Educational Data Mining and Sentiment Analysis in Virtual Learning Environments: a Systematic Mapping
DOI:
https://doi.org/10.18264/eadf.v12i2.1786Abstract
Sentiment analysis is a Data Mining area that involves natural language processing, information extraction, artificial intelligence and machine learning. Thus, sentiment analysis and also emotions from students in virtual learning environments enable verifying possible deficiencies during the learning process, for example. The aim of this paper is to present the results of a systematic mapping of the literature carried out on techniques, methods, algorithms, libraries and tools about educational data mining used for analyzing sentiments and emotions from students in virtual learning environments. Furthermore, the purposes for the analysis of sentiments and the types of emotions considered were identified. Therefore, 20 primary studies were selected to verify details. The results show the predominance of machine learning algorithms for sentiment analysis, addressing courses and teachers evaluation, the effectiveness of the learning environment, student satisfaction and difficulties. Furthermore, most studies explore the sentiment polarity: positive, negative and neutral.
Keywords: Educational data mining. Sentiments. Emotions. Virtual learning environment.
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