioned are stable and well
documented (Dillenbourg et al., 1996). The variability in the findings led
researchers to investigate under what conditions collaborative learning is efficient,
which is the question the conditions paradigm tries to answer. When it comes to the
quality of problem solutions and learning outcomes, whether working together is
more effective than working alone depends indeed on factors such as the task-
complexity, group size, differences in prior knowledge, age, team-member
familiarity, and social skills (M. Baker, 2015; P. A. Kirschner et al., 2018). The
many independent variables in this paradigm (i.e., the conditions) were shown not
to have simple effects on the learning outcomes but instead to interact with each
other in a complex, virtually inextricable way (Dillenbourg et al., 1996). Research
then moved to explore intermediate variables that describe the interactions among
group members to understand sources of variability in collaborative outcomes
(Barron, 2000). The interactions paradigm, with higher granularity than its
predecessors in terms of unit of analysis, is concerned with the questions of which
interactions occur under which conditions and what effects these interactions do
have (Dillenbourg et al., 1996). It was found that the simple frequency of
interaction does not predict individual achievement in collaborative learning
(Cohen, 1994), pointing to the need of characterizing the interactions.
Theories of learning (e.g., socio-cultural and situated cognition theories)
relocating the attention from the individual to the activity structures in which
learning occurs, highlighted the role of discourse in conceptual development
(Barron, 2000). As an object of study, communication provides a window into the
cognitive and social processes related to collaborative skills, such as grounding,
mutual goal establishment, progress toward goals, and sharing perspectives (Kuhn,
2015). Types of verbal interactions of interest as learning mechanisms were, for
example, negotiation, considered an indicator of joint involvement in task solutions,
and argumentation, seen as a possible means for resolving socio-cognitive conflict
(Beers, Boshuizen, Kirschner, & Gijselaers, 2007; Dillenbourg et al., 1996). The
study of productive verbal communication has made extensive use of conversation
analysis (Sacks, 1972), a methodology from the sociological research tradition
which continues to be an important tool to analyze and make detailed meaning of
interactions in collaborative learning (Stahl, Koschmann, & Suthers, 2014). Audio
22
and video recordings, together with the development of specialized software, have
enabled code and count analysis of content data, which accounts for much of the
field’s analytic efforts (Jeong, Hmelo-Silver, & Yu, 2014). However, those analyses
are time-consuming, very expensive microgenetic methods and do not scale up well
(Wise & Schwarz, 2017). Steps toward automation of content analysis have been
taken using natural language processing technologies (Blikstein & Worsley, 2016;
Kelly, Thompson, & Yeoman, 2015), but analyses of verbal data are often complex
(Chi, 1997) for both human coders and machines. In addition to the cost and
limitations of content analysis, and although verbal communication has
understandably taken the lion’s share of process analysis focus in collaborative
learning, researchers acknowledge that, to properly understand the collaborative
process, it is also necessary to take into account other features integral to the
collaboration process (e.g., nonverbal communication; M. Baker, 2015).
Conversational processes alone do not explain the effects observed (Dillenbourg et
al., 1996).
The complex processes of collaboration present a challenge for consistent,
accurate, and reliable measurement across individuals and across user populations
(OECD, 2017). A wide range of data sources has been used to different extents to
study collaborative learning, with increased reliance on questionnaires, interviews,
and observation (Jeong et al., 2014). However, process-oriented approaches,
encouraged by the interactions paradigm, have made patent the need for new tools
for analyzing and modelling interactions (Stahl et al., 2014). Collaborative learning
is a process that occurs over time, but quantitative research has traditionally focused
mostly on relationships between relatively static input and outcome variables (Wise
& Schwarz, 2017). There is room for development, so the field needs to explore a
larger repertoire of analytic strategies and data modalities (Jeong et al., 2014; Vogel
& Weinberger, 2018), especially to address the need for real-time assessment
measures (S. Dawson & Siemens, 2014).
In this context, novel technologies, innovative developments, and applications
of established technologies are opening up attractive research opportunities, with
wearable sensors being a prominent example. Wearable sensors are increasingly
part of everyday life as standalone devices or are embedded into accessories, such
as smartwatches, mostly for sports and fitness activity tracking (Swan, 2012). But
the affordances of wearable sensors for continuous and unobtrusive measurement
of physiological responses and human behavior have found research applications
in such areas as healthcare (e.g., Koskimäki et al., 2017; Pentland, 2004) and
marketing (e.g., Bettiga, Lamberti, & Noci, 2017), among other fields. In learning
23
research, the affordances to study cognitive-affective dimensions have also been
recognized (Schneider, Börner, van Rosmalen, & Specht, 2015), as well as the
“unprecedented insight into the minute-by-minute development” of social
interactions (Blikstein & Worsley, 2016, p. 222). Wearable sensors, such as
necklace microphones, can be used as sociometric badges (Kim, Mcfee, Olguin,
Waber, & Pentland, 2012) to track the structural features of verbal communication,
including speech overlapping, distribution, and contributions from individual team
members. The biosensor type of wearables provide access to physiological
responses, whose capability to make salient psychological cognitive and affective
processes is the target of study of the field of psychophysiology (Cacioppo &
Tassinary, 1990b).
Fig. 1. Nervous system simplification.
One of the most widely used physiological responses in psychophysiology is
electrodermal activity (EDA), which refers to the variation in the electrical
conductivity of the skin (M. E. Dawson, Schell, & Filion, 2017; Kreibig, 2010).
The electrodermal system has a particularity. This is shown in the simplified
scheme of the different branches of the nervous system displayed in Fig. 1,
representing how the electrodermal system is innervated only by the sympathetic
nervous system, as compared to other systems, such as the cardiovascular and the
respiratory systems, which have both sympathetic and parasympathetic innervation.
Furthermore, the electrodermal system is the only one in the human body with this
feature of exclusive sympathetic innervation, which is why EDA is considered a
pure marker of sympathetic activation (i.e., sympathetic arousal; Boucsein, 2012).
Arousal refers to the degree of physiological activation and responsiveness
triggered by an event, object, or situation during a person’s interaction with the
24
environment (De Lecea, Carter, & Adamantidis, 2012; Juvina, Larue, & Hough,
2018). The psychophysiological value of arousal lies in the indirect access it
provides to cognitive and affective processes (Mandler, 1984). Arousal increases
with the cognitive process of attention (Raskin, 1973; Sharot & Phelps, 2004),
which has been explained through evidence pointing to arousal and attention as
sharing the same neural mechanisms (Critchley, 2002). Arousal is also the
activation dimension of emotions according to the circumplex model of affect (see
Fig. 2 in Sub-chapter 2.3.1), which conceives emotions as states characterized by
arousal and valence (i.e., the degree of [un]pleasantness) components (Russell,
1980).
The opportunities are obvious, but wearable biosensors have not yet become
popular in learning research (Blikstein & Worsley, 2016). Reasons that might
prevent the use of physiological data can be related, first, to the technological
equipment needed to collect them (e.g., the equipment can be expensive, or
sometimes intrusive), and second, to the substantial domain-specific knowledge
base needed to analyze and interpret them (Palumbo et al., 2017).
This dissertation utilizes the Empatica® E3 and E4 multi-sensor wristbands
(Empatica Inc., Cambridge, MA, USA), devices specifically designed for research
as opposed to consumer-oriented devices in the market with lower accuracy
(Garbarino, Lai, Tognetti, Picard, & Bender, 2014), to measure EDA and calculate
a number of derived indices of sympathetic arousal and interpersonal physiology,
in collaborative learning situations in classroom and classroom-like environments.
The coordination and synchronicity characteristics of collaborative learning
processes (Roschelle & Teasley, 1995) are also hypothesized to be reflected in
mutual influence and shared physiological states among collaborators (Palumbo et
al., 2017). Accordingly, there is a need to investigate the characteristics of
physiological group responses, such as homogenous activation levels and
simultaneous or sequential physiological changes (Knierim, Rissler, Dorner,
Maedche, & Weinhardt, 2018). Moreover, physiological metrics of collaboration
can be considered universal, as they are objective indices (Chanel, Kivikangas, &
Ravaja, 2012; Henning, Armstead, & Ferris, 2009; Montague, Xu, & Chiou, 2014;
Picard, Fedor, & Ayzenberg, 2016; Swan, 2012) that are independent of language
and culture.
Through three scientific articles, this dissertation explores EDA and derivative
measures, such as sympathetic arousal and physiological coupling indices (PCIs),
concomitant with individual and interpersonal cognitive and affective processes,
respectively, during collaborative learning in a naturalistic setting. Article I studies
25
PCIs (i.e., group measures attending to different parameters) based on EDA, in
relation to subjective and performance-related measures of collaboration. Article II
provides a profile of the degree of physiological activation (i.e., arousal) in the
classroom, together with the relationship of arousal to achievement on the exam.
Finally, Article III explores common arousal levels and possible arousal contagion
in classroom groups.
The structure of this dissertation follows. After this introduction, Chapter 2
lays out the theoretical background for the work, which involves collaborative
learning research, EDA, arousal, and interpersonal physiology. The theory chapter
ends with a sub-chapter reflecting on the research gap from different fields, and on
the combination of need and opportunity motivating this work. Chapter 3 concisely
presents the aims of the dissertation, which shape the methodology detailed in
Chapter 4. The latter concludes with two sub-chapters addressing the research
evaluation and ethical considerations. Chapter 5 provides an overview of the three
articles on which this dissertation is based, connecting how each article contributes
to the overall aims. Also aligned with the aims, the main findings are presented and
discussed in Chapter 6. Finally, concluding remarks including significance of the
work, implications, applications, and future work, are presented in Chapter 7.
27
2 Theoretical framework With the integration of different theoretical frameworks, namely, the theories
behind collaborative learning, arousal theory from general psychology, and EDA
from the field of physiology, in addition to computer science approaches to
physiological data processing, this work shows the commitment and will to keep
advancing the interdisciplinarity that has characterized the learning sciences since
their early days (Hoadley, 2018), which is a staple of collaborative learning
research as well (Stahl et al., 2014).
2.1 Collaborative learning
Three leading theoretical frameworks for collaborative learning have been the
socio-constructivist approach, which builds on the work of Jean Piaget and his
followers; the socio-cultural approach, rooted in the work of Vygotsky; and the
shared or situated cognition theory (see Dillenbourg et al., 1996). The socio-
constructivist approach considers that learning is the result of disagreement
between two points of view in social interaction, when such a socio-cognitive
conflict leads to the coordination of points of view, resulting in an enhanced
understanding at the individual level (Doise & Mugny, 1984). In the socio-cultural
approach, communication rather than disagreement is seen as the mechanism of
learning, with inter-psychological processes happening in the interaction and then
being assimilated at the intra-psychological level (Wertsch, 1991). According to the
socio-cultural tradition, communication goes iteratively from a dialogue with peers
to an internal dialogue with oneself, the reflection part, when the new knowledge
is then internalized (Vygotsky, 1978). The two theories acknowledge the role of
both the group and the individual in the process of learning and the bidirectional
influence between both roles (Butterworth, 1982).
Collaboration is considered to be conducive to higher order thinking skills
through effective communication (Cohen, 1994). Verbal interactions activate
several cognitive processes, including perception, comprehension, information
processing, representation, and anticipation, among others (Ahn et al., 2018).
According to Bruer (1994), dialogue might lead to deeper cognition because it
involves both the comprehension and production of language, the latter more
cognitively demanding than the former. Bruer sees dialogue and discussion as
vehicles of learning, which not only lead to higher order processing, but also allow
students to share and distribute the cognitive load so that no individual working
28
memory is required to hold all the information needed to solve a problem (F.
Kirschner, Paas, & Kirschner, 2011). Apart from alleviating cognitive load, Bruer
reasons that collective working memory renders encoded information more
accessible, since the same knowledge may have a different representation in each
participant’s long-term memory, and a wider repertoire of cues are then available
to the group to trigger information retrieval from individual memories. In other
words, collective working memory reduces cognitive load, facilitates positive
interdependence, and increases information availability through redundancy (van
Bruggen, Kirschner, & Jochems, 2002).
Collaborative processes thought to be beneficial for learning include, but are
not limited to, knowledge co-construction (Jeong & Hmelo-Silver, 2016),
transactivity or building on each other’s reasoning (Weinberger, Stegmann, &
Fischer, 2007), and argumentation (M. Baker, 2015). Other support processes are,
for example, the identification of shared knowledge (Roschelle & Teasley, 1995),
the establishment of common ground (Reiter-Palmon, Sinha, Gevers, Odobez, &
Volpe, 2017), mutual modelling of the collaborative partner’s knowledge state
(Dillenbourg, 1999), and coordinating joint efforts (Järvenoja, Järvelä, &
Malmberg, 2015). All the previous processes implicitly highlight the notion of
synchronicity and simultaneity presumed in collaborative learning, as opposed to
cooperative learning, which is characterized by individually solving sub-tasks
through division of labor (Dillenbourg, 1999). Joint and/or together are the
keywords that prominent researchers in the field consistently use to define
collaborative learning or associated processes. Such descriptions include (emphasis
added in all cases) “working together” (Cohen, 1994), “mutual engagement of
participants in a coordinated effort to solve the problem together” (Roschelle &
Teasley, 1995), the notion of “joint attention as social cognition” (Tomasello, 1995),
partners doing the work together (Dillenbourg, 1999), “a jointly produced activity”
(Enyedy & Stevens, 2014), “an active and joint process” (M. Baker, 2015), and
“working together toward a shared learning goal” (Jeong & Hmelo-Silver, 2016),
among others. Accordingly, shared cognition is regarded as a critical team state
(Reiter-Palmon et al., 2017).
As students learn collaboratively, they often create a joint product, artifact, or
solution to the task at hand, and upon a justified and successful process, individual
domain knowledge is expected to be enhanced (i.e., learning gains; Rummel, 2018).
In this dissertation, the joint product and the pairwise learning gain (i.e.,
aggregation of the individual learning gains) are referred to as the collaborative
learning product and dual learning gain, respectively.
29
Collaboration can only occur if the group members strive for building and
maintaining a shared understanding of the task and its solutions (Barron, 2000). In
other words, the students’ attitudes toward collaboration, that is, their motivation
to constructively engage in collaborative work, become important factors in
predicting the process success (Cohen, 1994). In this dissertation, the term
collaborative will is used to designate the students’ attitudes toward collaboration.
2.2 Electrodermal activity
EDA refers to “electrical changes across the skin in areas of the body that are
psychologically responsive” (Roy, Boucsein, Fowles, & Gruzelier, 1993, p. v).
With a history of more than a century, a number of features have contributed to
make EDA one of the most widely used response systems in the trajectory of
psychophysiology (M. E. Dawson et al., 2017; Kreibig, 2010), the discipline
concerned with the measurement of physiological changes in order to increase
understanding of psychologically relevant phenomena (Cacioppo & Tassinary,
1990b; Lyytinen, 1984). A listing of such features follows:
1. EDA is easy to measure with a properly spaced pair of electrodes (Boucsein,
2012). So much so, that in its early decades, it was common for workers in this
field to use equipment built in their own laboratories, until a commercially
manufactured apparatus became more available (Venables & Christie, 1973).
