Modeling learning behaviors and predicting performance in an intelligent tutoring system
A two-layer hidden Markov modeling approach
Bibliographic Data
| ID | 21632620 |
|---|---|
| Authors | Yun Tang (0000-0003-2340-1109, Central China Normal University, corresponding author), Zhengfan Li (Central China Normal University), Zheng‐Fan Li (0000-0003-0477-0814, Central China Normal University), Guoyi Wang (North Carolina State University), Xiangen Hu (0000-0001-9045-4070, Central China Normal University) |
| Year | 2023 |
| Volume | 31 |
| Issue | 9 |
| Pages | 5495-5507 |
| Publication date | 2023-12-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2021.2010100 |
| OpenAlex | W3214736509 |
| Language | EN |
| Citations received | 7 |
| References cited | 40 |
To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with different learning outcomes demonstrated distinct learning patterns. Students who failed a problem set exhibited more passive learning behaviors and could hardly learn from practice, while students who mastered a problem set could effectively regulate their learning. Furthermore, we extended the use of TL-HMM to predicting learning outcome from behavior sequences and checked through cross-validation. TL-HMM is demonstrated helpful to gain insight into learners’ interactions with online learning environments. In practice, TL-HMM could be embedded in intelligent tutoring systems to monitor learning behaviors and learner status, so as to detect the difficulties of learners and facilitate learning
Hidden Markov model · Machine learning · Computer Science · Innovative Teaching and Learning Methods · Online and Blended Learning · Online Learning and Analytics · Artificial Intelligence
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1,4 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |