Exploring Student-Controlled Social Comparison

Previous research on technology-enhanced learning indicated that exposing students to information related to their peers’ performance might positively or negatively affect their behavior and performance. For example, recent research has demonstrated that augmenting traditional open learner models (OLMs) with views of the learner model of peers could boost student engagement and affect navigational patterns. On the other hand, the negative impact of social comparison has been also reported in the literature, which demonstrates that a comparison with much better-performing peers presents a threat to self-integrity. These conflicting findings have not yet been reconciled in the context of technology-enhanced learning. This work attempts to extend research in social comparison in an educational context and on OLMs by examining how the potential negative and positive sides of social comparison could be balanced by enabling students to select their peer comparison group, rather than by being forced to compare themselves with an aggregated class average .

[1]  Peter Brusilovsky,et al.  Social Adaptive Navigation Support for Open Corpus Electronic Textbooks , 2004, AH.

[2]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[3]  Peter Brusilovsky,et al.  An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement , 2016, IUI.

[4]  Marie-Pierre Fayant,et al.  On Being Exposed to Superior Others: Consequences of Self-Threatening Upward Social Comparisons , 2010 .

[5]  Jerry Suls,et al.  Social Comparison: Why, With Whom, and With What Effect? , 2002 .

[6]  B. Zimmerman,et al.  The Role of Observation and Emulation in the Development of Athletic Self-Regulation. , 2000 .

[7]  Birgitta König-Ries,et al.  Progressor: social navigation support through open social student modeling , 2013, New Rev. Hypermedia Multim..

[8]  Lei Shi,et al.  Learners Thrive Using Multifaceted Open Social Learner Modeling , 2016, IEEE MultiMedia.

[9]  Bart Rienties,et al.  Linking students' timing of engagement to learning design and academic performance , 2018, LAK.

[10]  Lauri Malmi,et al.  An integrated practice system for learning programming in Python: design and evaluation , 2018, Research and Practice in Technology Enhanced Learning.

[11]  Per B. Brockhoff,et al.  lmerTest Package: Tests in Linear Mixed Effects Models , 2017 .

[12]  L. Wheeler,et al.  The Proxy Model of Social Comparison for Self-Assessment of Ability , 1997, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[13]  Geert-Jan Houben,et al.  Follow the successful crowd: raising MOOC completion rates through social comparison at scale , 2017, LAK.

[14]  Peter Brusilovsky,et al.  Mastery Grids: An Open Source Social Educational Progress Visualization , 2014, EC-TEL.

[15]  Jonathan E. Butner,et al.  Compliance with a Request in Two Cultures: The Differential Influence of Social Proof and Commitment/Consistency on Collectivists and Individualists , 1999 .

[16]  Avi Feller,et al.  Discouraged by Peer Excellence , 2016, Psychological science.

[17]  Abraham P. Buunk,et al.  Social Comparison in the Classroom: A Review , 2008 .

[18]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[19]  B. Buunk,et al.  Individual differences in social comparison: development of a scale of social comparison orientation. , 1999, Journal of personality and social psychology.

[20]  Pascal Huguet,et al.  Social comparison choices in the classroom: further evidence for students' upward comparison tendency and its beneficial impact on performance , 2001 .

[21]  Lauri Malmi,et al.  Improving Engagement in Program Construction Examples for Learning Python Programming , 2020, International Journal of Artificial Intelligence in Education.

[22]  Paula J. Durlach,et al.  Open Social Student Modeling for Personalized Learning , 2016, IEEE Transactions on Emerging Topics in Computing.