2. The techniques used to record EDA are completely harmless and risk-free (M.
E. Dawson et al., 2017).
3. Electrodermal changes can be easily detected by eye (G. E. Schwartz &
Shapiro, 1973). Today, advanced sophisticated algorithms based on the
mathematical operation of deconvolution (Benedek & Kaernbach, 2010b)
allow for the offline biosignal processing of continuous EDA recordings, but
before the invention of the computer, the simple quantification of responses in
stimulus-response experimental paradigms proved convenient (G. E. Schwartz
& Shapiro, 1973).
4. EDA is considered a remarkably sensitive measure because it changes with low
levels of psychological and affective states and may vary with the intensity of
the state (Thorson, West, & Mendes, 2018).
5. EDA is relatively inexpensive in terms of the hardware and software required,
especially when compared to eye tracking, brain-computer interfaces and facial
30
expression recognition (Gonzalez-Sanchez, Baydogan, Chavez-Echeagaray,
Atkinson, & Burleson, 2017).
6. The exclusive innervation of the sweat glands by the sympathetic nervous
system provides a relatively direct and undiluted representation of sympathetic
activity (M. E. Dawson et al., 2017). The sympathetic nervous system is the
control system for the so-called fight-or-flight response, which reflects a state
of activation, thus, the use of EDA to index arousal. Conversely, the
parasympathetic nervous system is responsible for bringing the body back to
homeostasis, the state of equilibrium corresponding to the so-called rest-and-
digest response, thus counteracting arousal. In other words, EDA is a clean
measure of sympathetic arousal without parasympathetic interference.
These features of EDA, together with the vast amount of research that EDA has
been the object of, have led to its long-term consideration as a well-validated,
widely accepted, and perhaps the most readily accessible and simplest indicator of
sympathetic arousal (Boucsein, 2012; Critchley, 2002; Wang, 1957). Altogether,
EDA has been regarded, not surprisingly, as a valuable addition to the toolkit of
scientists, especially for the those tackling psychophysiological problems (G. E.
Schwartz & Shapiro, 1973).
EDA history dates back to the 19th century. By 1840, it was generally accepted
that the body’s electrical characteristics provided a basis for diagnosis and therapy,
as part of the view of vital processes as electrical (E. Neumann & Blanton, 1970).
Vigouroux (1879), a French electrotherapist and electro-diagnostician, was the first
to describe the changes in the electrical resistance of the skin. Féré (1888), usually
credited with the discovery of the electrodermal reflex, demonstrated that rapid
fluctuations of skin resistance could occur in response to emotional stimulation
(Edelberg, 1972b). The report by Féré appears to be the first attempt to use EDA to
index a psychological construct (Raskin, 1973). Since then, the amount of work on
EDA has been extensively reviewed to summarize and update the understanding of
the electrodermal mechanisms, as such, and their applications to
psychophysiological research (Boucsein, 1992, 2012; Critchley, 2002; M. E.
Dawson, Schell, & Filion, 1990, 2007; M. E. Dawson et al., 2017; Edelberg, 1967;
E. Neumann & Blanton, 1970; Venables & Christie, 1980; Venables & Martin,
1967). There have been periods of development and periods of stagnation, the latter
owing, at least in part, to the use of inadequate or poorly understood techniques
(Venables & Christie, 1973).
31
It is important to be aware of different terminologies that have been used to
refer to the EDA phenomenon. These terminologies not only can be found in
today’s scientific literature, but also in classic and still highly relevant work on the
topic. Although the formal history of EDA dates back to 1879 as previously
indicated, according to Boucsein (2012, p. 2), the term EDA was first introduced
by Johnson and Lubin in 1966 “as a common term for all electrical phenomena in
skin, including all active and passive electrical properties which can be traced back
to the skin and its appendages.” Other well used terms, at the time, were
psychogalvanic response (PGR) and galvanic skin response or reflex (GSR). PGR
was coined by Veraguth in 1908 when, oblivious to the three previous decades of
work on EDA, he believed that he had discovered a new reflex (E. Neumann &
Blanton, 1970). As for the GSR, its utilization has been long advised against due to
ambiguity in its early use on the one hand, and to the findings that multiplicity and
complexity of the EDA phenomena (e.g., spontaneous responses and
psychologically elicited changes) cannot be explained simply as a galvanic element,
on the other hand (Boucsein, 2012; Venables & Christie, 1973). Accordingly, EDA
is now the preferred term over GSR and PGR, for variations in electrical
conductance of the skin, including phasic changes that result from sympathetic
neuronal activity (Critchley, 2002; M. E. Dawson et al., 2017). EDA is therefore
the term used in this dissertation. Notwithstanding, it has to be mentioned that some
authors, though acknowledging that GSR is now less often used, consider it “still
accepted” (M. S. Schwartz, Collura, Kamiya, & Schwartz, 2017).
The physiological basis of EDA has been ascribed to muscular, vascular, and
secretory mechanisms, causing a debate which mounting evidence settled several
decades ago in favor of the secretory theory (Venables & Christie, 1973). Today, it
is known that the eccrine sweat glands, uniquely innervated by the sympathetic
nervous system, are the primary determinants of EDA (Boucsein, 2012; Critchley,
2002). The eccrine sweat glands are coiled, tubular structures that extend from the
epidermis to the lower dermis and secrete water, electrolytes, and mucin (i.e., sweat,
basically; Critchley, 2002). Eccrine and apocrine are the two forms of sweat glands
in the human body, but the former are of primary interest in psychophysiology
because, although their primary function is thermoregulation, all eccrine glands are
believed to be involved in psychological sweating (M. E. Dawson et al., 2017). The
distribution of eccrine glands, however, is not homogeneous over the entire skin.
Their highest density (approximately 400/mm2) occurs in palmar and plantar (or
sole) regions (Critchley, 2002). This means, on the one hand, that EDA measures
in different body parts are not comparable, and on the other hand, that the palm and
32
the sole are the best areas to record EDA. However, in ecologically valid studies,
the placing of electrodes on such regions interferes with the subjects’ activities and
is therefore not practical. Consequently, alternatives have been explored and more
practical locations for daily life measurements such as the wrist have been found
viable (Poh, Swenson, & Picard, 2010; van Dooren, de Vries, & Janssen, 2012).
This dissertation employs non-dominant wrist measurements of EDA via an
unobtrusive watch-like research-quality off-the-shelf wristband (see Garbarino et
al., 2014).
The EDA signal is the combination of a tonic, slow-varying component and a
phasic, fast-changing component superimposed on the tonic level (Boucsein, 2012).
The tonic component accounts for the level of skin conductance in the absence of
any particular discrete environmental event or external stimuli, and it is
denominated as the skin conductance level (SCL), after a suggestion by Venables
and Martin (1967). The phasic component, of wider use in this dissertation,
resembles peaks, any of which are referred to as skin conductance response (SCR),
a term coined by Burch and Greiner in 1958 according to Edelberg (1967, p. 44)
and endorsed by the Society for Psychophysiological Research in 1967 (Venables
& Christie, 1973). SCRs rise steeply to their peak and decline slowly, following an
exponential-like decay, to the baseline (Benedek & Kaernbach, 2010b). The
succession of SCRs usually results in a superposition of subsequent SCRs, as one
SCR arises on top of the declining trail of the preceding one (Boucsein, 2012).
Today, advanced deconvolution-based algorithms are available to decompose
superimposed SCRs in a way that enhances their quantification as compared to
previous historical approaches (Benedek & Kaernbach, 2010b).
It was not an interest in the phenomenon itself which led to the high volume
and rapid development of research on EDA, but the possibility of applications to
medical diagnosis and to studies of mental processes of interest in a wide variety
of fields (E. Neumann & Blanton, 1970). For example, Poh et al. (2010, pp. 1243–
1244) cite potential clinical applications of EDA which have been researched, such
as screening for cystic fibrosis, classification of depressive illnesses, prediction of
functional outcome in schizophrenia, discrimination between healthy and psychotic
patients, characterization of sympathetic arousal in autism, early diagnosis of
diabetic neuropathy, and providing biofeedback in treating epileptic seizures.
Edelberg (1967) cites clinical explorations for dermatological research and
neurological diagnosis. Although the clinical applications are many, those outside
medical settings (i.e., of interest in psychophysiological research) are not fewer.
EDA has been largely explored in relation to cognitive and affective processes,
33
including, but not limited to, attention (Critchley, 2002; Thorson et al., 2018),
cognitive load measurement (Boucsein & Backs, 2000; Shi, Ruiz, Taib, Choi, &
Chen, 2007), memory retention (Critchley, Eccles, & Garfinkel, 2013), stress
detection (Visnovcova, Mestanik, Gala, Mestanikova, & Tonhajzerova, 2016),
engagement (Gillies et al., 2016), decision-making and judgment (Finger &
Murphy, 2011), self-efficacy (McQuiggan, Mott, & Lester, 2008), goal orientation
(Edelberg, 1972a), the state of flow (Knierim et al., 2018), and the study of the
social psychology of human trust (G. E. Schwartz & Shapiro, 1973). Some of those
processes in the cognitive and affective domains have led to incorporating EDA
into intelligent tutoring systems (Cooper et al., 2009; Strauss et al., 2005) and
intelligent interruption management or interruption notification mechanisms
(Goyal & Fussell, 2017; Knierim et al., 2018).
Yet, as diverse as the interpretations might seem in the cognitive and affective
domains, they all share a common denominator: arousal. Whether explicit or
implicit, the notion of arousal, in particular sympathetic arousal, is present
whenever EDA is being used for psychophysiological purposes, since EDA and
arousal are inextricably linked. A reminder of the underlying rationale follows. The
skin, an organ whose activity is electrically traceable (i.e., EDA), is the only organ
of the entire human body exclusively innervated by the sympathetic nervous system,
which is the controller of the so-called “fight-or-flight” response (i.e., preparing the
body for action, whether physical or intellectual). Consequently, EDA enables
access to sympathetic activity. In other words, in psychophysiological research,
above all, EDA is an index of sympathetic arousal (Boucsein, 2012; Critchley, 2002;
Wang, 1957).
2.3 Arousal
The interest in the study of arousal arises from its role in cognitive and affective
processes (see Sub-chapter 2.3.1) to the extent that it has an impact on performance
(see Sub-chapter 2.3.2). The construct of arousal is at the center of the connection
of body and mind (i.e., cognitive-affective function; Hanoch & Vitouch, 2004;
Mandler, 1984). The notion of arousal was developed hand in hand with the early
work on EDA. According to Neumann and Blanton (1970), Féré (1888) initially
studied the EDA phenomenon as an arousal indicator. He demonstrated that rapid
fluctuations of skin electrical resistance could occur in response to emotional
stimulation (Edelberg, 1967). Thereafter came “a long period of neglect” (Raskin,
1973, p. 126), until further work developed the arousal theory (e.g., Duffy, 1962;
34
Hebb, 1955). Although arousal is a term of widespread use in psychology, other
terms have been used as synonyms in the scientific literature, including activation,
energy mobilization, excitation, energy, tension, and activity (Duffy, 1962; Russell
& Barrett, 1999).
While arousal measures the degree of physiological activation, and it would
therefore be recorded more objectively and precisely through physiological
responses, it has to be noted that different scales have been developed to measure
it (Bettiga et al., 2017). Such scales might be convenient when biosensors are not
available, but research has shown that there is often a mismatch between the two
measures (i.e., self-reported scales vs. biosensors). Accordingly, Reisenzein (1983)
proposes making a distinction between physiological arousal per se and perceived
arousal. Arousal in this dissertation is measured physiologically, as indexed
through EDA.
Physiological arousal, in turn, is a complex response which manifests through
a variety of central (i.e., electrocortical), motor (i.e., muscle tension), autonomic
(e.g., sweating, heart rate, and blood pressure), and endocrine (i.e., hormonal)
responses (American Psychiatric Association, 2000). For example, an increase in
muscle tension usually goes hand in hand with an increased SCL (Frijda, 1986).
However, it should not be assumed that the arousal response in each of the different
participating systems is necessarily equivalent, or even related. No correlation has
been found between electrocortical and autonomic arousal (Frijda, 1986), and the
heart has been reported to slow down even as other indices (e.g., pupil size) suggest
an increase of arousal (Kahneman, 1973). In general, the different indices of
physiological arousal, whether electrocortical, biochemical, electrodermal,
cardiovascular, or skeletal motor, have been shown to correlate poorly, and the term
directional fractionation has been chosen to refer to the different patterning (Lacey,
1967). Basically, directional fractionation means that different systems involved in
the arousal response show a different pattern, which might even be in different
directions (i.e., one response increasing while the other is decreasing). Accordingly,
separate types of arousal have been proposed (e.g., sympathetic and electrocortical),
which should not be mixed (Frijda, 1986). Sympathetic arousal as indexed by EDA
is the type that is the focus of this work.
2.3.1 Cognition, affect, and arousal
Arousal has a bidirectional relationship with cognitive and affective processes
(Mandler, 1982). Cognitive and affective drivers have a reflection in arousal levels
35
(Critchley, 2002; Poh et al., 2010), but, in turn, changes in arousal inform cognition
(Critchley et al., 2013; Critchley & Garfinkel, 2018) and affect (Reisenzein, 1983;
Schachter & Singer, 1962). It is known, for example, that people rely partly on
information from their arousal states in judging their capabilities (Bandura, 1982),
and that arousal has an effect on human behavior (Damasio, Tranel, & Damasio,
1991). Thus, the psychology and physiology of arousal have a reciprocal influence
on one another. Parenthetically, arousal is also influenced by physical activity and
by the intake of stimulant and depressant substances (e.g., caffeine; Hanoch &
Vitouch, 2004; Russell & Barrett, 1999), but, obviously, the cognitive and affective
drivers are the main interest in psychophysiology, as well as to this work where the
focus is on classrooms and classroom-like spaces as students learn science.
For the cognitive part, there is a close interdependence between arousal and
attention (Kahneman, 1973; Raskin, 1973; Robbins, 1997; Sharot & Phelps, 2004),
which naturally extends to other cognitive processes that presuppose attention, such
as memory and decision-making (Garfinkel, Critchley, & Pollatos, 2017). Arousal
assists in the mental sharpening that enhances attention to and the processing of
potentially important information (Critchley et al., 2013). Empirically, arousal has
been shown to increase with attention in relation to eye-catching, engaging stimuli,
and attention-demanding tasks (Poh et al., 2010). Such a relationship might be
explained by indications from clinical studies that a common neural substrate may
be shared by arousal and attention mechanisms (Critchley, 2002). By modulating
the selectivity of attention and then increasing attentional time on arousing stimuli,
arousal can delay disengagement and influence memory encoding (Fox, Russo,
Bowles, & Dutton, 2001). In addition, arousal at the time of memory encoding is
theorized to facilitate memory retrieval, more so for individuals with enhanced
capabilities to be aware of their arousal and internal bodily states in general, known
as interoceptive awareness (Garfinkel et al., 2017). Emotionally arousing incidents
and stimuli have been shown to be much better remembered than those without
emotional relevance (Marchewka et al., 2016). The representation of bodily
changes in arousal may mediate the feeling of knowing ascribed to some familiar
memories (Garfinkel et al., 2017). In terms of decision-making, interoceptive
signals are said to shape rapid decision-making in stressful contexts (Critchley et
al., 2013; Critchley & Garfinkel, 2018). The role of arousal in emotions, discussed
next, has been at the center of its consideration as a well-suited tool in decision-
making and judgment research (Finger & Murphy, 2011).
