Topic：

Learning Behavior and Sentiment Analysis in MOOCs

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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.

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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.?

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參考資料來源：陳靜 面向MOOCs的學習行為與情感分析