research-article

Discovery and temporal analysis of latent study patterns in MOOC interaction sequences

Authors:

Mina Shirvani Boroujeni and

Pierre DillenbourgAuthors Info & Claims

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

March 2018

Pages 206 - 215

https://doi.org/10.1145/3170358.3170388

Published: 07 March 2018 Publication History

Abstract

Capturing students' behavioral patterns through analysis of sequential interaction logs is an important task in educational data mining and could enable more effective and personalized support during the learning processes. This study aims at discovery and temporal analysis of learners' study patterns in MOOC assessment periods. We propose two different methods to achieve this goal. First, following a hypothesis-driven approach, we identify learners' study patterns based on their interaction with lectures and assignments. Through clustering of study pattern sequences, we capture different longitudinal activity profiles among learners and describe their properties. Second, we propose a temporal clustering pipeline for unsupervised discovery of latent patterns in learners' interaction data. We model and cluster activity sequences at each time step and perform cluster matching to enable tracking learning behaviours over time. Our proposed pipeline is general and applicable in different learning environments such as MOOC and ITS. Moreover, it allows for modeling and temporal analysis of interaction data at different levels of actions granularity and time resolution. We demonstrate the application of this method for detecting latent study patterns in a MOOC course.

References

[1]

R. Agrawal and R. Srikant. Mining sequential patterns. In Data Engineering, 1995. Proceedings of the Eleventh International Conference on, pages 3--14. IEEE, 1995.

Digital Library

[2]

Y. Bergner, Z. Shu, and A. von Davier. Visualization and confirmatory clustering of sequence data from a simulation-based assessment task. In Educational Data Mining 2014, 2014.

[3]

P. Bródka, S. Saganowski, and P. Kazienko. Ged: the method for group evolution discovery in social networks. Social Network Analysis and Mining, 3(1):1--14, 2013.

[4]

T. Caliński and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1--27, 1974.

[5]

M. Desmarais and F. Lemieux. Clustering and visualizing study state sequences. In Educational Data Mining 2013, 2013.

[6]

L. Faucon, L. Kidzinski, and P. Dillenbourg. Semi-markov model for simulating mooc students. In Proceedings of the 9th International Conference on Educational Data Mining, 2016.

[7]

C. Geigle and C. Zhai. Modeling mooc student behavior with two-layer hidden markov models. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pages 205--208. ACM, 2017.

Digital Library

[8]

D. Greene, D. Doyle, and P. Cunningham. Tracking the evolution of communities in dynamic social networks. In Advances in social networks analysis and mining (ASONAM), 2010 international conference on, pages 176--183. IEEE, 2010.

Digital Library

[9]

C. Hansen, C. Hansen, N. Hjuler, S. Alstrup, and C. Lioma. Sequence modelling for analysing student interaction with educational systems. arXiv preprint arXiv:1708.04164, 2017.

[10]

H. Jeong and G. Biswas. Mining student behavior models in learning-by-teaching environments. In Educational Data Mining 2008, pages 127--136, 2008.

[11]

J. S. Kinnebrew, K. M. Loretz, and G. Biswas. A contextualized, differential sequence mining method to derive students' learning behavior patterns. JEDM-Journal of Educational Data Mining, 5(1):190--219, 2013.

[12]

R. F. Kizilcec, C. Piech, and E. Schneider. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge, pages 170--179. ACM, 2013.

Digital Library

[13]

S. Klingler, T. Käser, B. Solenthaler, and M. H. Gross. Temporally coherent clustering of student data. In Proceedings of the 9th International Conference on Educational Data Mining, pages 102--109, 2016.

[14]

M. Köck and A. Paramythis. Activity sequence modelling and dynamic clustering for personalized e-learning. User Modeling and User-Adapted Interaction, 21(1):51--97, 2011.

Digital Library

[15]

C. Li and G. Biswas. A bayesian approach to temporal data clustering using hidden markov models. In ICML, pages 543--550, 2000.

Digital Library

[16]

J. Lin. Divergence measures based on the shannon entropy. Information Theory, IEEE Transactions on, 37(1):145--151, 1991.

Digital Library

[17]

R. M. Maldonado, K. Yacef, J. Kay, A. Kharrufa, and A. Al-Qaraghuli. Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. In Educational Data Mining 2011, 2010.

[18]

P. Mukala, J. Buijs, and W. Van Der Aalst. Exploring students' learning behaviour in moocs using process mining techniques, 2015.

[19]

F. Murtagh and P. Legendre. Ward's hierarchical agglomerative clustering method: which algorithms implement ward's criterion? Journal of Classification, 31(3):274--295, 2014.

Digital Library

[20]

J. C. Nesbit, M. Zhou, Y. Xu, and P. Winne. Advancing log analysis of student interactions with cognitive tools. In 12th biennial conference of the european association for research on learning and insruction (EARLI), 2007.

[21]

N. Patel, C. Sellman, and D. Lomas. Mining frequent learning pathways from a large educational dataset. arXiv preprint arXiv:1705.11125, 2017.

[22]

P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53--65, 1987.

