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Effects and acceptance of precision education in an AI-supported smart learning environment

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Abstract

The research presents precision education that aims to regulate students’ behaviors through the learning analytics dashboard (LAD) in the AI-supported smart learning environment (SLE). The LAD basically tracks and visualizes traces of learning actions to make students aware of their learning behaviors and reflect these against the agreed goals. This research aims to realize the digital transformation of the learning space, thereby improving students’ learning outcomes with the assistance of the learning dashboard. To examine whether there was a close relationship between the frequency of using the whole platform and academic results, the data was collected from 50 first-year university students who registered with the innovative thinking course. Based on the data, we constructed the Technology Acceptance Model (TAM) questionnaire and interview guide to realize the students’ acceptance and feedback towards the SLE. Students were clustered into high-mark and low-mark groups based on their final results. The Wilcoxon rank-sum test is used to identify a significant difference between the two groups using the precision education platform. Subsequently, the partial least squares structural equation modeling (PLS-SEM) is further utilized to analyze the relationship between system quality, perceived ease of use, and perceived usefulness on behavioral intention and learning transfer.

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Data availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Funding

This work was supported by the MOE Teaching Practice Research Program [grant number PGE1080215].

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  1. Center for General Education, College of Future, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan

    Yung-Hsiang Hu

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Hu, YH. Effects and acceptance of precision education in an AI-supported smart learning environment. Educ Inf Technol 27, 2013–2037 (2022). https://doi.org/10.1007/s10639-021-10664-3

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