Facilitating Vocabulary Note Taking on Computers Through the Deep Processing Strategy

Ming Chi Liu, Yueh Min Huang, Yu Cheng Chien

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The recording of words in a vocabulary notebook is regarded as a plausible and efficient method for learners, since they can organize and manage the individual words they wish to acquire. The wide appeal of portable computers has resulted in a rapid increase in taking notes via computers across college campuses. Although computers can increase transcription speed when students take notes, they are also detrimental to learning, because students tend to mindlessly transcribe results, resulting in shallow processing. To resolve the issue, in this study, an association-based strategy is introduced to assist learners in compiling vocabulary notebooks. To assess the strategy, an experiment was conducted to investigate the effects of the proposed nonlinear associations note-taking method with respect to the traditional linear outline method. The results show that both strategies could increase vocabulary retention; however, the associations format was significantly better than the outline format. In addition, through a deep analysis of both types of note-taking processes, we also found that students using the associations note-taking method developed more meaningful word association styles (e.g., linking words that had similar contexts) in composing their notes than students using the outline note-taking method. When examining how learners with different cognitive styles took the notes, the results showed that learner performance depends on cognitive style, as well as the chosen organizational format. The findings suggest that a nonlinear association note-taking strategy may help students organize words in a meaningful way.

Original languageEnglish
Pages (from-to)1296-1323
Number of pages28
JournalJournal of Educational Computing Research
Issue number8
Publication statusPublished - 2019 Jan 1

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications


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