Digital Library

[23]

S. Shen and M. Chi. Clustering student sequential trajectories using dynamic time warping. In Proceedings of the 10th International Conference on Educational Data Mining, pages 266--271, 2017.

[24]

B. Shih, K. R. Koedinger, and R. Scheines. Unsupervised discovery of student strategies. In Educational Data Mining 2010, pages 201--210, 2010.

[25]

M. Shirvani Boroujeni, T. Hecking, H. U. Hoppe, and P. Dillenbourg. Dynamics of mooc discussion forums. In Proceedings of the 7th International Conference on Learning Analytics & Knowledge, LAK'17, pages 128--137, 2017.

Digital Library

[26]

M. Shirvani Boroujeni, Ł. Kidzinski, and P. Dillenbourg. How employment constrains participation in moocs. In Proceedings of the 9th International Conference on Educational Data Mining, pages 376--377, 2016.

[27]

M. Shirvani Boroujeni, K. Sharma, Ł. Kidziński, L. Lucignano, and P. Dillenbourg. How to quantify student's regularity? In European Conference on Technology Enhanced Learning, pages 277--291. Springer, 2016.

[28]

N. Trcka, M. Pechenizkiy, and W. van der Aalst. Process mining from educational data. Chapman & Hall/CRC, 2010.

Cited By

Pan ZLi CZou WLiu M(2023)Applying Learning Analytics Approaches to Detect and Track Students' Cognitive States During Virtual Problem-Solving ActivitiesPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch002(15-43)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch002
Lang QZhang CQi HDu YZhu XZhang CLi M(2023)Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke CorrectionSustainability10.3390/su1503239315:3(2393)Online publication date: 28-Jan-2023
https://doi.org/10.3390/su15032393
Morris WCrossley SHolmes LTrumbore AHilliger IKhosravi HRienties BDawson S(2023)Using Transformer Language Models to Validate Peer-Assigned Essay Scores in Massive Open Online Courses (MOOCs)LAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576098(315-323)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576098
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Index Terms

Discovery and temporal analysis of latent study patterns in MOOC interaction sequences
1. Applied computing
  1. Education
    1. Distance learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

March 2018

489 pages

ISBN:9781450364003

DOI:10.1145/3170358

Conference Chairs:
Abelardo Pardo
The University of Sydney, AUS
,
Kathryn Bartimote-Aufflick
The University of Sydney, AUS
,
Grace Lynch
Society for Learning Analytics Research, AUS
,
Program Chairs:
Simon Buckingham Shum
University of Technology Sydney, AUS
,
Rebecca Ferguson
The Open University, UK
,
Agathe Merceron
Beuth University of Applied Sciences, GER
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, EC

Copyright © 2018 ACM.

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Association for Computing Machinery

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Published: 07 March 2018

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Swiss State Secretariat for Education, Research and Innovation (SERI)

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LAK '18

LAK '18: International Conference on Learning Analytics and Knowledge

March 7 - 9, 2018

New South Wales, Sydney, Australia

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LAK '18 Paper Acceptance Rate 35 of 115 submissions, 30%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

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https://doi.org/10.4018/978-1-6684-9527-8.ch002
Lang QZhang CQi HDu YZhu XZhang CLi M(2023)Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke CorrectionSustainability10.3390/su1503239315:3(2393)Online publication date: 28-Jan-2023
https://doi.org/10.3390/su15032393
Morris WCrossley SHolmes LTrumbore AHilliger IKhosravi HRienties BDawson S(2023)Using Transformer Language Models to Validate Peer-Assigned Essay Scores in Massive Open Online Courses (MOOCs)LAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576098(315-323)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576098
Yu JDing MWang QSun WHu W(2023)Community Sports Organization Development From a Social Network Evolution Perspective— Structures, Stages, and StimulusIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.313580910:3(878-889)Online publication date: Jun-2023
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Ren JWu S(2023)Prediction of User Temporal Interactions with Online Course Platforms Using Deep Learning AlgorithmsComputers and Education: Artificial Intelligence10.1016/j.caeai.2023.100133(100133)Online publication date: Mar-2023
https://doi.org/10.1016/j.caeai.2023.100133
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
Khafaei TTavakoli Taraghi AHosseinzadeh MRezaee A(2022)The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social NetworksSocial Science Computer Review10.1177/0894439321105581341:4(1187-1206)Online publication date: 27-Feb-2022
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Takami KDai YFlanagan BOgata HWise AMartinez-Maldonado RHilliger I(2022)Educational Explainable Recommender Usage and its Effectiveness in High School Summer Vacation AssignmentLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506882(458-464)Online publication date: 21-Mar-2022
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Park EIfenthaler DClariana R(2022)Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners' self‐regulated learning in an adaptive learning analytics dashboardBritish Journal of Educational Technology10.1111/bjet.1328754:1(98-125)Online publication date: 15-Nov-2022
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Torres DPulukuri SAbrams B(2022)Embedded Questions and Targeted Feedback Transform Passive Educational Videos into Effective Active Learning ToolsJournal of Chemical Education10.1021/acs.jchemed.2c0034299:7(2738-2742)Online publication date: 22-Jun-2022
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