Analyzing online discussion data for understanding the student's critical thinking. (7th October 2021)

• Record Type: Journal Article
• Authors: Yang, Juan; Du, Xu; Hung, Jui-Long; Tu, Chih-hsiung
• Is Part Of: Data technologies and applications. Volume 56:Number 2(2022)
• Page Start: 303
• Page End: 326
• Publication Date: 2021-10-07
• Subjects: Online discussion -- Critical thinking -- Text mining -- Multi-feature fusion -- Predictive modeling -- Instructional decision-making; Information science -- Periodicals; Electronic information resources -- Periodicals; Knowledge management -- Periodicals; 020.5
• DOI: 10.1108/DTA-04-2021-0088
• Languages: English
• ISSNs: 2514-9288

Abstract

Abstract : Purpose: Critical thinking is considered important in psychological science because it enables students to make effective decisions and optimizes their performance. Aiming at the challenges and issues of understanding the student's critical thinking, the objective of this study is to analyze online discussion data through an advanced multi-feature fusion modeling (MFFM) approach for automatically and accurately understanding the student's critical thinking levels. Design/methodology/approach: An advanced MFFM approach is proposed in this study. Specifically, with considering the time-series characteristic and the high correlations between adjacent words in discussion contents, the long short-term memory–convolutional neural network (LSTM-CNN) architecture is proposed to extract deep semantic features, and then these semantic features are combined with linguistic and psychological knowledge generated by the LIWC2015 tool as the inputs of full-connected layers to automatically and accurately predict students' critical thinking levels that are hidden in online discussion data. Findings: A series of experiments with 94 students' 7, 691 posts were conducted to verify the effectiveness of the proposed approach. The experimental results show that the proposed MFFM approach that combines two types of textual features outperforms baseline methods, and the semantic-based padding can further improve the prediction performance of MFFM. It can achieve 0.8205 overall accuracy and 0.6172 F1 score for the "high" category on the validation dataset. Furthermore, it is found that the semantic features extracted by LSTM-CNN are more powerful for identifying self-introduction or off-topic discussions, while the linguistic, as well as psychological features, can better distinguish the discussion posts with the highest critical thinking level. Originality/value: With the support of the proposed MFFM approach, online teachers can conveniently and effectively understand the interaction quality of online discussions, which can support instructional decision-making to better promote the student's knowledge construction process and improve learning performance.