Arousal and valence are the two dimensions of emotions according to the
classic circumplex model of affect (Russell, 1980). The model is represented
36
graphically in rectangular coordinate axes as shown in Fig. 2. The arousal axis
(vertical) represents a continuum from deactivation to activation (bottom-up),
whereas the valence axis (horizontal) represents a continuum from
negative/unpleasant to positive/pleasant (left-right). The notion of arousal as an
essential component of emotions, however, is much older than the model. The
construct of arousal was born together with EDA research through the realization
that fluctuations of EDA could occur in response to emotional stimulation
(Edelberg, 1967; Féré, 1888; E. Neumann & Blanton, 1970). Later, when the theory
of arousal or activation (Duffy, 1962) was further developed, the “general level of
energy mobilization” was included as “a major aspect of what has been historically
labeled as emotion” (Raskin, 1973, p. 126). Around the same time, Schachter and
Singer (1962) formulated their classical cognition-arousal theory of emotion, also
known as cognitive-physiological theory of emotion, two-factor theory of emotion,
or simply the Schachter theory of emotional states (Reisenzein, 1983). According
to the theory, an emotional state is the result of the cognitive appraisal (i.e., labelling)
of the state of arousal caused by the emotional stimulus. In addition, arousal
feedback can have an intensifying effect on emotional states, and this arousal-
emotion relationship is mediated, in part, by causal attributions regarding the
source of arousal (Reisenzein, 1983; Schachter & Singer, 1962).
Building on the circumplex model of affect and particularizing for academic
emotions, the quality of students’ information processing (i.e., cognition) and the
types of strategies unfolded by them have been mapped to the four quadrants of the
rectangular coordinate axes formed by arousal and valence (Pekrun, Goetz, Titz, &
Perry, 2002), as shown in Fig. 2. The quadrants have been labelled according to the
two arousal regions divided by the valence axis (“activating” for the top and
“deactivating” for the bottom), and the two valence regions divided by the arousal
axis (“positive” for the left and “negative” for the right). Thus, starting from the
top right and moving clockwise, the four quadrants are positive activating, positive
deactivating, negative deactivating, and negative activating. In the activating region,
positive activating emotions have been shown to elicit flexible, creative learning
strategies, such as elaboration, organization, and critical evaluation, whereas
negative activating emotions, on the other hand, seem to evoke more rigid strategies,
such as simple rehearsal and use of algorithmic procedures (Pekrun et al., 2002).
In the deactivating region, a shallower, superficial processing of information has
been found to predominate for deactivating emotions, whether positive or negative.
37
Fig. 2. Arousal-valence affective space and associated cognitive processing (from Article II, reproduced with permission from John Wiley and Sons).
Such a model of characterizing emotions with arousal and valence dimensions
might prove more useful than distinguishing specific emotions, since the latter
might be affected by insufficient discriminant validity owing to the overlapping
traits in different emotions (Russell & Barrett, 1999). Moreover, the arousal
dimension might be more relevant than the valence dimension when it comes to
learning. Activating states, whether positive (e.g., enjoyment of learning and pride
of success) or negative (e.g., anxiety, frustration, and confusion) seem preferable.
For example, it has been argued that negative activating states, which tend to
receive more attention than negative deactivating states (e.g., boredom), could be
productive for learning if they are properly regulated, and that they might not even
need remediation (R. Baker, D’Mello, Rodrigo, & Graesser, 2010). Conversely, in
the negative deactivating area, for example, boredom may impair learning by
leading to behavioral or mental avoidance strategies when students face tasks that
surpass their capabilities or are not engaging (Pekrun et al., 2002). The case of
boredom is of particular interest because it has been reported as more persistent
than other negative states, including both activating and deactivating ones (R.
Baker et al., 2010).
38
The cognitive processes and affective states closely related to arousal here
discussed, obviously, have an impact on performance too, leading to a transitive
relation between arousal and performance.
2.3.2 Arousal and performance
Known for over a century, the relationship between arousal and performance is
dictated by the so-called Yerkes-Dodson law (Yerkes & Dodson, 1908) and
described by an inverted-U function elaborated by Hebb (1955), as illustrated in
Fig. 3. When the arousal level is low, so is performance. As arousal rises due to
increased attention, interest, and engagement, performance increases up to a level
of optimal arousal for performance at the top of the inverted-U. If arousal keeps
rising to too high of a level because of excessive stress or strong anxiety,
performance is impaired. Such impairment has been explained through a reduction
of the processing capacity available for cognitive evaluation (Mandler, 1982;
Sanbonmatsu & Kardes, 1988). In the same vein, students with too high levels of
test anxiety perform worse on tests, and their overall academic achievement is
lower (Aritzeta et al., 2017). Test anxiety, located in the high arousal, negative
valence quadrant of the circumplex model of affect (see Fig. 2), can reduce working
memory resources and has been shown as the academic emotion most often
reported by the students (Pekrun et al., 2002).
The relationship between performance and arousal has been taken advantage
of, for example, to explore the development of interruption management systems.
Sometimes, external agents (e.g., a teacher) need to give a team either feedback or
additional information required to complete a task, while the task is being executed.
Such an interruption, although relevant, may result in being disruptive to the work.
However, a significant increase has been found in both task-performance and
reported collaboration experience when the information was presented during
arousal acceleration, compared with random interruption (Goyal & Fussell, 2017).
Studies of arousal in relation to performance and to the cognitive processes and
affective states behind performance have been carried out in a variety of settings
(Hanoch & Vitouch, 2004). Next, some related work is presented in the particular
setting of the classroom, including during exams.
39
Fig. 3. Yerkes-Dodson law (Hebb, 1955; Yerkes & Dodson, 1908).
2.3.3 Arousal in the classroom
Though studies of arousal during classroom instruction are rare, some exceptions
follow. Arroyo et al. (2009) used raw EDA from high school students taking their
regular mathematics class over a period of five days, to determine their affective
states in combination with self-reports, a camera for facial expression recognition,
a pressure mouse, and a posture analysis seat. More recently, Gillies et al. (2016)
reported a case study on the in-class arousal of sixth graders during a unit of their
science curriculum, in relation to their beliefs and engagement. They determined
arousal through EDA by averaging the signal within time bins. Calderón (2016)
tracked cardiovascular measures of arousal during two science education course
presentations from two student teachers. However, to the best of our knowledge,
arousal has not been measured throughout an entire course. Furthermore, studies
from some course sections which employed EDA to determine arousal have not
used the frequency of SCRs to operationalize the level of arousal.
When it comes to formal learning situations, exams have received much more
attention than everyday classroom lessons, given the special interest on the relation
between arousal and performance. Exams are challenging situations often
involving stress and anxiety because they have not only psychological implications
40
for the students but also practical consequences for their progress in academic life,
as well as their future. Real exams may produce different responses than
challenging laboratory situations because individuals evaluate exams more
seriously and are under more pressure to do well (Spangler, 1997). Learning
scientists have long been interested in studying students’ responses to exams
(particularly test anxiety) and the implication they have for their performance
(Pekrun et al., 2002). The physiological responses in exam situations have been
studied from the cardiovascular system, the endocrine system, and the immune
system perspectives (Spangler, 1997). However, no exam studies have included
pure measures of sympathetic activity (Spangler, 1997), as would be the case in
EDA measures. Moreover, wristband EDA measures are less invasive and
unobtrusive than the endocrine and immune system measurements involving saliva.
Furthermore, the analysis of saliva to assess those physiological responses requires
the use of medical laboratory equipment, while, for the wristband EDA measures,
once the recording is done, the analysis is carried out using computer software.
As discussed in Chapter 1 and Sub-chapter 2.1, collaborative learning
pedagogies are increasingly encouraged and used in the classroom for academic
(e.g., stimulating higher order cognitive processes) and practical (e.g., sharing of
resources) reasons, among others. It was also discussed that collaboration is not
always effective, which has driven research to explore a variety of measures in an
attempt to quantify and qualify collaborative processes. Arousal refers to an
individual degree of physiological activation and has been a central construct in
psychophysiology, the inference of psychological states from physiological
responses, at the individual level. But group processes obviously require group
approaches. The socio-cultural theory of learning postulates that inter-
psychological processes at the group level are then assimilated by the individual at
the intra-psychological level (Dillenbourg et al., 1996). Although the field of
psychophysiology targets the individual, inter-psychological processes already
provided a motivation several decades ago to also explore related inter-
physiological processes, giving birth to the sister discipline of social
psychophysiology or interpersonal physiology.
2.4 Interpersonal physiology
Once physiological technology developed to a point where empirical studies were
easy to carry out, physiological approaches to the analysis of small-group
interaction naturally became of particular interest to social psychologists and
41
psychophysiologists (G. E. Schwartz & Shapiro, 1973). From the marriage of social
psychology and psychophysiology, the field of interpersonal physiology (DiMascio,
Boyd, & Greenblatt, 1957) was born. Beginning in the 1950s, social scientists
started collecting physiological data from two or more people in interpersonal
interactions to explore commonalities and interdependence between their
physiological states (Thorson et al., 2018). Among the array of physiological
signals, EDA (and by extension sympathetic arousal) enjoyed privileged attention
as “a prime reflector of the social-biological nature of human behavior” (G. E.
Schwartz & Shapiro, 1973, p. 412). Ascribed connections of interpersonal
physiology to early development, language learning, group engagement, social
connection, and group membership might have been responsible for a continuous
interest in such an approach and a body of related literature which has kept growing
(Delaherche et al., 2012; Palumbo et al., 2017).
Interpersonal physiology refers to “the study of the reciprocal influence of
human physiological systems and social systems” (Kaplan & Bloom, 1960, p. 128).
In dyads, the stability and influence model (Thorson et al., 2018) considers how a
person’s physiology at a certain point in time is predicted by both his/her own
physiology at a prior time point (i.e., the stability effect) and by the other dyad
member’s physiology at the prior time point (i.e., the influence effect). This model
easily scales up to groups of any size. The influence is not explained by physiology
itself, as such, but is associated with the psychosocial processes which occur
between or among group members (e.g., one person’s heart rate influencing a
partner’s heart rate via a verbal outburst of anger; Thorson et al., 2018).
Whether driven by a shared environment, coordinated behaviors, matched
responses to a third variable, interpersonal processes, or other factors, the
mechanisms behind interpersonal physiology are still unclear (Field, 2012;
Palumbo et al., 2017). Evidence from the past decades seems to point, at least in
part, to the mirror system, composed of mirror neurons in several brain regions
(Rizzolatti & Fabbri-Destro, 2008). The first demonstration of a mirror system in
humans dates to 1995, when a study using magnetic stimulation found that “there
is a system matching action observation and execution” (Fadiga, Fogassi, Pavesi,
& Rizzolatti, 1995, p. 2608). Currently, the mirror system is discussed as the
evolutionary basis for observational learning (Field, 2012) and might play a role in
interpersonal physiology (Karvonen, Kykyri, Kaartinen, Penttonen, & Seikkula,
2016; Rizzolatti & Arbib, 1998).
The variety of methodologies and approaches from different disciplines are
reflected in the number of conceptualizations used in the study of interpersonal
42
physiology, including but not limited to attunement (Field, 2012; Karvonen et al.,
2016), compliance (Chanel et al., 2012; Elkins et al., 2009; Henning, Boucsein, &
Gil, 2001), concordance (Slovák, Tennent, Reeves, & Fitzpatrick, 2014), co-
regulation (Sbarra & Hazan, 2008), coupling (Chanel, Bétrancourt, Pun, Cereghetti,
& Molinari, 2013; Singer et al., 2016), covariation (Kaplan, Burch, Bloom, &
Edelberg, 1963), entrainment (Dikker et al., 2017), influence (Thorson et al., 2018),
linkage (Simo Järvelä, Kivikangas, Katsyri, & Ravaja, 2014), physiological
markers of togetherness (Noy, Levit-Binun, & Golland, 2015), and synchrony
(Dich, Reilly, & Schneider, 2018; Gillies et al., 2016; Liu, Zhou, Palumbo, & Wang,
2016). Occasionally, these terms imply conceptual differences in what is being
measured or are used to refer to specific analytic techniques or theoretical
approaches, but a systematic review of the literature shows that this is inconsistent
(Palumbo et al., 2017).
Different types of interpersonal relationships have been the focus of studies on
interpersonal physiology, such as parent-child, therapist-client, individuals in a
couple, artist-audience, conductor-choir, friends, teammates, groups of strangers,
and so forth (Palumbo et al., 2017). Physiological interdependence has been found
to be greater, for example, between individuals who are in a romantic relationship
(Sbarra & Hazan, 2008), in psychotherapists who are more empathetic to their
clients (Delaherche et al., 2012), and in game players who report greater rapport
with each other (Noy et al., 2015). However, when it comes to students in a
collaborative learning setting, the use of physiological data has received little
attention, as evidenced by only two papers found in a recent systematic review of
the literature including all publication dates through November 2015 (Palumbo et
al., 2017).
2.4.1 Previous work on interpersonal physiology in collaborative learning
Early examples are found in the work of Kaplan and associates in the 1960s, which
explored sympathetic arousal in relation to the affective orientation of group
members in either dyads or four-person groups of medical and nursing students
(Kaplan, 1967; Kaplan, Burch, Bedner, & Trenda, 1965; Kaplan et al., 1963). Four
decades later, sympathetic arousal commonalities were investigated in dyads of
college students in terms of nonlinear dynamics and linkage effects (Guastello,
Pincus, & Gunderson, 2006). Physiological commonalities in emotion management
and convergence during collaborative processes have been studied at the dyad level
43
using a variety of physiological responses and measures, such as
electrocardiography, respiration, EDA, temperature, and eye-tracking (Chanel et al.,
2013). More recently, physiological commonalities in relation to cognitive and
affective states (e.g., mental workload and emotional valence) during a pair-
programming task in a classroom environment have been studied using
electrocardiography and EDA (Ahonen et al., 2016; Ahonen, Cowley, Hellas, &
Puolamäki, 2018). All these previous studies focused on physiological responses
from the autonomic nervous system, which can be measured more cheaply, quickly,
and unobtrusively than those of the central nervous system (see Fig. 1 in Chapter
1) and are more easily interpreted (Novak, Mihelj, & Munih, 2012). Nonetheless,
using portable technology in a high school classroom during 11 lessons,
commonalities in electroencephalogram signals have been suggested to predict
group engagement and social dynamics (Dikker et al., 2017). None of the studies
focused on triads as the unit of analysis.
The dyadic nature of some of the relationships studied (e.g., therapist-client
and couples), together with the greater availability of pairwise physiological
indices (discussed in Sub-chapter 2.4.3), might be among the reasons why
interpersonal physiology has predominantly focused on dyads as units of analysis
(Delaherche et al., 2012). However, the general dynamics of triads and larger
groups are different than those of dyads. For example, dyads cannot form coalitions
and have newcomers or old-timers, nor can there be majority/minority influence in
dyads. Also, negotiation and conflict (i.e., argument) are more complicated in triads
and larger groups (Reiter-Palmon et al., 2017). When it comes to learning, dyads
are often considered to be peer learning, with real collaborative learning beginning
with three or more participants. Therefore, the focus of this work is on triads.
