Investigating students' acceptance of a statistics learning platform using technology acceptance model

Research output: Contribution to journalArticlespeer-review

47 Citations (Scopus)

Abstract

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a revised model consisting of four external factors (self-efficacy, facilitating conditions, subjective norm, and anxiety) that may influence students' perceptions and acceptance of the platform. A mixed research method was used. A total of 102 participants were involved in this study. Data collection includes questionnaires survey, individual interviews, and focus group discussions. The findings show that students' intention to use the platform is affected by their attitude toward the platform, which is significantly influenced by perceived usefulness. Further suggestions regarding the design and implementation of a learning platform are provided based on the observations and results of the study. This preliminary study provides valuable insights into our further refinement of the platform and development of a learning analytics platform in the future for better learning of students in statistics. Copyright © 2017 The Author(s).
Original languageEnglish
Pages (from-to)865-897
JournalJournal of Educational Computing Research
Volume55
Issue number6
Early online dateJan 2017
DOIs
Publication statusPublished - 2017

Citation

Song, Y., & Kong, S.-C. (2017). Investigating students' acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 55(6), 865-897.

Keywords

  • Statistics learning platform
  • Statistics education
  • Digital resources
  • Technological acceptance model
  • Blended learning approach

Fingerprint

Dive into the research topics of 'Investigating students' acceptance of a statistics learning platform using technology acceptance model'. Together they form a unique fingerprint.