Article

Modeling student online learning using clustering

Authors:

Jungsoon YooAuthors Info & Claims

ACMSE '06: Proceedings of the 44th annual ACM Southeast Conference

Pages 186 - 191

https://doi.org/10.1145/1185448.1185490

Published: 10 March 2006 Publication History

Abstract

This paper discusses and evaluates a modeling approach for student online learning. It is developed as a key component of an adaptive online tutoring system, AToL. At the beginning of the learning process, classification of student learning style is applied based on each student's responses to a few learning related questions. Clustering is then used to model student behavior for each learning style using data collected as the students interact with the system. A Bayesian Markov chain based temporal data clustering method is developed for this step. We evaluated the student modeling component of the AToL system using data collected from the CS-I students who participated in the experiments in Spring 05. We compared the quality of the models built using these two approaches. We also compared the models built for the same group of students when learning different concepts.

References

[1]

Cadez, I., Heckerman, D., and Meek, C., Smyth, P., and White, S., Visualization of Navigation Patterns on a Web Site using Model Based Clustering Data Mining and Knowledge Discovery, 7(4), 399--424, 2003.

Digital Library

[2]

Chib, S., Marginal Likelihood from the Gibbs Sampling. Journal of the American Statistical Association, 1313--1321, December 1995.

[3]

Chudova, D., Smyth, P., Sequences and Strings: Pattern Discovery in Sequences under a Markov Assumption, in Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Digital Library

[4]

Fisher, D., Data Mining Tasks and Methods: Clustering: Conceptual Clustering, Handbook of Data Mining and Knowledge Discovery, 388--396, 2002.

Digital Library

[5]

Heckerman, D., Geiger, D., and Chickering, D. M., A Tutorial on Learning with Bayesian Networks, Machine Learning, 20:197--243, 1995.

Digital Library

[6]

Huhtala, Y., Karkkainen, J., and Toivonen, H. and Nevanlinna R., Mining for Similarity in Aligned Time Series using Wavelets, in Proceedings of SPIE on Data Mining and Knowledge Discovery: Theory, Tools, and Technology, edited by B. V. Dasarathy, 142--149, 1999.

[7]

Jain, A. K., Murty, M. N., and Flynn, P. J., Data Clustering: A Review, ACM Computing Surveys (CSUR), 31(3), 264--323, September 1999.

Digital Library

[8]

Jain, A. K. and Dubes, D. C., Algorithms for Clustering Data, Prentice Hall, 1988.

Digital Library

[9]

Keogh, E., and Kasetty, S., On the need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration, Data Mining and Knowledge Discovery, 7(4), 349--371, 2003.

Digital Library

[10]

Kolb, D. A. Experiential Learning: experience as the source of learning and development, New Jersey: Prentice-Hall, 1984.

[11]

Li, C., Biswas, G., Dale, M., and Dale, P. Matryoshka: A Hidden Markov Model Based Temporal Data Clustering Methodology for Modeling System Dynamics, Intelligent Data Analysis, 6(3), 281--308, 2002.

[12]

Li, C., A Bayesian Approach to Temporal Data Clustering using the Hidden Markov Model Methodology, PhD thesis, Vanderbilt University, December 2000.

Digital Library

[13]

Li, C. and Yoo, J. P., A Study of The Effects of Bias in Criterion Functions for Temporal Data Clustering, in Proceedings of the ACMSE 2005, the 43rd Annual ACM Southeast Conference, March 18-20, 2005, Kennesaw State University, Kennesaw, Georgia. 85--89.

Digital Library

[14]

Oates, T., Firoiu, L., and Cohen, P. R., Clustering Time Series with Hidden Markov Models and Dynamic Time Warping, in the Proceedings of the IJCAI-99 Workshop on Robot Action Planning, 1999.

[15]

Quinlan, J. R. C4.5: Program for Machine Learning, San Francisco, CA: Morgan Kauffmann, 1993.