2.4.2 Arousal contagion
It is generally recognized in social psychology that the emotional responses of one
person may elicit emotional responses from another (Berger, 1962; Reeck, Ames,
& Ochsner, 2016). The effect, which has been conceptualized as emotional
contagion, is driven by feedback from facial and vocal expressions, postures, and
behaviors (Hatfield, Cacioppo, & Rapson, 1994). Emotional contagion can also
occur when changes in one’s visceral state during emotion may be mirrored in an
observer, inducing a corresponding representation (Critchley et al., 2013). The
emotion “caught” by the influenced person can be either similar or complementary,
the latter sometimes called counter-contagion (Hatfield et al., 1994). Based on
44
whether the emotional contagion leads to a similar or complementary valence (i.e.,
pleasant or unpleasant), for example, Berger (1962) provided “a simple, yet
convenient definitional framework” for the social emotions of empathy, envy, and
sadism (G. E. Schwartz & Shapiro, 1973, p. 391). According to the framework,
empathy is described as the social emotion where the valence is similar (whether
pleasant or unpleasant) in influencer and influenced, while in envy, a pleasant state
in the influencer causes an unpleasant state in the influenced person, and the other
way around in sadism, which elicits a pleasant affect in the influenced based on an
unpleasant state of the influencer. Rudimentary or primitive emotional contagion is
described as relatively automatic, unintentional, uncontrollable, and largely
inaccessible to awareness (Hatfield et al., 1994).
A particular case of interpersonal physiology explored in this dissertation is
that of arousal contagion, inspired by the notion of emotional contagion, given that
arousal is the activation dimension of emotion according to the circumplex model
of affect (Russell, 1980). Arousal contagion helps studying the same phenomena in
groups (e.g., to assess collaboration or leadership), without the need to distinguish
specific emotions, which could result in insufficient discriminant validity, as there
are overlapping traits in different emotions (Pekrun et al., 2002). The motivating
affordance is that, as a degree of physiological activation, arousal is objectively
measurable and theoretically relevant for the introspection of cognitive and
affective states.
2.4.3 Physiological coupling indices
The theoretical relevance of interpersonal physiology for the study of group
processes, including collaborative learning, faces the innate challenge of defining
and delineating variables of theoretical significance and practical relevance (G. E.
Schwartz & Shapiro, 1973), given the variety of physiological signals, the number
of features extractable from each at the individual level, and the panoply of
aggregation approaches for group level analyses (Delaherche et al., 2012; Kreibig,
2010; Palumbo et al., 2017). The individual features of physiological signals can
be descriptive statistics (e.g., mean, median, variance, standard deviation, different
percentiles, and interquartile range), which can be applied to all signals, or signal-
specific, which depend on the particular signal at hand. For example, studied
features from EDA include, but are not limited to, SCL, SCR amplitude, SCR
latency, SCR rise time, SCR half-recovery time, SCR peaks per minute, and SCR
area under the curve (Boucsein, 2012; M. E. Dawson et al., 2017). As an example
45
of other bodily systems, tens of signal-specific features have been extracted from
respiratory and cardiovascular and signals (Kreibig, 2010). In an attempt to
estimate cognitive load, Ferreira and associates (2014) employed up to 128 features
from four signals. These examples illustrate the variety of options available at the
individual level.
The aggregation of individual features at the group level, or the direct
computation of group features from particular parameters (e.g., difference in
amplitude, rate of change, direction, and linear relationship) of the individual
signals, translates into a variety of indices which are called PCIs in this dissertation.
Basically, PCIs are indices of interpersonal physiology.
Building on the indices proposed by Elkins et al. (2009), this dissertation
explores as PCIs in relation to some collaborative learning aspects (see Article I),
the indices of signal matching, instantaneous derivative matching, directional
agreement, Pearson’s correlation coefficient, and a transformation of it. Except
from directional agreement, those indices can only be computed on a pairwise basis.
Signal matching measures the difference in the amplitude of two signals.
Instantaneous derivative matching accounts for the difference in the rate of change,
as determined by the first derivative of two signals. Directional agreement
measures the percent of time that an arbitrary number of signals is changing in the
same direction (i.e., upwards, constant, or downwards), making it a simple yet
convenient index for the study of the interpersonal physiology for more than two
persons.
Signal matching, instantaneous derivative matching, and directional agreement,
applied to cardiovascular and EDA measures, have been explored in connection to
team performance, joint activity, and shared trust in technology (Elkins et al., 2009;
Montague et al., 2014). Much more often employed in interpersonal physiology has
been Pearson’s correlation, a standard and popular statistical tool, which in this case
determines the strength of the linear relationship between the physiological signals
from two individuals. Although no conclusive results have been obtained, cross
correlation has been found to predict task completion time (Henning et al., 2001)
to be higher in a conflicting situation than when playing cooperatively in digital
gaming (Chanel et al., 2012), to indicate team performance after controlling for
task/technology conditions (Montague et al., 2014), and to relate to self-reported
social presence in digital gaming (Simo Järvelä et al., 2014), among others. Even
half a century ago, correlations of the frequency of electrodermal SCRs over time
between two individuals were presumed to relate to rapport (G. E. Schwartz &
Shapiro, 1973). However, correlations, although simple, powerful, and easy to
46
interpret, might not be well-suited for continuously measured physiological signals
because data typically show sequential dependency (i.e., autocorrelations) and non-
stationarity over time (i.e., changing mean and variance), which violates general
linear model assumptions of data independence and stationarity (Palumbo et al.,
2017). In an attempt to overcome such limitations, some transformations, such as
the Fisher’s z-transform, and sophisticated statistical techniques, such as dynamical
correlation, have been proposed. The Fisher’s z-transform consists of applying the
inverse hyperbolic tangent to the correlation coefficient, in order to stabilize the
variance and obtain a normally distributed index (Chanel et al., 2013; Simo Järvelä
et al., 2014). Dynamic correlation is a nonparametric approach that places no
assumption on homogeneity of the sample, although it has limitations of its own
and complicates interpretation (Liu et al., 2016).
Although not applicable to the EDA signal, other PCIs found in the
interpersonal physiology literature are, for example, dynamic common activation
and weighted coherence. Dynamic common activation reflects both the strength
and variation of functional magnetic resonance imaging activation across subjects
(Singer et al., 2016). Weighted coherence (Porges et al., 1980) is a frequency
domain PCI which quantifies the similarities of two individuals’ physiological
responses on a specified frequency band regardless of phase differences (Montague
et al., 2014). Weighted coherence is calculated over a range of frequencies rather
than at only a specific frequency, which makes it suitable to capture the inherent
variability in frequency of interpersonal physiological indices during active
behaviors (Henning et al., 2009). Naturally, being a frequency domain index, it is
mostly useful for periodic physiological signals, such as heart beat and respiration
rate (Chanel et al., 2013). EDA, the subject of this dissertation, is not a periodic
signal. Therefore, weighted coherence was not considered among the PCIs.
In sum, PCIs can play a role in studying social interactions (e.g., as in
collaborative learning and team processes) by providing an objective measure, but
research in this direction needs further explorations to keep enhancing our
understanding on the affordances of interpersonal physiological processes to index
interpersonal psychological processes related to cognition and affect and,
ultimately, to learning.
2.5 Research gap
Collaborative learning is extensively researched (M. Baker, 2015; Jeong & Hmelo-
Silver, 2016). Yet, the field is missing continuous measures with real-time potential,
47
as collaboration is a process whose evolution in time is of interest and a determinant
of success (Dillenbourg et al., 1996; Jeong et al., 2014). These measures have been
also referred to as online measures, meaning that they are taken as the process
unfolds, and not before or after. In addition, the field is on the lookout for measures
complementing subjective data as coming from self-reports (Wise & Schwarz,
2017).
EDA has a long history of research, over a century old (Boucsein, 2012).
However, it has not been until recently that it has become easily and unobtrusively
measurable in the wild, outside laboratory settings, thanks to the increasing
availability and technological quality of wearable biosensors (Garbarino et al.,
2014; Torniainen, Cowley, Henelius, Lukander, & Pakarinen, 2015). Yet, students
remain an understudied population, as clinical studies take the lion share of EDA
research (M. E. Dawson et al., 2017; Poh et al., 2010).
In psychology, arousal is a commonly studied construct due to its connection
to cognition and affect, and the natural psychological interest in these domains
(Kuppens, Tuerlinckx, Russell, & Barrett, 2013). Yet, we know little about arousal
in the classroom and its patterns. In the few studies tackling arousal in the formal
academic setting of the classroom (e.g., Arroyo et al., 2009; Gillies et al., 2016),
the affective component steals the show, while it is rare to find interpretations or
foci on the realm of cognitive processes such as attention. A little more studied have
been arousal or physiological measures in exams situations (Spangler, 1997;
Spangler, Pekrun, Kramer, & Hofmann, 2002). However, measures of arousal
based on the frequency of SCRs have not yet been tested during exams in
connection to students’ performance.
Interpersonal physiology has long been exploring the commonalities and
influence between subjects with certain relationships, predominantly parent-child,
therapist-client, and individuals in a couple (Palumbo et al., 2017). When it comes
to individuals collaborating, workplace performance is a more frequent target than
collaborative learning in schools (Elkins et al., 2009; Henning et al., 2001). In terms
of group size, dyads have received much of the attention, while other common
group sizes in collaborative learning such as triads have been neglected (Reiter-
Palmon et al., 2017; Thorson et al., 2018).
In this context, this dissertation has found a research gap to address, matching
multidisciplinarity, interest, opportunity, and relevance. This work contributes to
the particular case of collaborative learning by investigating PCIs, arousal levels,
and arousal contagion, as such, and in relation to outcomes as learning gain and
academic achievement.
49
3 Aims This dissertation pursues the exploration of EDA and derivative measures, such as
sympathetic arousal and PCIs, concomitant with individual and interpersonal
cognitive and affective processes, during collaborative learning in a naturalistic
setting. To that extent, the main aims of this dissertation are to:
1. investigate the interpersonal physiology of EDA in connection to collaborative
learning processes (addressed in Article I: research question [RQ] 1, RQ2;
Article III: RQ1, RQ2),
2. profile sympathetic arousal in the classroom at both the individual and
interpersonal levels in collaborative learning (addressed in Article II: RQ1,
RQ2, RQ3; Article III),
3. examine both the individual and interpersonal physiology of EDA in relation
to academic achievement (addressed in Article I: RQ2; Article II: RQ4), and
4. explore sympathetic arousal contagion among group members as a result of the
cognitive-affective social interactions in collaborative learning (addressed in
Article III: RQ3, RQ4).
In order to meet the aims, two data collections were organized, one in a classroom-
like research environment of the University of Oulu (see Article I) and another
during two consecutive runs of a regular course in a real classroom (see Articles II
and III).
51
4 Methods Two research designs were conceived for this dissertation, having ecological
validity in mind. To each design, a data collection process was associated. There
were similarities and differences between the designs. The participants in the
studies were Finnish high school students. A classroom-like environment and a real
classroom were used for collecting the data in naturalistic locations. The topics
students were dealing with during the data collections were part of their curriculum
and the tasks were either embedded in or regular part of their coursework. The
materials involved a biosensor wristband (Empatica®) to record EDA,
performance instruments (e.g., knowledge tests, course exams), online learning
environments, and questionnaires. Specialized computer software was used for the
analysis of the relevant variables according to the aims. In the light of the
reproducibility principle, this chapter presents the research methodology in detail,
an overview of which is listed in Table 1.
Table 1. Method overview.
Aspect Data collection I Data collection II
Participants N = 24; high school students N = 24; high school students
Location Classroom-like Classroom, classroom-like
Topic Nutrition Advanced Physics
Duration 2 h 15 min 2 x (75 min, 3 x week, 6 weeks)
EDA sensor Empatica® E3 Empatica® E4
Performance measures Pre- & post-test, joint
solution
Course exam
Online learning environment weSPOT Open edX
Questionnaire(s) Motivated Strategies for
Learning Questionnaire
(MSLQ)
MSLQ and a questionnaire adapted
from Schmitz and Skinner (1993)
EDA analysis software EDA Explorer Ledalab
Variables PCIs, collaborative will,
collaborative learning
product, dual learning gain
Sympathetic arousal: level, direction,
and contagion; exam grades
4.1 Participants
The participants in the two data collections for this research were high school
students from the University of Oulu’s Teacher Training School.
52
Participants (N = 24) in the first data collection (see Article I) were 13 males
(54%) and 11 females (46%) aged 16 to 19 years (M = 17.3; SD = 0.62).
Participants (N = 24) in the second data collection (see Articles II and III) were
randomly selected from those enrolled in the regular, elective Advanced Physics
course, which runs twice during the spring term. Twelve students were recruited in
each course run as participants. The gender distribution was six females (25%) and
18 males (75%) aged 16 to 17 years (M = 16.5; SD = 0.5). Their attendance average
was 10.9/12 (SD = 1.3) for the first course run and 10.5/12 (SD = 1.3) for the
second. They were high achievers in the preceding physics course, obtaining an
average grade of 9.0 out of 10 (SD = 0.6).
4.2 Materials
The materials for this study can be divided into four groups: the EDA sensors,
performance instruments, online learning environments, and questionnaires.
4.2.1 The Empatica® E3 and E4 wristbands
Essential tools in the data collection for this dissertation were the Empatica® E3
and E4 (Empatica Inc., Cambridge, MA, USA) biosensor wristbands (see Fig. 4 for
pictures of the E4; the E3 is similar), since physiological data, particularly EDA, is
at the core of this research. The Empatica® E3 was used for the data collection I
(see Article I), and the E4 for the data collection II (see Articles II and III). The
Empatica® E3 and E4 wristbands are equipped with four sensors: a
photoplethysmograph to measure blood volume pulse, an EDA sensor, a 3-axis
accelerometer, and an infrared thermopile to measure peripheral skin temperature.
The EDA sensor, the one used in the studies of this dissertation, has a sampling
frequency of 4 Hz (i.e., it takes four measurements per second). The Empatica® E3
and E4 EDA sensor utilizes the exosomatic method to measure EDA, which
consists of applying a small external current by means of two electrodes and
determining the skin electrical conductance through Ohm’s law (Edelberg, 1967).
The exosomatic method is usually credited to an 1888 study by the French
neurologist Charles Féré, but was already described by the French electrotherapist
and electrodiagnostician Romain Vigouroux in an 1879 study (Edelberg, 1967).
The units for the skin electrical conductance are microSiemens (µS). On a historical
note, resistance, the electrical inverse of conductance, was also used in the early
days of EDA research. However, in 1970, the editor of the journal
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Psychophysiology proposed the elimination of resistance measures and the use of
conductance only when measuring exosomatic activity, based on the peripheral
mechanisms of the skin (Venables & Christie, 1973, p. 6).
The Empatica® E3 and E4 have two modes of operation: recording in internal
memory and real-time streaming via Bluetooth®. The former was deemed more
suitable for the studies of this dissertation, which used offline analysis of the data.
