Toward better intelligent learning (iLearning) performance: what makes iLearning work for students in a university setting?

Hung-Yi Wu, Hung-Shu Wu, I-Shuo Chen (Lead / Corresponding author), Yu-Pei Su

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Abstract

We explored the critical factors associated with iLearning that impact students’ learning performance and identified the factors with a notable influence to help managers in higher education institutions increase the effectiveness of iLearning for students. We initially synthesised 4 main dimensions (including 26 criteria): performance expectancy, lecturers’ influence, quality of service, and personal innovativeness. Subsequently, we conducted surveys in two stages. First, by studying a group of students with experience using iLearning at Taiwanese universities, we extracted 5 critical dimensions (including 18 criteria) through a factor analysis. Second, by studying a group of senior educators and practitioners in Taiwan, we prioritised the dimensions and criteria through the analytic hierarchy process (AHP). We found that performance expectancy is the top critical dimension, and the top five critical criteria pertain to enhancing the learning performance, increasing the learning participation, altering learning habits, ensuring access at all times, and enabling prompt use of learning resources. Moreover, we recommend several suggestions for the relevant managers to enhance the students’ iLearning performance.
Original languageEnglish
JournalBehaviour & Information Technology
Early online date31 Jan 2022
DOIs
Publication statusE-pub ahead of print - 31 Jan 2022

Keywords

  • Intelligent learning (iLearning)
  • analytic hierarchy process (AHP)
  • factor analysis (FA)
  • higher education
  • university

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