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Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Authors:

Kshitij Sharma,

Zacharoula Papamitsiou,

Jennifer K. Olsen,

Michail GiannakosAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 480 - 489

https://doi.org/10.1145/3375462.3375498

Published: 23 March 2020 Publication History

Abstract

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in.

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Cited By

Fu WZhang JZhang DLi TLan MLiu N(2025)An Empirical Study of Adaptive Feedback to Enhance Cognitive Ability in Programming Learning among College Students: A Perspective Based on Multimodal Data AnalysisJournal of Educational Computing Research10.1177/07356331241313126Online publication date: 4-Jan-2025
https://doi.org/10.1177/07356331241313126
Amat APlunk AAdiani DWilkes DSarkar N(2024)Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual EnvironmentsSignals10.3390/signals50200195:2(382-401)Online publication date: 3-Jun-2024
https://doi.org/10.3390/signals5020019
Li FZhang HChen BZhou QLi X(2024)Visualization of Hotspots and Frontiers in Learning Analytics under Big Data Environment — Based on Citespace Knowledge Map AnalysisProceedings of the 2024 8th International Conference on Digital Technology in Education (ICDTE)10.1145/3696230.3696267(221-231)Online publication date: 7-Aug-2024
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Index Terms

Predicting learners' effortful behaviour in adaptive assessment using multimodal data
1. Applied computing
  1. Education
    1. E-learning

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cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

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Published: 23 March 2020

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LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

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Fu WZhang JZhang DLi TLan MLiu N(2025)An Empirical Study of Adaptive Feedback to Enhance Cognitive Ability in Programming Learning among College Students: A Perspective Based on Multimodal Data AnalysisJournal of Educational Computing Research10.1177/07356331241313126Online publication date: 4-Jan-2025
https://doi.org/10.1177/07356331241313126
Amat APlunk AAdiani DWilkes DSarkar N(2024)Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual EnvironmentsSignals10.3390/signals50200195:2(382-401)Online publication date: 3-Jun-2024
https://doi.org/10.3390/signals5020019
Li FZhang HChen BZhou QLi X(2024)Visualization of Hotspots and Frontiers in Learning Analytics under Big Data Environment — Based on Citespace Knowledge Map AnalysisProceedings of the 2024 8th International Conference on Digital Technology in Education (ICDTE)10.1145/3696230.3696267(221-231)Online publication date: 7-Aug-2024
https://dl.acm.org/doi/10.1145/3696230.3696267
Lang COstrow K(2024)Challenge variance: Exploiting format differences for personalized learner modelsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659542(102-109)Online publication date: 22-Jun-2024
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Sembey RHoda RGrundy J(2024)Emerging technologies in higher education assessment and feedback practices: A systematic literature reviewJournal of Systems and Software10.1016/j.jss.2024.111988211(111988)Online publication date: May-2024
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Giannakos M(2024)Theory and Intermediate-Level Knowledge in Multimodal Learning AnalyticsTheory Informing and Arising from Learning Analytics10.1007/978-3-031-60571-0_6(87-104)Online publication date: 29-Dec-2024
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