Data recorded in internal memory are stored in a comma-separated values file for
each sensor and are accessible via a USB connection. The EDA file consists of a
first row with the recording starting time, a second row with the sampling frequency
(4 Hz), and the continuous EDA measures in the subsequent rows. The Bluetooth®
streaming affords applications for learning beyond research, such as for learning
analytic dashboards, intelligent tutoring systems, and implementation of alerts in
critical moments, among others. Such potential applications are facilitated by a
software development kit, including a mobile programming interface that enables
the developing of apps to leverage real-time data from the multisensor wristband.
Fig. 4. Front and back view of the Empatica® E4 watch-like biosensor wristband (photos by courtesy of Empatica Inc.).
Although the two data collections involved a larger number of participants, for the
studies of this dissertation, participants were limited by the biosensor wristbands
available. At the time of the first data collection, our laboratory was equipped with
six Empatica® E3 wristbands, and the number rose to 12 Empatica® E4s for the
second data collection, in a purchase deal that involved returning the E3s. The
Empatica® E3 and E4 are research devices, more accurate and precise than
54
consumer-oriented biosensor wristbands available in the market, but also several
times more expensive, which limited the number that our laboratory was able to
acquire, as has happened in other studies also (e.g., Koskimäki et al., 2017).
4.2.2 Performance instruments
The performance measures in the first data collection included a pre-test, a post-
test, and a collaborative learning product (i.e., the groups’ joint solution to the task),
while in the second data collection, a real-life advanced physics course exam was
used. The pre- and post-tests were identical: multiple-choice tests consisting of ten
questions with four options each. An example of a question is: “Fiber intake is
important because it: a) allows for consistent energy supply, b) maintains bowel
health, c) lowers cholesterol levels, and d) contains calcium.” In total, out of the 40
choices, 22 were correct. Shared spreadsheets using Google Sheets technology
were provided for students to collaboratively report on their joint solution to the
task. The physics course exam consisted of three sections (A, B, and C) with three
questions each. Section A was theoretical and focused on the definition, connection,
and explanation of concepts and phenomena (e.g., resonance, interference, and
Doppler effect). Sections B and C had a more practical character and focused on
calculations and graphical representations. The students had to choose and answer
two questions from each section, except from section C, where only one question
was required to be answered. Thus, students had to answer in total five questions
on the exam.
4.2.3 Online learning environments
Two online learning environments were used in this study: weSPOT
(Mikroyannidis et al., 2013) and Open edX (https://open.edx.org/) for the first and
second data collections, respectively. WeSPOT stands for working environment
with social and personal open tools for inquiry-based learning. Accordingly,
weSPOT scripts collaborative learning in compliance with an inquiry-based
learning paradigm (Edelson, Gordin, & Pea, 1999) consisting of five steps: 1) plan
the design method, 2) set the criteria, 3) collect the data, 4) discuss the findings,
and 5) communicate the results. Open edX is an open source software which
powers the edX platform (https://www.edx.org/) for so-called massive open online
courses (MOOCs). It was chosen because of its customization affordances since it
is open source and utilized to create a dedicated online course following the
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traditional course structure as provided by the teachers (e.g., course content,
additional support learning material, simulations, and videos).
4.2.4 Questionnaires
The MSLQ (Pintrich, Smith, Garcia, & McKeachie, 1991, 1993) was used in both
data collections to assess the learning regulation profiles of the students, which
served as criterion for the formation of groups as heterogeneously as possible. The
MSLQ is one of the most frequently used inventories for that purpose (Bjork,
Dunlosky, & Kornell, 2013; Panadero, 2017). The questionnaire consists of 81
items organized in two sections, motivation and learning strategies, which contain
6 and 9 scales, respectively. Each item is rated on a Likert scale from 1 (not at all
true of me) to 7 (very true of me).
The peer learning scale of the MSLQ—comprising items 34, 45, and 50—was
also utilized to assess the student’s predisposition to collaborate in a learning task
(i.e., collaborative will; see Article I). The items of this scale account for the
students’ propensity to explain learning material to group members, work together
with others to complete an assignment, and discuss the task in the group.
A questionnaire (see Table 1 of Article II) to assess students’ perception of
cognitive, affective, motivational, and collaborative aspects on a lesson basis was
adapted from the one employed by Schmitz and Skinner (1993) for an experiment
in a similar setting, a naturalistic study with data collected in the classroom over
four months. The questionnaire consisted of four items asking students to rate their
task domain knowledge, mood, motivation, and group functioning, on a scale of 1
to 10. The simplicity principle prevailed in choosing and tailoring this
questionnaire given the limited time available for these measurements in the
naturalistic setting of our study: a real high school course, packed with content and
coursework, including both paper-and-pencil tasks and hands-on experiments.
Conveniently, the questionnaire was embedded in the dedicated course created on
the Open edX platform. In this way (i.e., simplicity and easiness), and since the
students were expected to complete the questionnaire in every single lesson, we
minimized the impact on the measurement environment (Winne & Perry, 2000).
While larger inventories are considered to be more reliable, rating scales based on
fewer items are acknowledged to be economical and useful under specific
circumstances (Pekrun et al., 2002).
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4.3 Procedure
Two data collections were organized targeting EDA specifically, but also
performance measures and questionnaires as data sources. Two important issues
involving EDA recording were considered in both data collections: room
temperature and biosensor wristband adjustment. Room temperature for EDA
measures is recommended to be 23 °C (Boucsein, 2012). No adjustment was
required in this regard since the temperature in the locations for both data
collections was controlled by a thermostat, whose standard setting coincided with
the recommended temperature. Also, according to the good practices of recording
EDA, students were instructed to wear the biosensor wristband on their non-
dominant hand in order to reduce the effect of movement artifacts and to adjust the
wristband strap neither loosely nor tightly so that the electrodes made proper but
not excessive contact with the skin, which would result in a pressure artifact
(Edelberg, 1967).
In both data collections, students worked in triads that were formed based on
the principles of gender diversity (Bear & Woolley, 2011) and heterogeneity of
learning regulation profiles (i.e., according to the MSLQ scores), for the sake of
between-triad comparability.
4.3.1 Data collection I
Two weeks before the collaborative task, the students were asked to answer the pre-
test and the MSLQ at their school. The collaborative task was carried out in a
classroom-like research infrastructure of the University of Oulu (i.e., the Learning
and Interaction Observation Forum; http://www.oulu.fi/leaf-eng/). The experiment
was organized in four sessions with 12 different participants each (N = 48),
although, for the purpose of this dissertation, only participants wearing the
biosensor wristband were considered (N = 24), that is, six participants in each
session. The sessions took place in the morning and the afternoon of two
consecutive days for a total of four sessions. The duration of the sessions was
2 h 15 min. The first 20 min were for preparation and instructions, followed by
1 h 40 min to work on the actual task and 15 min for the post-test. In the preparation
phase, students were introduced to the materials, particularly the biosensor
wristband, the weSPOT environment, and the collaborative learning task.
Students sat at individual tables clustered in four groups of three. Each table
was supplied with a digital tablet to support the task execution. The students in two
57
groups were each provided with an Empatica® E3 biosensor wristband to measure
EDA.
The task consisted of the design of adequate, healthy breakfasts for at least one
of several cases corresponding to people with different nutritional requirements,
such as a marathon runner in training, a teenager, a patient with a heart condition,
a diabetic person, and someone wanting to lose weight. Relevant variables of the
target hypothetical case, needed to carry out the task, were provided, such as age,
height, weight, and lifestyle details or health issues with implications for the
breakfast characteristics. The task was designed to fit in the students’ science
curriculum by being aligned with their current knowledge and the content and goals
of their science studies.
On the tablet provided to each student, a shortcut to two Google-Docs® files
was available: a document and a spreadsheet. The document detailed the nutritional
needs of marathon runners. The spreadsheet had a template with rows for food
items and columns for the weight (in g) of the different nutrients contained by the
food item. The template included several examples for illustration purposes. Upon
task completion, students delivered their solution through the weSPOT
environment. Finally, the students were asked to answer the post-test.
4.3.2 Data collection II
Ecological validity was at the center of the second data collection. Therefore, the
classroom was the chosen setting. Using Empatica® E4 biosensor wristbands, EDA
was measured in 12 students simultaneously in each of two consecutive runs of an
elective, advanced physics course (including the exam), as the course is offered
twice during the spring term. The course consists of 18 lessons, plus the exam.
There are lessons three times a week, lasting 75 min each. The students put the
wristband on at the beginning of each lesson and took it off at the end. Topics of
the lessons include, among others, wave formation and interference, reflection and
refraction, sound waves, and geometrical optics. In general, the lessons begin with
a theoretical part (i.e., the teacher’s explanation of the topic) followed by a
collaborative practical activity with paper-and-pencil problems, or hands-on
experiments, and instructions and support material in the Open edX environment.
In the practical activity, students put the learned physical theories, laws, and
principles to the test in groups of three. The groups remained the same for the
duration of the course. The students accessed Open edX during class on the tablets
assigned to them by the school as a technological support to their studies. At the
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end of each lesson, students were asked to answer the questionnaire adapted from
Schmitz and Skinner (1993), which was embedded in the Open edX environment.
4.4 Data analysis
Aligned with the materials and data collected, the analysis targeted three data
modalities. First, the EDA data collected by the biosensor wristbands were
processed to extract different relevant indices in accordance with the aims. Second,
the variety of performance instruments were assessed. Third, questionnaire data
were prepared for correlation analysis. Next, the analysis of these three data
modalities is detailed.
4.4.1 Electrodermal activity
Two approaches are used to analyze the EDA data, one utilizing the EDA signal as
such (see Article I), and the other based on sympathetic arousal, a derivative
measure from the EDA signal (see Articles II and III). For efficient use of the
storage capacity in internal memory, the biosensor wristband by design records the
data in a comma-separated values file containing a single column, where the first
row displays the starting time in UNIX format (i.e., the number of seconds elapsed
from 00:00:00 UTC on January 1st, 1970), the second row stores the sampling
frequency in Hz, and the actual EDA signal values (in μS) start from the third row
on. Using the initial time and the sampling frequency (number of samples taken
each second), together with the position of each value, it is straightforward to obtain
the time-value pairs needed for synchronization and analysis. Therefore, common
to both approaches is an initial data pre-processing to transform the file provided
by the biosensor wristband to a data format where each EDA value appears next to
its corresponding timestamp. Next, the specificities of each approach are discussed.
Approach I: Electrodermal activity signal based
In this approach, used in Article I, the EDA signal constitutes the pillar of the
analysis. The EDA signal is susceptible to pressure and movement artifacts
(Edelberg, 1967). Pressure artifacts are linked to wristband maladjustment when it
is put on too tight. To reduce pressure artifacts, students were instructed to wear the
wristband not too tight. As for reducing movement artifacts, as explained earlier,
students were asked to wear the biosensor wristband in their nondominant hand.
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Notwithstanding, it is necessary to identify possible artifacts in the data to be able
to discard them in the analysis phase for the sake of the reliability of the results.
The web-based tool EDA Explorer (Taylor et al., 2015) was used to automatically
detect artifacts. EDA Explorer employs a machine learning classifier to determine
on a 5-second epoch basis whether it corresponds to noise or a clean signal.
Reportedly, the accuracy of such a binary classifier surpasses 95% (Taylor et al.,
2015). The outcome of applying EDA Explorer to the EDA signal recorded in the
first data collection was that 490,880 from 547,848 data points (i.e., 90% of the
signal) were classified as clean by the algorithm. Consequently, those were the data
selected for the next analysis steps, while the other 10%, labelled as noise, was
discarded at this point.
The PCIs to be computed from the clean EDA look at different parameters of
the signals. Some of these PCIs are robust in respect to individual differences in the
SCRs (e.g., directional agreement is immune to anything related to amplitude as it
only considers the direction), while others are highly sensitive to it (e.g., the signal
matching precisely accounts for amplitude differences). Therefore, especially for
the sensitive cases, it is necessary to normalize the signal so as to make it
comparable across group members. The individual differences are caused, for
example, by the thickness of skin (Braithwaite, Watson, Jones, & Rowe, 2015),
especially the thickness of the corneum (M. E. Dawson et al., 2017), which is the
outermost layer of the epidermis. Normalization for comparability purposes was
then accomplished by calculating the t-scores (i.e., subtracting the sample mean
from every value and then dividing by the sample standard deviation). Both sample
M and SD were computed on an individual basis.
The final step before computation of the PCIs is the synchronization in each
group of the clean, normalized data for all group members. Since each individual
signal has a timestamp for each data point, the synchronization process can be
basically seen as the alignment of the timestamps at the group level. Although, the
synchronization step was carried out after normalization, the reader should note
that both processes are totally independent and, as such, can be executed in any
order without affecting the final result.
The clean, normalized, and synchronized data are the base for the computation
of the PCIs. Since, by definition, most of the PCIs are computed pairwise, with
directional agreement being an exception as it easily extends to groups of any size,
the PCIs were obtained pairwise for all triad member combinations (AB, AC, and
BC). The PCIs used are signal matching, instantaneous derivative matching,
directional agreement, Pearson’s correlation coefficient, and Fisher’s z-transform.
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Signal matching consists first of a between signal differentiation and then of
an aggregation. This is, instantaneous absolute differences in the two students’
signals were computed and then aggregated as the mean of the entire collaborative
learning session.
Instantaneous derivative matching compares the slopes of the two students’
signals. Mathematically, this is expressed in Eq. (1) as:
∑ | − − − |, (1)
where T is the time in number of samples of the signal, a and b refer to the signal
of each student, respectively, t refers to the current data point, and t+1 to the next
data point.
Directional agreement refers to the percentage of the time in which the signals
(EDA in this case) of the students are going in the same direction, that is,
simultaneously rising, falling, or staying constant. For the computation of
directional agreement, first it was determined whether there was instantaneous
agreement or not, and then the percentage of agreement was obtained.
Pearson’s correlation coefficient needs no further explanation as it is a standard
and widely used measure in statistical analysis. Fisher’s z-transform is a
transformation of Pearson’s correlation coefficient—by applying the inverse
hyperbolic tangent—so as to obtain a normally distributed index.
Approach II: Sympathetic arousal based
In this approach, followed in Articles II and III, EDA is not used as such, but as the
means to index sympathetic arousal, the degree of physiological activation, in this
case from the sympathetic nervous system. Since arousal was to be indexed by the
frequency of SCRs, the MATLAB-based Ledalab (version 3.4.9;
http://www.ledalab.de/) software was used for the detection of SCRs. It allows for
the separation of superimposed responses using continuous decomposition analysis
by means of nonnegative deconvolution (Benedek & Kaernbach, 2010b). Thus,
Ledalab helps to address the traditional bias of counting the SCRs without
accounting for those in a superimposed response, a classical method known as
trough-to-peak (Boucsein, 2012). Ledalab is also the tool for the analysis of the
EDA signal recommended by the E3 and E4 biosensor wristband manufacturer
(Empatica Inc., 2018).
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The Ledalab algorithm assumes raw, unfiltered data as the input for
decomposition analysis and feature extraction of the EDA signal (Benedek &
Kaernbach, 2010a). Therefore, no signal preprocessing, such as cleaning or
filtering, was performed on the EDA signal as obtained from the wristband sensor.
