| 摘要(英) |
This study is based on a System Programming course of the ilearning online learning platform. The experiment consists of two main purposes: early warning and video recommendation. In this study, a recommendation mechanism was proposed, which combined the results of early warning and the list of recommended videos to give students at the right time to improve students′ learning efficiency and improve their learning outcomes.
Early warning includes two factors: the timing of intervention and the accuracy of prediction. This paper collects online learning history, syllabus, homework and test scores of students in the past five years, extracts features from it, and uses Principal Component Regression (PCR) to establish a learning effectiveness prediction model, and then observe the prediction indicators named pMSE to find the best intervention time point.
On the other hand, this study compares eight classification algorithms, using accuracy, precision, recall, f1-score, and AUC as indicators to evaluate the quality of the model, and distinguishes high-risk students, and finally gives appropriateness alert words to remind students.
The video recommendation is to give students a suitable recommendation list through the online learning process and test answering situation of the students, in order to achieve the goal of improving the learning performance of the students. The experimental results show that the results of the grouping with the questionnaire found that among the student populations with moderate learning motivation and moderate learning interest, the experimental group students were significantly better than the control group. It means that the recommendation mechanism for students of specific groups of students has a significant impact.
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| 參考文獻 |
Abraham Wald. New York: John Wiley and Sons, Inc. Sequentail Analysis., New York, 1947.
Asif, R., Merceron, A., & Pathan, M. K. (2014). Predicting student academic performance at degree level: a case study. International Journal of Intelligent Systems and Applications, 7(1), 49.
Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145.
Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031.
Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students′ learning performance in natural science courses. Computers & Education, 69, 121-130.
Lu, O. H., Huang, A. Y., Huang, J. C., LIN, A. J., HIROAKI OGATA, Yang, S. J. . (2017). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Educational Technology & Society.
Oladokun, V., Adebanjo, A., & Charles-Owaba, O. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72-79.
Owen H. T. Lu, Anna Y. Q. Huang, & Stephen. J.H. Yang. (2018). Benchmarking and Tuning Regression Algorithms on Predicting Students’ Academic Performance. 26th International Conference on Computers in Education. Philippines: Asia-Pacific Society for Computers in Education
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students′ final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
Yoo, J., & Kim, J. (2014). Can online discussion participation predict group project performance? investigating the roles of linguistic features and participation patterns. International Journal of Artificial Intelligence in Education, 24(1), 8-32. |
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