research-article

Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses

Authors:

Peter Brusilovsky,

Michael Yudelson,

Arto HellasAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 76 - 84

https://doi.org/10.1145/3079628.3079672

Published: 09 July 2017 Publication History

Abstract

Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task -- predicting student problem-solving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research.

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Cited By

Kong BHemberg EBell AO'Reilly UHilliger IKhosravi HRienties BDawson S(2023)Investigating Student's Problem-solving Approaches in MOOCs using Natural Language ProcessingLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576091(262-272)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576091
Strousopoulos PTroussas CKrouska AMylonas PSgouropoulou C(2023)Integrating Stereotype User Models for Adaptive Scenarios in Game Playing within Immersive Virtual Environments2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023)10.1109/SMAP59435.2023.10255160(1-6)Online publication date: 25-Sep-2023
https://doi.org/10.1109/SMAP59435.2023.10255160
Malmi LHellas AIhantola PIsomöttönen VJormanainen IKilamo TKnutas AKorhonen ALaakso MLópez-Pernas SPoranen TSalakoski TSuhonen J(2023)Computing Education Research in FinlandPast, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_16(335-372)Online publication date: 5-Jan-2023
https://doi.org/10.1007/978-3-031-25336-2_16
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Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses

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Published In

cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Copyright © 2017 ACM.

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Published: 09 July 2017

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Kong BHemberg EBell AO'Reilly UHilliger IKhosravi HRienties BDawson S(2023)Investigating Student's Problem-solving Approaches in MOOCs using Natural Language ProcessingLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576091(262-272)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576091
Strousopoulos PTroussas CKrouska AMylonas PSgouropoulou C(2023)Integrating Stereotype User Models for Adaptive Scenarios in Game Playing within Immersive Virtual Environments2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023)10.1109/SMAP59435.2023.10255160(1-6)Online publication date: 25-Sep-2023
https://doi.org/10.1109/SMAP59435.2023.10255160
Malmi LHellas AIhantola PIsomöttönen VJormanainen IKilamo TKnutas AKorhonen ALaakso MLópez-Pernas SPoranen TSalakoski TSuhonen J(2023)Computing Education Research in FinlandPast, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_16(335-372)Online publication date: 5-Jan-2023
https://doi.org/10.1007/978-3-031-25336-2_16
Akhuseyinoglu KBrusilovsky P(2022)Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual DifferencesFrontiers in Artificial Intelligence10.3389/frai.2022.8073205Online publication date: 15-Feb-2022
https://doi.org/10.3389/frai.2022.807320
Trakunphutthirak RLee V(2021)Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal DataJournal of Educational Computing Research10.1177/0735633121104877760:3(742-776)Online publication date: 27-Sep-2021
https://doi.org/10.1177/07356331211048777
Lorås MSindre GTrætteberg HAalberg T(2021)Study Behavior in Computing Education—A Systematic Literature ReviewACM Transactions on Computing Education10.1145/346912922:1(1-40)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3469129
Akhuseyinoglu KBrusilovsky P(2021)Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online LearningProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456834(201-212)Online publication date: 21-Jun-2021
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Morshed Fahid FTian XEmerson AB. Wiggins JBounajim DSmith AWiebe EMott BElizabeth Boyer KLester J(2021)Progression Trajectory-Based Student Modeling for Novice Block-Based ProgrammingProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456833(189-200)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456833
Lei PMendes A(2021)A systematic literature review on knowledge tracing in learning programming2021 IEEE Frontiers in Education Conference (FIE)10.1109/FIE49875.2021.9637323(1-7)Online publication date: 13-Oct-2021
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Troussas CChrysafiadi KVirvou M(2021)Personalized tutoring through a stereotype student model incorporating a hybrid learning style instrumentEducation and Information Technologies10.1007/s10639-020-10366-226:2(2295-2307)Online publication date: 1-Mar-2021
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