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. Copyright © 2017 The Author(s).
CitationKong, S. C., Li, P., & Song, Y. (2018). Evaluating a bilingual text-mining system with a taxonomy of key words and hierarchical visualization for understanding learner-generated text. Journal of Educational Computing Research, 56(3), 369-395. doi: 10.1177/0735633117707991
- Bilingual text-mining system
- Bilingual taxonomy of key words
- Hierarchical visualization
- Learner-generated text