Evaluating a Bilingual Text-Mining System With a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text. (June 2018)

• Record Type: Journal Article
• Authors: Kong, Siu Cheung; Li, Ping; Song, Yanjie
• Is Part Of: Journal of educational computing research. Volume 56:Number 3(2018)
• Page Start: 369
• Page End: 395
• Publication Date: 2018-06
• Subjects: bilingual text-mining system -- bilingual taxonomy of key words -- evaluation -- hierarchical visualization -- learner-generated text; Computer literacy -- Periodicals; Computer-assisted instruction -- Periodicals; Computer managed instruction -- Periodicals; Education -- Data processing -- Periodicals; 371.334
• DOI: 10.1177/0735633117707991
• Languages: English
• ISSNs: 0735-6331

Abstract

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course "e-Learning in primary mathematics" was asked to reflect "what is e-Learning" before and after the course. Their concept of "e-Learning" was investigated by counting the matching key words using the text-mining system and a content analysis of learner-generated text using a rubric, respectively. The correlations of the results using these two methods were .823 and .840 in the preteaching and postteaching reflections. This text-mining system has the potential as a supporting tool for teachers to gain a general understanding of learner-generated text using the hierarchical visualization for supporting pedagogical decision-making, which can be applied to massive open online courses with a large enrolment of learners.