F. Okubo
H. Ogata
ABSTRACT
In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.
- Baradwaj, B. & Pal, S. 2011. Mining Educational Data to Analyze Student's Performance, International Journal of Advanced Computer Science and Applications, 6, 2, 63--69.Google Scholar
- Bodén, M. 2002. A Guide to Recurrent Neural Networks and Backpropagation, The Dallas Project, SICS Technical Report, 1--10.Google Scholar
- Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, A., Kojima, K. & Yamada, M. 2015. E-Book-based Learning Analytics in University Education, Proc. ICCE2015, 401--406.Google Scholar
- Okubo, F., Hirokawa, S., Oi, M., Shimada, A., Kojima, K. & Yamada, M. & Ogata, H. 2016. Learning Activity Features of High Performance Students, Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016), 28--33.Google Scholar
- Okubo, F., Shimada, A., Yin, C. & Ogata, H. 2015. Visualization and Prediction of Learning Activities by Using Discrete Graphs, Proc. ICCE2015, 739--744.Google Scholar
- You, J. W. 2016. Identifying significant indicators using LMS data to predict course achievement in online learning, Internet and Higher Education, 29, 23--30.Google ScholarCross Ref
Index Terms
- A neural network approach for students' performance prediction
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