Evaluating a Bilingual Text-Mining System With a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

Siu Cheung Kong, Ping Li, Yanjie Song

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)369-395
Number of pages27
JournalJournal of Educational Computing Research
Volume56
Issue number3
DOIs
Publication statusPublished - 1 Jun 2018
Externally publishedYes

Keywords

  • bilingual taxonomy of key words
  • bilingual text-mining system
  • evaluation
  • hierarchical visualization
  • learner-generated text

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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