Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
Source abstract
Abstract
The log files of Massive Open Online Courses (MOOCs) reveal useful information that can help interpret student behavior. In this study, we focus on student performance based on their access to course resources and the grades they achieve. We define states as the Moodle resources and quiz grades for each student ID, considering participation in resources such as wikis and forums. We use efficient Hidden Markov Models to interpret the abundance of information provided in the Moodle log files. The transitions among certain resources for each student or groups of students are determined as behaviors. Other studies employ Machine Learning and Pattern Classification algorithms to recognize these behaviors. As an example, we visualize these transitions for individual learners. Additionally, we have created row and column charts to present our findings in a comprehensible manner. For implementing the proposed methodology, we use the R programming language. The dataset that we use was obtained from Kaggle and pertains to a MOOC of 4037 students.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
References (42)
Show source-provided references
1. The Adoption of E-Learning beyond MOOCs for Higher Education10.1108/IJAIM-08-2020-0129 ↗
2. Analyzing Learners Behavior and Resources Effectiveness in a Distance Learning Course: A Case Study of the Hellenic Open University
3. Learning Analytics: The Emergence of a Discipline10.1177/0002764213498851 ↗
4. Toward a Model for Acceptance of MOOCs in Higher Education: The Modified UTAUT Model for Saudi Arabia10.1007/s10639-020-10317-x ↗
5. Dospinescu, N.; Dospinescu, O. How the Support That Students Receive during Online Learning Influences Their Academic Performance10.1007/s10639-024-12639-6 ↗
6. Using Sequential Pattern Mining to Understand How Students Use Guidance While Doing Scientific Calculations10.1007/s10758-023-09677-3 ↗
7. Faucon, L., Kidzinski, L., and Dillenbourg, P. (2016–2, January 29). Semi-Markov Model for Simulating MOOC Students. Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC, USA.
8. Davis, D., Chen, G., Hauff, C., and Houben, G. (2016–2, January 29). Gauging MOOC Learners’ Adherence to the Designed Learning Path. Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC, USA.
9. Jeong, H., and Biswas, G. (2008, January 20–21). Mining Student Behavior Models in Learning-by-Teaching Environments. Proceedings of the Educational Data Mining 2008, Montreal, QC, Canada.
10. Kock, M., and Paramythis, A. (2010, January 24–26). Towards Adaptive Learning Support on the Basis of Behavioural Patterns in Learning Activity Sequences. Proceedings of the 2010 International Conference on Intelligent Networking and Collaborative Systems, Thessaloniki, Greece.10.1109/INCOS.2010.76 ↗
11. Klemke, R., Eradze, M., and Antonaci, A. (2018). The Flipped MOOC: Using Gamification and Learning Analytics in MOOC Design—A Conceptual Approach. Educ. Sci., 8.10.3390/educsci8010025 ↗
12. Consistent Commitment: Patterns of Engagement across Time in Massive Open Online Courses (MOOCs)10.18608/jla.2015.23.5 ↗
13. Shanabrook, D.H., Cooper, D.G., Woolf, B.P., and Arroyo, I. (2010, January 11–13). Identifying High-Level Student Behavior Using Sequence-Based Motif Discovery. Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA.
14. Wen, M., and Rose, C.P. (2014, January 3). Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management ACM, Shanghai, China.10.1145/2661829.2662033 ↗
15. Shih, B., Koedinger, K., and Scheines, R. (2010, January 11–13). Unsupervised Discovery of Student Learning Tactics. Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA.
16. Analyzing Sequence Data with Markov Chain Models in Scientific Experiments10.1007/s42979-021-00768-5 ↗
17. Beal, C., and Cohen, P.R. (2007, January 9–13). Modeling Learning Patterns of Students with a Tutoring System Using Hidden Markov Models. Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, AIED 2007, Los Angeles, CA, USA.
18. Liu, X., and Cheng, T. (2003;, January 18–20). Video-Based Face Recognition Using Adaptive Hidden Markov Models. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA.
