Bloom-Epistemic and Sentiment Analysis Hierarchical Classification in Course Discussion Forums

Toba, Hapnes and Hernita, Yolanda Trixie and Ayub, Mewati and Wijanto, Maresha Caroline (2024) Bloom-Epistemic and Sentiment Analysis Hierarchical Classification in Course Discussion Forums. International Journal of Evaluation and Research in Education (IJERE), 13 (1). pp. 80-90. ISSN 2252-8822

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Abstract

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.

Item Type:	Article
Contributors:	Contribution Contributors NIDN/NIDK Email UNSPECIFIED Toba, Hapnes UNSPECIFIED UNSPECIFIED UNSPECIFIED Hernita, Yolanda Trixie UNSPECIFIED UNSPECIFIED UNSPECIFIED Ayub, Mewati UNSPECIFIED UNSPECIFIED UNSPECIFIED Wijanto, Maresha Caroline UNSPECIFIED UNSPECIFIED
Uncontrolled Keywords:	Bloom taxonomy; Course learning system; Discussion forum; Machine-learning; Sentiment analysis
Subjects:	T Technology > T Technology (General)
Depositing User:	Perpustakaan Maranatha
Date Deposited:	17 Dec 2023 23:55
Last Modified:	17 Dec 2023 23:55
URI:	http://repository.maranatha.edu/id/eprint/32385

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