Abstract
With the rapid development of information technology, an increasing number of student learning activities are taking place in online environments. Analyzing online learning behavior data can further improve teaching and assessment methods, enhance learning outcomes, and promote independent learning. However, it is a challenge for teachers to evaluate learning status and academic performance of each student through online learning systems. This study aimed to explore an efficient method for assessing student academic performance. A performance prediction method combined prior knowledge and deep learning models was proposed. Before putting learning behavior data into deep learning models, a weight matrix for learning behaviors was reconstructed. And the learning behavior features derived from this weight matrix along with the raw learning behavior data were imported into a custom Dense layer network. Additionally, an attention mechanism was incorporated into the model to improve model prediction accuracy. Experimental results showed that this method could effectively predict academic performance, identify high-risk students who may need assistance and improve the quality of online teaching.
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Acknowledgments
This study was funded by the MOE Project [Foreign Language Proficiency Assessment System in the New Era, China #2] under Grant [number 22JJD740019]; the Ministry of Education of Humanities and Social Science Project under Grant [number 20YJCZH124].
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Appendix
Appendix
Learning Behavior Categories
Index | Learning Behavior Category | Illustration |
|---|---|---|
0 | resource | Total number of clicks on PDF resources |
1 | url | Total clicks on the links to audio/video |
2 | homepage | Total clicks on course homepage |
3 | subpage | Total clicks to access other websites during the course |
4 | glossary | Total clicks on vocabulary related to the course |
5 | forumng | Total clicks on the discussion forum |
6 | oucollaborate | Total clicks of online video discussions |
7 | dataplus | Total clicks on additional resources, such as videos, websites, etc |
8 | quiz | Total on the course quiz |
9 | ouelluminate | Total clicks in online tutorial sessions |
10 | htmlactivity | Total clicks on HTML interaction |
11 | dualpane | Total clicks on website related information and activities |
12 | sharedsubpage | Total clicks to access shared pages |
13 | questionnaire | Total clicks on the questionnaires related to the course |
4 | page | Total clicks on the information related to the course |
15 | externalquiz | Total clicks for additional quizzes |
16 | ouwiki | Total clicks accessing Wiki |
17 | repeatactivity | Total clicks on the course contents from previous weeks |
18 | folder | Total clicks on course related files |
19 | oucontent | Total clicks on the contents of the assignment |
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Xie, Z., Lu, M., Pan, X. (2024). Online Performance Prediction Combined Prior Knowledge and Deep Learning Models. In: Kubincová, Z., et al. Emerging Technologies for Education. SETE 2023. Lecture Notes in Computer Science, vol 14606. Springer, Singapore. https://doi.org/10.1007/978-981-97-4243-1_9
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DOI: https://doi.org/10.1007/978-981-97-4243-1_9
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