| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === 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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