Open AccessLearning Process Oriented Teaching Quality Improvement
Author(s) -
Yifan Liu,
Jing Zhan,
Xue Fan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012131
Subject(s) - computer science , support vector machine , machine learning , artificial intelligence , process (computing) , cluster analysis , hyperparameter optimization , stability (learning theory) , data mining , feature (linguistics) , quality (philosophy) , linguistics , philosophy , epistemology , operating system
In order to get targeted improving strategies for better teaching quality, it is necessary to establish learning data model for specific courses covering the whole learning process, and accurate and stable student performance classification model accordingly. Drawing on existing learning data analysis of online teaching, this paper proposes process oriented learning feature model, student performance classification method, and corresponding improving strategies analysis. With learning data from “Computer Network Fundamentals” course, in which combined online and offline teaching methods are used, learning process oriented feature model are defined using correlation analysis and clustering methods; improved Support Vector Machine (SVM) classification method with grid search optimization are proposed to get optimized student performance classification model, which gains 8% improvement on the accuracy compared to classified SVM model. Based on experiments, the accuracy of optimized classification model is 90%, with 6% false positive rate and 11% false negatives rate, which are reasonable and shows the accuracy and stability of our model. The improving strategies analysis for different students classification are also given, which can provide strong support for teaching quality improvement.