In determining which change in conductance will be considered an SCR, an
amplitude threshold needs to be specified as parameter. For this dissertation,
0.05 μS have been chosen for the threshold as it is a long-time standard owing to
the maximum sensitivity of earlier EDA sensors (Braithwaite et al., 2015; Venables
& Christie, 1973, 1980).
By means of Ledalab, the SCR onsets (i.e., the starting point of the peaks) were
determined from the EDA recordings. The onsets were used to compute the SCR
frequency on a minute basis, to be used as a measure of arousal in peaks/min (ppm).
The calculation of SCR frequency followed a moving window (width 1 min; step
250 ms) approach, so that arousal at each instant would be the count of SCR onsets
in the previous minute. Arousal was thus computed every 250 ms, the maximum
temporal resolution available as it is the sampling interval of the Empatica® E3
and E4 EDA sensors. The frequency approach has the advantage of not having to
standardize the data for comparison across individuals, as amplitude measures are
not considered. The frequency on a minute basis and the total count of SCRs or
arousal episodes were both used in the analysis to answer different RQs, according
to their scope and target.
The SCR frequency was also used as the criterion to categorize arousal into
low, medium, and high levels, based on the literature. Typically, a frequency of 1–
3 ppm occurs at rest (M. E. Dawson et al., 2017, p. 225), and as frequency increases
with the arousal level, values higher than 20 ppm are interpreted as high arousal
(Boucsein, 2012, p. 222). Accordingly, frequencies of up to 3 ppm were labelled as
low, from 20 ppm and up were labelled as high, and anything in between was
labelled as medium. The incidence of each arousal level was determined for every
student in terms of percentage relative to the time of each lesson. The persistence
of each occurrence of an arousal level for every student was computed as the
continuous duration of the occurrence (i.e., without switches of level in between).
Notwithstanding the precautions taken in instructing students on the correct
placement of the biosensor wristband, its occasional maladjustment resulted in the
measurement of basically noise rather than an actual EDA signal, due to inadequate
electrode-skin contact. Therefore, an important part of the analysis consisted in the
removal of those cases for the sake of the reliability of the results. A technique for
automatic assistance to such a purpose was developed for this dissertation, building
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on the typical frequency of SCRs at rest (1–3 ppm). Essentially, based on the
minimum of 1 ppm as the worst case, the students’ EDA recordings where the
number of SCR onsets was less than 1 ppm times the lesson duration in min were
not considered in the analysis. That was the case for 137 (33%) of the EDA
recording sessions during the course lessons. Due to students being absent for some
lessons, 46 (11%) of the expected recording sessions were missing. Taking together
the missing sessions and those discarded, 237 (56%) were available for further
analysis at the individual level (see Article II). As for the group level (see Article
III), it was necessary to identify the cases where valid EDA data were available for
all members of a triad in a certain lesson, since the unit of analysis in this context
was the triad. Eighteen such cases (corresponding to 18 x 3 = 54 recordings) were
found, meaning that in the final sample for group level analysis, 7 of 8 triads and
15 of 35 lessons were represented. Fortunately, in the exam, there were no absent
students, and no EDA data had to be discarded as noisy.
In the previous approach (see Approach I: Electrodermal activity signal based)
one of the PCIs used was directional agreement, computed directly on the EDA
data. For Article III, directional agreement was selected among the PCIs for its easy
and direct calculation for groups of any size, since the other indices can only be
computed pairwise, and also since it produced the better results in the analysis for
Article I. In addition, since the focus of Article III was on sympathetic arousal in
collaborative learning, directional agreement was applied to the arousal signal
derived from the EDA signal, rather than to the EDA signal as such as in Approach
I. Moreover, Approach I applied directional agreement to the whole session, as this
approach uses a moving window allowing for a continuous measure of directional
agreement for every instant during the lesson after the second initial minute. The
latter is because a first minute is necessary for the initialization of the arousal
measure, and a second minute is necessary for the first computation of directional
agreement based on those initial arousal values. Previously, directional agreement
has been calculated immediately from data points (e.g., Elkins et al., 2009;
Montague et al., 2014), but it was noticed from the data that sometimes there are
instantaneous fluctuations that do not actually represent the direction when the
adjacent samples are considered. For example, there can be an instantaneous small
increase in one value in respect to the previous one, but this could actually be a
tendency to decrease if we look at the neighboring data. To overcome this issue, it
was decided to base the direction calculation on one-minute trend lines rather than
on two consecutive values which are 250 ms apart. Trend lines were determined
for one-minute windows every 250 ms (moving window), and the sign slope of the
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trend line was used as the measure of direction for that instant, namely, upward (+1),
constant (0), or downward (–1). This means that the arousal direction at each
instant was the sign of the slope of the trend line involving the previous minute of
data. Trend lines were obtained using MATLAB’s built-in polyfit function. Finally,
again using a moving window of 1 min with a step of 250 ms, the arousal
directional agreement for each triad at every particular instant was computed as the
percentage of the previous min in which all triad members’ arousal was going in
the same direction.
Another phenomenon targeted in the analysis was high arousal contagion (see
Article III). To that extent, the duration of each high arousal interval was marked at
the beginning of the interval. Then, for each interval, it was determined if, within
it, some other triad member(s) reached the level of activation of high arousal
(influencer point of view or potential arousal contagion), and, if so, at which latency
(i.e., time elapsed from the interval start to a triad peer’s high arousal interval start).
It was also determined how many triad peers could have caused it via arousal
contagion (influenced point of view), if any.
4.4.2 Performance
The pre- and post-tests were graded from 0 to 40 by adding the sum of options
chosen or left unchosen correctly (scoring 1) or incorrectly (scoring 0). The
individual learning gain was calculated by subtracting pre-test from post-tests
scores. The dual learning gain was aggregated as the sum of the individual learning
gains.
The collaborative learning product was measured as the grade of the groups’
joint solution to the task of designing appropriate breakfast menus for people with
different needs. The solution was graded from 4 to 15 by a research assistant,
according to the variety of the breakfast (up to 3 points; e.g., 1 if there were <3
ingredients), accuracy (up to 3 points; e.g., 2 if there was realistic information about
the macronutrients and fiber, but the solution contained small mistakes in the
calculations), adequacy in terms of matching the needs of macronutrients (up to 3
points) and energy (up to 3 points), attention to all the special needs of the particular
person (up to 2 points) of the different cases, and number of breakfast menus
designed (1 point for more than one).
The results of both the dual learning gain and the collaborative learning product
were correlated (see Article I) to five PCIs: signal matching, instantaneous
derivative matching, directional agreement, Pearson’s correlation coefficient, and
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Fisher’s z-transform. The purpose was to investigate the predictive power of such
indices in terms of the collaborative learning outcomes.
The exam of the advanced physics course (data collection II) was graded by
the teacher. In this sense, it constituted an authentic measure as it is precisely how
the students’ achievement is determined in real life. In light of the Yerkes-Dodson
law, this performance measure was correlated to the students’ number of
sympathetic arousal episodes (indicating sympathetic activation) during the exam
(see Article II).
4.4.3 Questionnaires
The MSLQ scales are scored by averaging the ratings of the items belonging to
them (Pintrich et al., 1991). Consequently, the collaborative will, as measured by
the peer learning scale, ranges from 1 to 7, similar to the individual items. The
collaborative will was correlated to the five PCIs (see Article I): signal matching,
instantaneous derivative matching, directional agreement, Pearson’s correlation
coefficient, and Fisher’s z-transform. The purpose was to study the relation between
such objective indices and the students’ subjective ratings as to their propensity to
engage in collaborative behavior.
The ratings of the questionnaire adapted from Schmitz and Skinner (1993)
were used directly for correlation with the in-class arousal, as measured by the
count of SCRs or sympathetic arousal episodes. The questionnaire measured
students’ perception of cognitive, affective, motivational, and collaborative aspects
on a lesson basis. Unfortunately, in some lessons, students did not complete the
questionnaire as required. From the 237 cases where valid student EDA data were
available, 153 had the corresponding answered questionnaires. Hence, correlation
analyses between the questionnaires and EDA data were performed on this subset
of 153 cases.
4.5 Research evaluation
Since EDA is at the core of this research, its evaluation as a physiological response
and as a signal is paramount for reliability, together with the validity of its use as a
proxy for sympathetic arousal.
Research utilizing EDA in a variety of disciplines has a solid tradition spanning
over a century, with the large number of studies stimulating periodic and
comprehensive reviews over the decades (Boucsein, 1992, 2012; Critchley, 2002;
65
M. E. Dawson et al., 1990, 2007, 2017; Edelberg, 1967; E. Neumann & Blanton,
1970; Venables & Christie, 1980). To date, EDA remains one of the most common
data sources in the field of psychophysiology (Boucsein, 2012; Cacioppo &
Tassinary, 1990a; Kreibig, 2010), that is, the area of study of psychological
processes based on physiological signals. The results of the extensive research EDA
has been the subject of have enabled its consideration in the scientific community
as a well-validated, widely accepted measure of sympathetic arousal (Critchley,
2002). In addition, it is regarded as perhaps the most readily accessible and simplest
indicator of sympathetic arousal (E. Neumann & Blanton, 1970), in connection to
the exclusive characteristic of the electrodermal system as being uniquely
innervated by the sympathetic nervous system (Boucsein, 2012). In the early years
of EDA, there were discrepancies as to whether the EDA phenomenon was
produced by vasomotor activity or sweat gland activity, but its interpretation as a
measure of arousal has undisputedly accompanied EDA since its very discovery
(Edelberg, 1967). Building on such a rich and long tradition, it is therefore
considered valid to use EDA in this dissertation to index sympathetic arousal.
No less important is the ecological validity (see A. L. Brown, 1992) of the data
collections of this dissertation for the interpretation of the results. In this regard, it
is a strength of this research that the study for Articles II and III took place during
a real course in an actual classroom, while the study for Article I took place in a
classroom-like research environment, with a task integrated in a high school
science curriculum. Not only the locations contributed to the ecological validity,
but also the unobtrusive, seamless nature of the comfortable, watch-like, and
scientific-quality of the wearable biosensor used. Although it has to be
acknowledged that the palms and soles constitute the preferred skin sections for
recording EDA (Boucsein et al., 2012), as they contain the highest density of
eccrine sweat glands (Critchley, 2002; Edelberg, 1967), studies have shown that
the wrist is a viable alternative to them (Poh et al., 2010; van Dooren et al., 2012),
while also being more practical for daily life measurements. The EDA data
collections also complied with the recommendations of recording the signal from
the nondominant hand to minimize motion artifacts (Boucsein et al., 2012;
Edelberg, 1967), and under a constant temperature of 23 °C to minimize
thermoregulatory interference (Boucsein, 2012).
Technological advances contribute to the accessibility and reliability of
physiological measures of interest in psychological research (Cacioppo & Tassinary,
1990a; Swan, 2012), including applications to learning (Schneider et al., 2015). In
this context, the Empatica® E3 and E4 biosensor wristbands have been designed
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purposefully for scientific research, while being practically unobtrusive (Garbarino
et al., 2014). Their EDA sensor has been validated (Empatica Inc., 2017) against
the Procomp® Infiniti SA9309M EDA sensor (Thought Technology Ltd., Montreal,
Canada). Successful validation has been carried out also for other wristband sensors
(e.g., McCarthy, Pradhan, Redpath, & Adler, 2016). However, despite the quality
of the sensor, part of the data were discarded (see Sub-chapter 4.4.1) as a result of
concerns regarding noisy recordings possibly due to wristband maladjustment. The
analysis for the first data collection used the EDA Explorer software to detect noise
within the EDA signal, with a reported accuracy of 95% (Taylor et al., 2015). In the
case of the second data collection, the algorithms powering the Ledalab software
used are a recent development in EDA signal processing based on mathematical
and physiological modeling (Boucsein, 2012). Ledalab is the tool to analyze EDA
that is currently recommended by the Empatica® E3 and E4 manufacturer
(Empatica Inc., 2018). The amount of data discarded is comparable to that of other
studies also using biosensors in a science high school classroom (e.g., Arroyo et al.,
2009). Further technological developments are needed to systematically maximize
the amount of valid data. Nevertheless, the dimensions of the second data collection
involving two entire runs of a physics course still allowed us to study the
phenomenon of interest at the individual and interpersonal levels in collaborative
learning. The several-month data collection together with the two course runs also
helped to compensate for the number of participants that was limited by the number
of biosensor wristbands available.
It is known that gender plays a role in collaboration (Bear & Woolley, 2011),
and that same gender structure is preferable when comparing group measures based
on EDA (Boucsein et al., 2012). Unfortunately, due to the predominantly male
gender of the students enrolled in the course for the second data collection, it was
not possible to compose all triads with the same gender structure, having six triads
with one female and two males, and two triads composed of three males.
Nonetheless, clear effects of gender in psychophysiological studies have been
elusive (Cacioppo & Tassinary, 1990a). However, research using
electrocardiography and/or EDA measures has reported largely comparable
responses in males and females, resulting in gender having only a small effect (S.
A. Neumann & Waldstein, 2001), and in other cases has deliberately excluded
gender from analysis after initial examination of results, due to an inconsistent
effect on the variables (Simo Järvelä et al., 2014). Therefore, the reportedly small
and inconsistent effect of gender mitigates this limitation.
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Finally, the fact that the second data collection was conducted with a student
population of high achievers must be considered when it comes to external validity
or generalizability of the results to a more heterogeneous sample.
4.6 Ethics
This dissertation follows the ethical guidelines and principles of research in the
humanities and social and behavioral sciences of the Finnish Advisory Board on
Research Integrity (2009; 2012). Such principles involve the three areas of
respecting the autonomy of research subjects, avoiding harm, and insuring privacy
and data protection.
Since physiological data were to be collected on the participants while learning,
an ethical statement at the project level was requested to, and the corresponding
approval obtained from, the Ethics Committee of Human Sciences of the University
of Oulu.
Participation in the study was voluntary, and refusing to participate had no
implication at all for the students’ grades. An informed written consent was
obtained from the students, who could revoke it at any time during the respective
studies.
Written information was provided to the students and their parents, including
the principal researcher’s contact information, the research topic, the method of
collecting data and the estimated time required, the purpose for which data would
be collected, how it would be archived for secondary use, and the voluntary nature
of participation (Finnish Advisory Board on Research Ethics, 2009, p. 7). In
addition to the written information, the principal researcher of the project, together
with several project members, detailed the information orally to the students prior
to the data collection and answered their questions.
Since the first data collection took place in a classroom-like laboratory outside
the school, the task was coordinated with the science teachers so that it was part of
their course work, meaning it did not represent extra effort for the students. In the
second data collection, the measurements were taken in their regular classroom
following a minimum intervention method.
The personal data collected were those strictly necessary to analyze the data
and report on the research. For the data analysis phase, data were anonymized by
replacing students’ names with numeric identifiers. Such anonymization also
allowed for protection of privacy in any potential research publications that would
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report the findings. To ensure confidentiality, the digital library data were stored
and made available only to authorized research team members
During the studies, pictures were taken to support research dissemination by
helping stakeholders to better understand the design, setting, environment, material,
cognitive-affective states, and so on. Due to the sensitivity of this information, their
informed written consent was also requested. However, even in those cases where
consent was obtained, using pictures with faces was avoided unless it was necessary
to make a point about a particular relevant cognitive-affective state.
