research-article

Analyzing the consistency in within-activity learning patterns in blended learning

Authors:

Dragan GaševićAuthors Info & Claims

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

Pages 1 - 10

https://doi.org/10.1145/3375462.3375470

Published: 23 March 2020 Publication History

Abstract

Performance and consistency play a large role in learning. This study analyzes the relation between consistency in students' online work habits and academic performance in a blended course. We utilize the data from logs recorded by a learning management system (LMS) in two information technology courses. The two courses required the completion of monthly asynchronous online discussion tasks and weekly assignments, respectively. We measure consistency by using Data Time Warping (DTW) distance for two successive tasks (assignments or discussions), as an appropriate measure to assess similarity of time series, over 11-day timeline starting 10 days before and up to the submission deadline. We found meaningful clusters of students exhibiting similar behavior and we use these to identify three distinct consistency patterns: highly consistent, incrementally consistent, and inconsistent users. We also found evidence of significant associations between these patterns and learner's academic performance.

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Cited By

Alghamdi SRaković MYang KFan YGašević DChen G(2025)Analytics of Temporal Patterns of Self-regulated Learners: A Time Series ApproachProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706504(283-292)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706504
Getman ABoitcov MAdamovich KCostley J(2024)The role of engagement strategies and path-dependency in online learningInnovations in Education and Teaching International10.1080/14703297.2024.2413440(1-15)Online publication date: 14-Oct-2024
https://doi.org/10.1080/14703297.2024.2413440
Fenu GGalici RMarras M(2024)Data-Efficient Student Profiling in Online CoursesArtificial Intelligence with and for Learning Sciences. Past, Present, and Future Horizons10.1007/978-3-031-57402-3_2(11-20)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-57402-3_2
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Analyzing the consistency in within-activity learning patterns in blended 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

Copyright © 2020 ACM.

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Publication History

Published: 23 March 2020

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Social Sciences and Humanities Research Council of Canada

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LAK '20

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%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Alghamdi SRaković MYang KFan YGašević DChen G(2025)Analytics of Temporal Patterns of Self-regulated Learners: A Time Series ApproachProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706504(283-292)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706504
Getman ABoitcov MAdamovich KCostley J(2024)The role of engagement strategies and path-dependency in online learningInnovations in Education and Teaching International10.1080/14703297.2024.2413440(1-15)Online publication date: 14-Oct-2024
https://doi.org/10.1080/14703297.2024.2413440
Fenu GGalici RMarras M(2024)Data-Efficient Student Profiling in Online CoursesArtificial Intelligence with and for Learning Sciences. Past, Present, and Future Horizons10.1007/978-3-031-57402-3_2(11-20)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-57402-3_2
Galici RKaser TFenu GMarras MHilliger IKhosravi HRienties BDawson S(2023)Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based LearningLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576148(441-452)Online publication date: 13-Mar-2023
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Galici RKäser TFenu GMarras M(2023)How Close are Predictive Models to Teachers in Detecting Learners at Risk?Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3595620(135-145)Online publication date: 18-Jun-2023
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Vyas SDwivedi SBrenner LPedron IGabbard JKrishnamurthy VMehta R(2023)Adaptive Training on Basic AR Interactions: Bi-Variate Metrics and Neuroergonomic Evaluation ParadigmsInternational Journal of Human–Computer Interaction10.1080/10447318.2023.225057840:20(6252-6267)Online publication date: Sep-2023
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Fenu GGalici RMarras MPicciau S(2023)Supporting Instructors with Course Attendance and Quality Prediction in Synchronous LearningHigher Education Learning Methodologies and Technologies Online10.1007/978-3-031-29800-4_6(71-83)Online publication date: 1-May-2023
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YeckehZaare IAronoff CGrot GMerkle LDoyle MSheard JSoh LDorn B(2022)Retrieval-based Teaching Incentivizes Spacing and Improves Grades in Computer Science EducationProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499408(892-898)Online publication date: 22-Feb-2022
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