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The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students’ data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM have focused more on technical aspects, such as evaluating methods and techniques, while ignoring the end-users’ acceptance of the technology. Realising its importance, this study has analysed the determinants that could influence the acceptance of EDM technology, particularly among undergraduate students since they are the most affected by the technology. For this reason, 11 hypotheses have been formulated based on determinants of technology readiness index (TRI) and technology acceptance model 3 (TAM3), which could render an in-depth insight regarding EDM acceptance. A survey was conducted on 211 undergraduate students from six public universities in Malaysia for a period of 6 months (May to October 2014) using questionnaires as the instrument to collect data to test the hypothesised relationships. The partial least squares structural equation modeling (PLS-SEM) approach was used to analyse the proposed acceptance model, which was run using SmartPLS, version 3 software. The findings have revealed that ‘relevance for analysing’, ‘self-efficacy’, ‘facilitating conditions’, ‘perceived usefulness’, ‘perceived ease of use’, ‘optimism’ and ‘discomfort’ have influenced the acceptance of EDM technology among undergraduate students.
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- Educational data mining acceptance among undergraduate students
Zawiyah M. Yusof
Mohd Zakree Ahmad Nazri
- Springer US
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