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5 Overview of the articles This dissertation involves three published scientific articles, which address the four
aims pursued. In investigating psychological processes associated with learning
through the proxy of a concomitant physiological signal, the articles follow
multidisciplinary approaches at the intersection of the learning sciences,
psychophysiology, and computer science. Articles I and III focus on the
interpersonal level within triads, and Article II examines sympathetic arousal in the
classroom at the individual level. The connection of the articles to the dissertation
aims and to the data collections is summarized in Table 2.
Table 2. Overview of relevant aspects about the articles.
Article Aim Data collection Individual/Interpersonal
I 1, 3 I Interpersonal
II 2, 3 II Individual
III 1, 2, 4 II Interpersonal
5.1 Article I: Investigating collaborative learning success with physiological coupling indices based on electrodermal activity
This article investigates the interpersonal physiology of EDA in connection to
collaborative learning processes (Aim 1) and to students’ academic achievement
(Aim 3). The article is based on the first data collection of the dissertation (see Sub-
chapter 4.3.1). Participants (N = 24) were high school students tasked with
designing a healthy breakfast for an athlete training for a marathon. The task was
aligned with their science curriculum and fit into their coursework. Students
worked in groups of three to solve the task.
Interpersonal physiology was operationalized through PCIs involving
parameters such as the amplitude (signal matching), rate of change (instantaneous
derivative matching), direction (directional agreement), linear relationship
(Pearson’s correlation coefficient), and weighted correlation (Fisher’s z-transform).
Three collaborative learning measures were explored in relation to the PCIs (Aim
1): 1) the collaborative will, that is, the predisposition to collaborate in a learning
task, measured using the peer learning scale of the MSLQ (Pintrich et al., 1993);
2) the collaborative learning product, that is, the task solution produced by the
students in collaboration; and 3) the dual learning gain, that is, the aggregate of the
individual learning gains as determined by the difference between pre- and post-
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test scores. The collaborative learning product and the dual learning gain were used
as indicators of academic achievement.
Directional agreement, the percentage of the time that the EDA of the group
members were going simultaneously in the same direction (i.e., upwards,
downwards, or constant), was found to be the best predictor (r = .7) of the dual
learning gain (Aim 3). In other words, the more the team was physiologically
coupled in terms of the direction of their EDA, the more they learned together.
Directional agreement was also the only physiological coupling index found in the
literature that is readily applicable to teams of any size, while the other indices can
only be applied pairwise by definition, meaning that for teams with more than two
group members, the indices have to be computed for all the possible pairs and then
aggregated. The latter is not only more laborious, but also complicates
operationalization and interpretation, since there are several competing approaches
for aggregation. As a physiological coupling index, directional agreement emerged,
thus, as being simpler and more convenient with more explanatory power than
signal matching, instantaneous derivative matching, Pearson’s correlation, and the
Fisher’s z-transform, when considering measures for teams of more than two
individuals.
Instantaneous derivative matching, which accounts for the similarities in the
rate of change of the EDA signal, was found to have a moderate correlation to the
collaborative learning product (r = .59; Aim 3) and to the collaborative will (r = .5;
Aim 1). This is to say that teams for which the rate of change of the EDA signal of
their members was closer produced a better task solution, and the teams with
individuals more prone to collaborate showed a more similar rate of EDA change.
5.2 Article II: Profiling sympathetic arousal in a physics course: How active are students?
This study builds on the notion that learning is an active process (Bjork et al., 2013;
Hebb, 1955) involving cognitive and emotional processes and that arousal is a
physiological component of those processes (Critchley, 2002; Poh et al., 2010).
Learning activations are examined from an EDA perspective. The approach chosen
is necessarily exploratory due to the lack of studies characterizing sympathetic
arousal in the naturalistic setting of an actual class, as opposed to in a laboratory.
Sympathetic arousal is profiled in the classroom (Aim 2)—in terms of incidence
and persistence of low, medium, and high arousal levels—during two consecutive
runs of an elective advanced high school physics course, which is offered twice to
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the students during the spring term. Incidence was described in terms of percentage
relative to the time of each lesson, which was 75 min. Persistence was
operationalized as the continuous duration of a certain arousal level (i.e., without
switches of level in between). Participants in the data collection (see Sub-chapter
4.3.2) were high school students (N = 24) who were randomly selected from the
course population. In the course exam, the relation between arousal and
performance was also explored (Aim 3) in the light of the well-known Yerkes-
Dodson law (Yerkes & Dodson, 1908) as theoretical background.
EDA was measured unobtrusively during the course lessons and exam via the
Empatica® E4 wristband to index arousal by the frequency of SCRs, which
resemble peaks in the signal. Based on the peaks/min (ppm), the level of arousal
was categorized into low, medium and high (Boucsein, 2012, p. 222; M. E. Dawson
et al., 2017, p. 225). There were at least six changes of arousal level within the
lessons, with an average of 48, representing a change every 1.5 min. High arousal
was the only level that did not occur for each student in every lesson. On average,
low arousal was the level of highest incidence (60% of the lesson) and longest
persistence, lasting on average three times longer than medium arousal and two
times longer than high arousal level occurrences.
In general, this picture of pervasive low arousal in the classroom is aligned
with a similar finding from a study involving three computer-based learning
environments (R. Baker et al., 2010), where boredom (i.e., a low arousal state) was
found to be the most persistent state, in contrast to others in the high arousal area,
such as confusion, frustration, and engaged concentration. This might be
considered, at best, to be neutral and, at worst, detrimental to learning.
During the course exam, arousal was positively and highly correlated (r = .66)
with achievement (Aim 3) as measured by the students’ grades; the more active the
students in terms of sympathetic arousal, the higher the grades and vice versa. The
study adds to the extant literature by providing evidence supporting the left half of
the Yerkes-Dodson curve (i.e., non-detrimental arousal) during an authentic exam
situation, from a sympathetic arousal perspective as indexed by EDA.
5.3 Article III: Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members?
This paper explored sympathetic arousal during collaborative learning in the
naturalistic setting of the classroom (Aims 1 & 2) by studying, first, the
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commonalities in the direction and level (i.e., low, medium, high) of arousal in a
triad, and, second, the phenomenon of arousal contagion (Aim 4) inspired by the
notion of emotional contagion (Hatfield et al., 1994). The study benefited from the
affordances of the Empatica® E4 biosensor wristband for unobtrusive and
continuous measurement of EDA to index sympathetic arousal. EDA was measured
in 24 high school students working in triads during the lessons of two runs of an
elective advanced physics course (see Sub-chapter 4.3.2).
Most of the time (≈60–95% of the lesson) the triad members were at different
arousal levels, and, when they were at the same level, it was mainly the low arousal
(or deactivated) level. The low percentage of simultaneous medium or high arousal
suggests that, when the triad is doing something, the work is being led or conducted
by one or two of the triad members who have been termed task-doers or the people
who are leading the problem-solving at the moment (Miyake & Kirschner, 2014).
Meanwhile, the other member(s) is/are less involved or engaged. Given the
synchronicity and coordination presupposed in collaborative learning as a jointly
produced activity (Enyedy & Stevens, 2014; Roschelle & Teasley, 1995), where
joint attention is paramount (Tomasello, 1995), and given the close relation
between arousal and attention (Sharot & Phelps, 2004), the results provide evidence
for the claim that only certain specific phases of group work are truly collaborative
(M. Baker, 2015).
Possible within-triad arousal contagion cases (Aim 4) took place mostly on a
1:1 basis (71.3%) and with a latency from within a few seconds up to 10 min, but
usually within 1 min. The predominance of arousal contagion on a 1:1 basis
suggests that the strongest interactions, including influence, occur mostly at a pair
level, even if the team is larger, a triad in this case. In addition, the study illustrates
the potential of physiological measures to enrich and strengthen research on
collaborative learning by exploring a new form of data using a computational
approach with high granularity to characterize the process, which is significantly
less labor-intensive than traditional approaches, such as conversation and video
analyses, and offers affordances which are all welcome and needed in the field
(Wise & Schwarz, 2017).
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6 Main findings and discussion In connection to the dissertation aims, the main findings are presented and
discussed in this chapter. Results linked to Aims 1 and 2 are presented and
discussed in Sub-chapter 6.1. The relation of academic achievement to the
individual and interpersonal physiology of EDA (Aim 3) is examined in Sub-
chapters 6.2 (individual) and 6.3 (interpersonal). Finally, the sympathetic arousal
contagion among group members as a result of the cognitive-affective social
interactions in collaborative learning (Aim 4) is discussed in Sub-chapter 6.4.
6.1 Triad arousal levels in collaborative learning
In the naturalistic space for formal learning, the classroom, low arousal had the
highest incidence among activation levels (i.e., low, medium, and high). On average,
students spent 60% of each lesson at a low activation level, accounting for more
than half of the class. These results are conspicuous in the light of, on the one hand,
the interest that is reasonable to assume in the students, as the advanced physics
course subject of study is elective, and on the other hand, the intricate
interconnection of interest, attention, engagement, and arousal. In addition,
episodes of low arousal were the most persistent, averaging three times longer than
those of medium arousal and two times longer than high arousal level occurrences.
Taken together, these findings provide a profile of sympathetic arousal in the
classroom at the individual level (Aim 1).
This picture of pervasive low arousal in the classroom, in general, is aligned
with a similar finding from a study involving three computer-based learning
environments (R. Baker et al., 2010), where boredom (i.e., a low arousal state) was
found to be the most persistent state, in contrast to others in the high arousal area,
such as confusion, frustration, and engaged concentration. Therefore, in both digital
and physical learning environments, low arousal levels seem to be the standard.
Unfortunately, students at a low arousal level are considered, in general, to be
disengaged (Pekrun et al., 2002), and at best, to be accumulating shallow facts in
comfortable learning environments without challenges, which does not yield deep
learning (D’Mello & Graesser, 2012). It has to be acknowledged that, as much as
moments of relaxation facilitate disengagement, low arousal might serve the
recovery purposes necessary in between intense periods of cognitive efforts and
could also reinforce motivation for the next stage of learning (Pekrun et al., 2002).
Consequently, low arousal plays a role in learning, but, ideally, the proportion of
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low arousal should be small in comparison with states of activation (i.e., medium
and high arousal), as learning is most effective when it is active (Bjork et al., 2013;
Hebb, 1955).
The predominance of low arousal (i.e., low activity or inactive students) in
class, even when students themselves choose to enroll in the course, which
presupposes interest, might support the view that, too often, school tasks and
practices are inappropriate for the intended learning goals (Scardamalia & Bereiter,
2014), thus failing to engage even interested learners. Moreover, it supports the
claim that more attention should be paid and resources dedicated to tackling the
problem of bored students (or inactive ones, in general), than dealing with confused
or frustrated students (R. Baker et al., 2010), because the latter both exist less often
and have shorter persistence on average.
The previous result (i.e., predominance of low arousal in the classroom) has an
individual level scope. At the group level, this dissertation explored the
commonality in the arousal levels of triad members during collaborative learning
(Aim 1 and group level part of Aim 2). The triad members were at different arousal
levels most of the time (≈60–95% of the lesson). When they happened to be at the
same level, it was mainly the low arousal (or deactivated) level, while <4% of the
lesson the triad members were simultaneously at the high arousal level. As far as
the synchronicity and coordination presupposed in collaborative learning, this
result is aligned with findings of systematic inequalities in participation among
group members (Cohen, 1994), infrequency of true collaboration (Barron, 2003),
and claims that, most likely, only specific phases of group work are collaborative
(M. Baker, 2015). In the same vein, Dillenbourg (1996) proposes to refer to precise
categories of interactions rather than to collaboration.
The use of interactive activities, such as communication about and
coordination of activities (e.g., transactive activities; see P. A. Kirschner et al., 2018)
during the learning process, is ascribed to positively affect the learning outcome
(Vogel & Weinberger, 2018). However, the difference in the triad members’ arousal
levels might indicate a division of labor, or that the students are taking turns in
doing the collaborative work (i.e., alternating task-doers). Some spontaneous
division of labor may occur in collaboration, in which there are two roles, task-doer
and observer, corresponding to task execution and task monitoring, respectively
(Miyake, 1986). The distribution of roles depends on the nature of the task and may
change frequently (Dillenbourg et al., 1996). In any case, division of labor is a
feature associated with cooperation rather than collaboration (Dillenbourg, 1999).
Although cooperation and collaboration are hardly separable in practice when it
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comes to group work (Jeong & Hmelo-Silver, 2016), the finding of triad members
being mostly at different arousal levels points to cooperation taking the lion’s share
of group work.
When given a group task, it has been shown that students tend to approach it
as individual work, unless there is some reason for the group to interact (Cohen,
1994). Hertz-Lazarowitz (1989) distinguishes between simple (low) and complex
(high) collaborative group tasks. According to her, the former tends to be
characterized by interactions among the means and the product, while in the latter,
students interact about the process to do the work together, involving discussions
on planning and decision-making, which she argues lead to more high-level
elaboration and, consequently, to more effective learning. Collaboration needs to
be structured beyond physical proximity to others, discussing material with other
students, helping other students, or sharing materials with other students (Johnson
& Johnson, 2002). The importance of coordinated and cohesive participation has
been repeatedly emphasized by collaborative learning research (e.g., Barron, 2000;
Fransen, Weinberger, & Kirschner, 2013; Kreijns, Kirschner, & Jochems, 2003).
6.2 Arousal during exams and achievement
The relation between individual physiology and academic achievement is part of
the third aim of this dissertation. A high, positive, and statistically significant
Pearson’s correlation (r = .66; p = .02) was found between the number of
sympathetic arousal episodes of the students during the course exam, as indexed by
the count of SCRs, and their achievement, as measured by their exam grades. The
more active the students, the higher the grades and vice versa. This result is aligned
with the well-known Yerkes-Dodson law (Hebb, 1955; Yerkes & Dodson, 1908)
and places our observation space on the left half of the inverted-U curve that
characterizes performance as a function of arousal according to the law. The
inverted-U shape describes how performance improves with arousal but only up to
a point from where any further increase in arousal (e.g., excessive stress or strong
anxiety) impairs performance (see Fig. 3). Because the participants were high
achievers in the previous physics course, it is reasonable to presuppose that they
would have high physics self-efficacy (Bandura, 1982) or confidence in their
capacity to succeed in the exam. Therefore, stress and anxiety at the level that
hinders performance was neither expected nor observed. The Yerkes-Dodson law
has been previously tested across a variety of cognitive tasks in laboratory settings
(Sanbonmatsu & Kardes, 1988). Arousal during exams has been explored in
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relation to academic emotional reactions (especially test anxiety), using a variety
of physiological responses, namely, those from the cardiovascular, endocrine, and
immune systems, but not from the perspective of pure sympathetic arousal
(Spangler, 1997). This study adds to the extant literature by providing evidence
supporting the left half the Yerkes-Dodson curve (i.e., non-detrimental arousal)
during an authentic exam situation, from a sympathetic arousal perspective as
indexed by EDA.