19. Hassan, M.R., and Nath, B. (2005, January 8–10). Stock Market Forecasting Using Hidden Markov Model: A New Approach. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA’05), Warsaw, Poland.10.1109/ISDA.2005.85 ↗
20. Rabiner, L.R. (1990). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Readings in Speech Recognition, Elsevier.10.1016/B978-0-08-051584-7.50027-9 ↗
21. Predicting Student Performance in an Educational Game Using a Hidden Markov Model10.1109/TE.2020.2984900 ↗
22. Arieli-Attali, M., Ou, L., and Simmering, V.R. (2019). Understanding Test Takers’ Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data. Front. Psychol., 10.10.3389/fpsyg.2019.00083 ↗
23. Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting10.1088/1757-899X/160/1/012067 ↗
24. Jin, X., Wang, Q., Yan, Z., Yang, H., Li, Z., and Wang, Z. (2021, January 26–28). A Study on Fake and Real Lane Change Behavior Prediction Based on Discrete Hidden Markov Model. Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China.10.23919/CCC52363.2021.9550172 ↗
25. Driving Behavior at Midblock Crosswalks with Rectangular Rapid Flashing Beacons: Hidden Markov Model Approach Using Naturalistic Data10.1016/j.aap.2021.106406 ↗
26. Panaggio, M.J., Fang, M., Bang, H., Armstrong, P.A., Binder, A.M., Grass, J.E., Magid, J., Papazian, M., Shapiro-Mendoza, C.K., and Parks, S.E. (2023). Inferring School District Learning Modalities during the COVID-19 Pandemic with a Hidden Markov Model. PLoS ONE, 18.10.1371/journal.pone.0292354 ↗
27. Gupta, A., and Dhingra, B. (2012, January 16–18). Stock Market Prediction Using Hidden Markov Models. Proceedings of the 2012 Students Conference on Engineering and Systems, Allahabad, India.10.1109/SCES.2012.6199099 ↗
28. Balakrishnan, Γ. (2024, December 15). Tech Reports. Available online: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-109.html.
29. Combining Optimal Clustering and Hidden Markov Models for Extractive Summarization10.3115/1119312.1119315 ↗
30. Wang, X., Wu, H., and Yi, Z. (2018, January 27–29). Research on Bank Anti-Fraud Model Based on K-Means and Hidden Markov Model. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.10.1109/ICIVC.2018.8492795 ↗
31. Sassi, I., Anter, S., and Bekkhoucha, A. (2021, January 9). Fast Parallel Constrained Viterbi Algorithm for Big Data with Applications to Financial Time Series. Proceedings of the 2021 4th International Conference on Robot Systems and Applications ACM, Chengdu, China.10.1145/3467691.3467697 ↗
32. Alachiotis, N.S., Kotsiantis, S., and Verykios, V.S. (2021, January 26–28). Evaluating Contact Sessions and Assignments Grades Impact with Association Rules. Proceedings of the 11th International Conference in Open and Distance Learning, Athens, Greece.
33. Supervised Machine Learning Models for Student Performance Prediction
34. Alachiotis, N.S., Stavropoulos, E.C., and Verykios, V.S. (2017, January 23–26). Learning Analytics with Excel in a Blended Learning Course. Proceedings of the 9th International Conference in Open and Distance Learning, Athens, Greece.10.12681/icodl.1077 ↗
35. Sneiders, M., Urtans, E., and Abu, A. (Balt. J. Mod. Comput., 2023). Time Series Models for Predicting Student Grades on the Moodle Platform, Balt. J. Mod. Comput., Awaiting Revision.
36. Selecting Hidden Markov Model State Number with Cross-Validated Likelihood10.1007/s00180-007-0097-1 ↗
37. Aarno, D., and Kragic, D. (2006, January 9–15). Layered HMM for Motion Intention Recognition. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.10.1109/IROS.2006.282606 ↗
38. Verlinde, P. (2003). Error Detecting and Correcting Codes. Encyclopedia of Information Systems, Elsevier.10.1016/B0-12-227240-4/00062-9 ↗
39. Modeling Student Behavior with Two-Layer Hidden Markov Models
40. Ibe, O.C. (2009). Markov Processes for Stochastic Modeling, Elsevier Academic Press.
41. Mackare, K., Jansone, A., and Sneiders, M. (2022, January 16–18). The Digital Material Formatting App Prototype 3.0. Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives.10.1109/ICECCME55909.2022.9988477 ↗
42. Amo, D., Alier, M., García-Peñalvo, F.J., Fonseca, D., and Guerrero, C. (2018, January 18–19). Learning Analytics to Assess Students’ Behavior with Scratch through Clickstream. Proceedings of the Learning Analytics Summer Institute Spain 2018, León, Spain.