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Analyzing online discussion data for understanding the student's critical thinking

Juan Yang (School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China) (National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China)
Xu Du (National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China)
Jui-Long Hung (Department of Educational Technology, Boise State University, Boise, Idaho, USA)
Chih-hsiung Tu (Department of Educational Specialties, Northern Arizona University, Flagstaff, Arizona, USA)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 7 October 2021

Issue publication date: 15 March 2022

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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.

Keywords

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.

Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 62107032 and 61877027), and the Planning Subject for Education Sciences of Hubei Province.

Citation

Yang, J., Du, X., Hung, J.-L. and Tu, C.-h. (2022), "Analyzing online discussion data for understanding the student's critical thinking", Data Technologies and Applications, Vol. 56 No. 2, pp. 303-326. https://doi.org/10.1108/DTA-04-2021-0088

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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