A Deep Learning-Oriented Personal Credit Model for Online Learning Platform

摘要

There are several problems in online learning platform, such as the lack of effective supervision of learning process and the low completion rate, the general situation of slack learning attitude and cheating, the lack of scientific planning of learning methods and the serious fragmentation, and the low standard-reaching rate of learning results and the poor credibility. In order to solve these problems, this paper defines the concept of personal credit in learning, and declares the characteristics of personal credit from the four aspects of online learning process, attitude, method and results based on the comparative analysis of deep learning and surface learning in educational theory, then builds a personal credit model oriented to deep learning in online learning platform. The model includes five evaluation dimensions of participation, positivity, regularity, qualified rate and influence, as well as 14 explicit or implicit behavior indicators, especially the corresponding learning behavior abnormality detection mechanism. The experimental results show that this model has the characteristics of scientific clustering index, comprehensive evaluation angles and reliable exception handling, which can effectively evaluate learners' learning behavior state and provide a new way for online learning platform to implement educational contracts and share services.