6.3 Pairwise directional agreement and dual learning gain
The second part of aim three of this dissertation concerns the relation between
interpersonal physiology and academic achievement. Directional agreement
measures the percentage of the time that the signals of a number of individuals are
going in the same direction (i.e., upwards, downwards, or constant). During the
collaborative task of designing a healthy breakfast for a marathon athlete, EDA-
based directional agreement was found to have a strong correlation (r = .7) to the
dual learning gain, the aggregation of individual learning gains, which were
determined by means of pre- and post-tests. In other words, at the pairwise level,
students whose sympathetic activations as indexed by their EDA signals were more
attuned in terms of direction learned better together. The commonalities in EDA
direction might reflect a process of joint attention, as sympathetic activation
(arousal) has been shown to increase with attention (Critchley, 2002). Joint
attention, in turn, might be a result of the mutual engagement and coordinated effort
to solve the problem together, which defines collaborative learning (Roschelle &
Teasley, 1995).
Directional agreement explained almost half (r2 = .49) of the variation in the
dual learning gain. Of the PCIs explored (i.e., signal matching, instantaneous
derivative matching, directional agreement, linear correlation, and Fisher’s z-
transform), directional agreement proved to be the best predictor of this indicator,
which is in agreement with a former study showing directional agreement as the
most sensitive PCI to differences in team performance (Elkins et al., 2009).
This is a useful result, considering, on one hand, that directional agreement is
simpler than the other PCIs studied and, on the other hand, that it is the only one
which scales up best (i.e., it can be calculated directly for groups of any size, as
compared to pairwise only calculations of the other PCIs).
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6.4 Within-triad high arousal contagion in collaborative learning
The fourth aim of the dissertation pursues the exploration of sympathetic arousal
contagion among group members during collaborative learning. The cognitive-
affective social interactions which take place in collaborative learning act as a
mechanism through which emotional contagion (Hatfield et al., 1994) might occur
(Thorson et al., 2018). Being that arousal is one of the two dimensions of emotion
in the circumplex model of affect (Russell, 1980), it motivated the notion of arousal
contagion explored in this dissertation, derived from the emotional contagion
phenomenon. The focus is on high arousal contagion as the most intense activation
level.
At the individual level, 635 high arousal intervals occurred, of which 263 (41%)
might have caused or have been caused by arousal contagion, meaning that they
took place at the time that some other triad member was experiencing a high arousal
interval. In other words, a triad member in high arousal causes a teammate (or two)
to reach that level of activation in 4 of 10 intervals of high arousal. It is worth noting
that this ratio serves as an upper boundary, since the other triad member(s) might
have come to the high arousal level on his or her own through mechanisms other
than arousal contagion. Nonetheless, to assume that, most likely, triad members
might be influencing one another appears reasonable, given that, although not
impossible, it is difficult to isolate oneself in a collaborative learning setting
(Miyake & Kirschner, 2014).
The possible arousal contagion cases took place mostly (71.3%) on a 1:1 basis,
and 1:2 contagion cases accounted for about 15%, while cases where two triad
members in high arousal could have brought the third member to that activation
level (i.e., 2:1) through arousal contagion represented almost 14%. The fact that the
majority of arousal contagion cases happened on a 1:1 basis suggests that, although
there are three students working (learning in this case) together, the interaction
seems to be mostly between two of the members, not necessarily always the same
two, but between two rather than among the three of them. Although a priori it
would seem more probable that two triad members in high arousal provoke the third
to be highly active, compared to one provoking the other two to reach a high arousal
state, the results showed that arousal contagion happened quite similarly on a 1:2
basis as on a 2:1 basis, the former being only 1% higher than the latter.
In terms of latency, cases of possible arousal contagion were found to occur
from within a few seconds up to close to 10 min. Most of the possible high arousal
contagion happened within one min, and from then on, the chances of contagion
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tended to decrease with time. Provided that arousal contagion occurred, its latency
seemed to be, therefore, relatively short. This might suggest that arousal contagion
is most likely to happen if the high arousal cues are made available early by the
triad member(s) at that activation level. Longer latency values (e.g., from 5 to
10 min) might be explained in several ways. On one hand, no visible cue might be
available to indicate that another triad member is at a high arousal level (Thorson
et al., 2018). On the other hand, the cue might be visible, but the appraisal could
differ at first (e.g., the to be influenced triad member does not see a need to become
active at that moment according to the situation or his/her own motivation to
participate), and the situation might be reappraised a few min later (e.g., due to a
persistent cue or a situation change; Koole, 2009).
The directionality of physiological influence (i.e., who influences whom) has
been used as indicator of group leadership (Chanel & Muhl, 2015). To that extent,
the triad members leading the contagion were examined. Evidence of all possible
profiles was found. Thus, in some cases, there was a predominant member of the
triad from whom the arousal contagion arises or two predominant members, while
in other cases, it is quite evenly originated from all three members. This result
aligns with the reported distinct dynamics of collaborative learning unfolding in
different triads (see Enyedy & Stevens, 2014; Miyake & Kirschner, 2014). In fact,
the importance of developing ways to study interaction quality over time is
underscored by reports that suggest large variations in the ways that group
interactions can unfold (Barron, 2000).
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7 Conclusions Collaborative skills have been largely taken for granted, but research shows that
they are not an automatic consequence of group work and that training is necessary
for effective collaborative groups (Cohen, 1994). Collaborative learning has been
shown to be ineffective and even counterproductive if it is not properly
implemented (M. Baker, 2015; P. A. Kirschner et al., 2018; Kuhn, 2015; Mullins et
al., 2011). Consequently, researchers on educational psychology, teachers, and
policy-makers, have long been interested in the study of collaborative learning and
the devising of metrics to assist in distinguishing effective from ineffective
collaboration, so that these results could be used in training. Wise and Schwarz
(2017) use the term collaborative learning analytics. Few instruments in
educational psychology capture joint action, and there is a need to provide accounts
of interactions that capture the dynamic interplay in collaborative learning (Barron,
2000). Although the extent to which the evaluation of learning can be based only
on group processes is debatable, some phenomena may emerge only in learners’
interactions, such as co-constructed knowledge or social skills (Vogel &
Weinberger, 2018).
Interactions are reflected in a variety of modalities, including verbal and non-
verbal (Enyedy & Stevens, 2014). In a dominantly descriptive design, studies of
collaborative learning have mainly involved conversation analyses and surveys,
including questionnaires (e.g., open-ended, multiple-choice, and Likert scales) and
interviews (Jeong et al., 2014). However, those analyses are time-consuming, very
expensive microgenetic methods, and do not scale up well (Wise & Schwarz, 2017),
which has led to the consideration of alternative data modalities to support the
characterization of interactions. One such modality is physiological data, which has
a tradition in the study of cognitive-affective processes in psychophysiological
research, although it is rarely employed in educational psychology. Physiological
data might assist in the instantaneous characterization of interactions according to
parameters, such as commonalities and influence among peers, and could help to
identify fluctuations in attention and coordination, features of interaction that are
not often described in research on collaborative learning processes (Barron, 2000).
One of the most popular and widely studied physiological signals in
psychophysiology is EDA, regarded as one of the simplest and most readily
accessible indicators of sympathetic arousal. In turn, sympathetic arousal is an
important psychological construct at the intersection of mind and body (Mandler,
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1984), connected to cognitive processes, especially attention (Raskin, 1973), and
to affective processes through the circumplex model of affect (Russell, 1980).
This dissertation was conducted in the light of the gaps identified in the
literature, the need for scalable ways to characterize interactions in collaborative
learning, the theoretical frameworks of collaborative learning research, EDA,
sympathetic arousal and interpersonal physiology, and the affordances of a
wristband biosensor for an unobtrusive, continuous recording of EDA in
collaborative learning settings. The overall aim was the exploration of EDA and
derivative measures, such as sympathetic arousal and PCIs, concomitant with
individual and interpersonal cognitive and affective processes, respectively, during
collaborative learning in a naturalistic setting.
At the individual level, students spent more than half of the class at a low
arousal level, possibly signaling disengagement. At the group level, triad members
were most of the time at a different arousal level, and when the levels coincided, it
was mainly at the low arousal level. The difference might indicate that students
took turns (alternating task-doers) in executing the task, or applied some division
of labor, rather than truly collaborating. In terms of achievement, two physiological
indices were found to relate to learning gains and exam grades. In the first data
collection, pairwise EDA-indexed directional agreement was positively and highly
correlated to dual learning gain. The more the EDA signals moved in the same
direction, the better students learned together. In the second data collection, arousal
during the exam, as indexed by the total count of SCRs (peaks in the EDA signal),
was a predictor of the students’ exam grades. The more active they were, the better
their achievement. Arousal is also known to impair performance when the level is
too high, for example, due to excessive stress or strong anxiety (Yerkes-Dodson
law), but those levels were not reached in our sample, probably because the students
were high achievers in the preceding level course of the same subject. In terms of
influence from a physiological perspective, 41% of the high arousal intervals that
were found could have caused or have been caused by arousal contagion, a
construct derived in this dissertation from the phenomenon of emotional contagion
and the classical consideration of arousal as the activation dimension of emotion.
Furthermore, the possible arousal contagion cases were found to take place mostly
(71.3%) on a 1:1 basis, indicating that interactions in the collaborative learning
triad seem to occur mostly between two of the three members, not necessarily
always the same two, but between two rather than among the three of them.
From the methodological point of view, two significant contributions were
made by this dissertation. First, the scheme for classifying sympathetic arousal
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levels into low, medium and high was obtained from the combination of different
literature sources and results. Second, and also based on results reported in the
literature, the criteria were established to disregard as noise those EDA recordings
where the number of peaks in the signal (SCRs) is lower than the minimum
expected theoretically.
A panoply of data sources and their corresponding methodological approaches
have been used in collaborative learning research. This dissertation has delved into
EDA, drawing from and building on the psychophysiological research tradition, at
the individual and interpersonal levels, and applied to the study of collaborative
learning. Importantly, the data were collected in ecologically valid settings, either
classrooms or classroom-like environments.
This work is part of the quest to advance learning research by enriching
traditional subjective data sources (e.g., questionnaires and interviews) with
objective online measures of learning processes (Azevedo, 2015; Boekaerts &
Corno, 2005; Winne, 2010). The current state of the art makes it imperative to focus
on the physiological signal at this stage as a first integration step, but it is my hope
that the studies comprising this dissertation will serve as the basis for including
physiological signals as a regular practice in data collection and experiment designs
in the learning sciences. As many other researchers in the field (e.g., Azevedo, 2015;
Boekaerts & Corno, 2005; Sanna Järvelä, Hadwin, Malmberg, & Miller, 2018;
Panadero, Klug, & Järvelä, 2015), I believe that triangulation of different data
modalities, including physiological data, has the potential to provide stronger
evidence and lead us as a community to draw sounder conclusions.
Several directions could be considered for future work. First, more
physiological signals can be explored as long as it makes theoretical sense. Second,
other indices could be developed or reused from the literature. It is important to
keep it simple for interpretability and practical usefulness. Previous research
reported that the simplest measures used were shown to be the most sensitive ones,
which has led to the claim that physiological indices, both at individual and
interpersonal levels, should be straightforward and uncomplicated (Elkins et al.,
2009), which extends to other measures as well. Third, the study of the combination
of indices from different signals and/or data modalities (e.g., physiological, self-
reported, and observation), since the eyes of science are on multimodal approaches,
should be pursued so that the limitations of individual modalities can be overcome,
leading to higher validity of the conclusions drawn. Physiological data, as opposed
to self-reports, is objective, but cannot be interpreted without knowledge of the
context. In this sense, qualitative data are needed to further characterize and
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understand the variations in PCIs and sympathetic arousal at individual and
interpersonal levels, in terms of, for example, why a triad reaches full directional
agreement in one lesson but not in another, and what situations cause triad members
to sustain full arousal directional agreement during several min. Fourth, although
here a general profile of the class and relations of the physiological measures to
academic achievement were pursued, the methodology can be applied to the study
of specific events of interest (e.g., directional agreement or arousal levels within a
response window).
The physiological indices used in this dissertation could be fed back to the
students via a learning analytics dashboard to support their reflection upon and
awareness of the learning process. Both retrospective and real-time applications
(e.g., alarms when certain thresholds are surpassed) are possible. It is known that
individuals vary in their ability to perceive their autonomic physiological state,
known as interoceptive awareness, which they need to inform cognitions and
behaviors (Critchley et al., 2013). For example, people rely partly on information
from their physiological state in judging their capabilities (Bandura, 1982). At the
collaboration level, it has been argued that groups should become aware of their
interpersonal processes and take time to discuss how they are doing as a group
(Cohen, 1994). For both individual and interpersonal level purposes, the
incorporation of physiological measures would enrich and provide a new
dimension to learning analytics dashboards, which to date largely focus on log data
(Schwendimann et al., 2017). From the teacher’s perspective, such a dashboard
could assist in group composition (Ahonen et al., 2018) and identify those activities
which cause more students to be active and engaged. Similarly, it could help in
distinguishing those tasks leading to actual group work rather than to alternating
task doers or division of labor. In sum, the findings of this study have applications
to learning dashboards, prompts (e.g., in intelligent tutoring systems or online
learning environments), and in general, to the development of tools for better
collaboration.
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Original publications I Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating
collaborative learning success with physiological coupling indices based on electrodermal activity. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 64–73). New York, NY: ACM. https://doi.org/10.1145/2883851.2883897
II Pijeira-Díaz, H. J., Drachsler, H., Kirschner, P. A., & Järvelä, S. (2018). Profiling sympathetic arousal in a physics course: How active are students? Journal of Computer Assisted Learning, 34(4), 397–408. https://doi.org/10.1111/jcal.12271
III Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2019). Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members? Computers in Human Behavior, 92(March), 188–197. https://doi.org/10.1016/j.chb.2018.11.008
Copyright of Article I held by the author. Article II reprinted with permission from
John Wiley and Sons. Permission is not required from the publisher of Article III
(i.e., Elsevier), as the author retains the right to include the article in a thesis or
dissertation, provided it is not published commercially.
Original publications are not included in the electronic version of the dissertation.
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181. Välimäki, Anna-Leena (2018) Kun työ tulee hymyillen kotiin : suomalaisenperhepäivähoidon 50 vuoden tarina 1960-luvulta 2010-luvulle
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UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND
A C T A U N I V E R S I T A T I S O U L U E N S I S
University Lecturer Tuomo Glumoff
University Lecturer Santeri Palviainen
Senior research fellow Jari Juuti
Professor Olli Vuolteenaho
University Lecturer Veli-Matti Ulvinen
Planning Director Pertti Tikkanen
Professor Jari Juga
University Lecturer Anu Soikkeli
Professor Olli Vuolteenaho
Publications Editor Kirsti Nurkkala
ISBN 978-952-62-2218-9 (Paperback)ISBN 978-952-62-2219-6 (PDF)ISSN 0355-323X (Print)ISSN 1796-2242 (Online)
U N I V E R S I TAT I S O U L U E N S I SACTAE
SCIENTIAE RERUM SOCIALIUM
E 184
AC
TAH
éctor Javier Pijeira D
íaz
OULU 2019
E 184
Héctor Javier Pijeira Díaz
ELECTRODERMAL ACTIVITY AND SYMPATHETIC AROUSAL DURING COLLABORATIVE LEARNING
UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF EDUCATION