Learning styles and formative assessment strategy: Enhancing student achievement in Web-based learning

K. H. Wang, T. H. Wang, W. L. Wang, S. C. Huang

Research output: Contribution to journalReview article

91 Citations (Scopus)


The purpose of this research was to investigate the effects of formative assessment and learning style on student achievement in a Web-based learning environment. A quasi-experimental research design was used. Participants were 455 seventh grade students from 12 classes of six junior high schools. A Web-based course, named BioCAL, combining three different formative assessment strategies was developed. The formative assessment strategies included Formative Assessment Module of the Web-Based Assessment and Test Analysis system (FAM-WATA) (with six Web-based formative assessment strategies), Normal Module of Web-Based Assessment and Test Analysis system (N-WATA) (only with partial Web-based formative assessment strategy) and Paper and Pencil Test (PPT) (without Web-based formative assessment strategy). Subjects were tested using Kolb's Learning Style Inventory, and assigned randomly by class into three groups. Each group took Web-based courses using one of the formative assessment strategies. Pre- and post-achievement testing was carried out. A one-way ANCOVA analysis showed that both learning style and formative assessment strategy are significant factors affecting student achievement in a Web-based learning environment. However, there is no interaction between these two factors. A post hoc comparison showed that performances of the FAM-WATA group are higher than the N-WATA and PPT groups. Learners with a 'Diverger' learning style performed best followed by, 'Assimilator', 'Accommodator', and 'Converger', respectively. Finally, FAM-WATA group students are satisfied with six strategies of the FAM-WATA.

Original languageEnglish
Pages (from-to)207-217
Number of pages11
JournalJournal of Computer Assisted Learning
Issue number3
Publication statusPublished - 2006 Jun 1


All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications

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