Learning Behavior and Sentiment Analysis
Topic:
Learning Behavior and Sentiment Analysis in MOOCs
Background:
As a form of online education, massive open online courses (MOOCs) are widely used and have boomed in recent years because of advantages such as non-geographical and no time limitation.
Most courses have high dropout rates and learners are prone to have poor performance due to the unconstrained learning environment.
Although there have been many studies, the following problems still exist in the applications of learning behavior and sentiment analysis.
Firstly, for dropout behavior prediction, learning behavior discrepancy leads to a wide range of fluctuation of prediction results, which may result in low prediction results.
Secondly, there lacks of causal analysis between learning behaviors and performance. Most studies have focused on correlation analysis between them, resulting in unreliable conclusions and undefective decision support.
Thirdly, for sentiment analysis, supervised methods rely on a large amount of labeled data. Constructing large-scale labeled datasets is very laborious and time consuming. Besides, there exist a large amount of unlabeled data that have not been fully utilized.
What:
To address the problems mentioned above, this dissertation studies the learning behavior and sentiment analysis in online learning environment, aiming at accurate identification of at-risk learners and their sentiment, and making causal analysis.
The main works and contributions of this dissertation are summarized as follows:
(1) A dropout behavior prediction approach based on web log is proposed.
(2) A causal analysis approach between learning behaviors and learning performance is proposed.
(3) A co-training semi-supervised learning model for sentiment polarity identification of course review is proposed.
(4) An unsupervised rule-based aspect and opinion extraction approach for course review is proposed.
参考资料来源:陈静 面向MOOCs的学习行为与情感分析