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Perceived usefulness of a machine learning-assisted recommendation system for generic competency development

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Listed:
  • Simon Wong
  • Ka Lok Wong
  • Yui-Yip Lau
  • Kia Tsang
  • Ada Chan

Abstract

Generic competency development activities (GCDAs) help students develop critical thinking, problem-solving, innovation, creativity, communication and social skills. This study evaluated students’ acceptance of a machine learning-assisted recommendation system (MARS) developed to recommend GCDAs for students in a higher education institution. This study adopted a quantitative approach to evaluate the higher education students’ perceived usefulness of MARS based on a new appropriate model derived from three widely used models related to technology adoption and leisure activities. In August 2023, the participants of orientation for freshmen were invited to complete an online questionnaire after they tried MARS. 351 valid responses were analyzed using multiple regression. The results revealed that the students positively perceive accepting MARS as a useful tool for choosing GCDAs and indicated the students’ perceptions were affected more by their programs of study, career development and personal interests than by social influence and facilitating conditions on their selection of GCDAs. These findings based on the new model provide implications for the implementation of education technology for generic competency development.

Suggested Citation

  • Simon Wong & Ka Lok Wong & Yui-Yip Lau & Kia Tsang & Ada Chan, 2024. "Perceived usefulness of a machine learning-assisted recommendation system for generic competency development," Journal of Education and e-Learning Research, Asian Online Journal Publishing Group, vol. 11(3), pages 614-621.
    • Handle: RePEc:aoj:jeelre:v:11:y:2024:i:3:p:614-621:id:5971
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