Digital Library

[16]

Rabiner, L. R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, 77(2), 257--285, 1989.

Digital Library

[17]

Ramoni, M., Sebastiani, P., and Cohen, P., Bayesian Clustering by Dynamics, Machine Learning 47 (1), 91--121, Aril 2002

Digital Library

[18]

Schwarz, G., Estimating the dimension of a model, Annuals of Statistics, 6, 461--464, 1978.

[19]

Stevens, P., Soller, A., Cooper, M., and Sprang, M. Modeling the Development of Problem-Solving Skills in Chemistry with a Web-Based Tutor. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems, MaceiO-Alagoas -- Brasil, 2004.

[20]

Yoo, J. P., Li, C., and Pettey, C., Adaptive Teaching Strategy for Online Learning, to appear in Proceedings of the International Conference on Intelligent User Interface (IUI2005), San Diego, CA, 2005. 266--268.

Digital Library

Cited By

Yuan WZhao SWang LCai LZhang Y(2024)Online course evaluation model based on graph auto-encoderIntelligent Data Analysis10.3233/IDA-23055728:6(1467-1489)Online publication date: 15-Nov-2024
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Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Yang YHooshyar DPedaste MWang MHuang YLim H(2020)Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learningJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02041-8Online publication date: 14-May-2020
https://doi.org/10.1007/s12652-020-02041-8
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cover image ACM Other conferences

ACMSE '06: Proceedings of the 44th annual ACM Southeast Conference

March 2006

823 pages

ISBN:1595933158

DOI:10.1145/1185448

General Chair:
Ronaldo Menezes
Florida Institute of Technology

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 10 March 2006

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ACM SE06

ACM SE06: ACM Southeast Regional Conference

March 10 - 12, 2006

Florida, Melbourne

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ACMSE '06 Paper Acceptance Rate 100 of 244 submissions, 41%;

Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

Yuan WZhao SWang LCai LZhang Y(2024)Online course evaluation model based on graph auto-encoderIntelligent Data Analysis10.3233/IDA-23055728:6(1467-1489)Online publication date: 15-Nov-2024
https://doi.org/10.3233/IDA-230557
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Yang YHooshyar DPedaste MWang MHuang YLim H(2020)Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learningJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02041-8Online publication date: 14-May-2020
https://doi.org/10.1007/s12652-020-02041-8
Yang YHooshyar DPedaste MWang MHuang YLim H(2020)Predicting course achievement of university students based on their procrastination behaviour on MoodleSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05110-424:24(18777-18793)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00500-020-05110-4
Hooshyar DPedaste MYang Y(2019)Mining Educational Data to Predict Students’ Performance through Procrastination BehaviorEntropy10.3390/e2201001222:1(12)Online publication date: 20-Dec-2019
https://doi.org/10.3390/e22010012
Cena FLikavec SRapp A(2019)Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous ComputingInformation Systems Frontiers10.1007/s10796-017-9818-321:5(1085-1110)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10796-017-9818-3
Dutt AIsmail MHerawan T(2017)A Systematic Review on Educational Data MiningIEEE Access10.1109/ACCESS.2017.26542475(15991-16005)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2654247
Alawi SShaharanee IJamil J(2017)Profiling Oman education data using data mining approach10.1063/1.5005467(020134)Online publication date: 2017
https://doi.org/10.1063/1.5005467
Vo TNguyen HVo T(2016)Making kernel-based vector quantization robust and effective for incomplete educational data clusteringVietnam Journal of Computer Science10.1007/s40595-016-0060-63:2(93-102)Online publication date: 1-May-2016
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Mihăescu MTănasie ADascalu MTrausan-Matu S(2016)Extracting Patterns from Educational Traces via Clustering and Associated Quality MetricsArtificial Intelligence: Methodology, Systems, and Applications10.1007/978-3-319-44748-3_11(109-118)Online publication date: 18-Aug-2016
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