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Predicting the Intention to Use Learning Analytics for Academic Advising in Higher Education

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  • Mahadi Bahari

    (Department of Information Systems, Faculty of Management, Universiti Teknologi Malaysia, Johor 81310, Malaysia
    College of Business Administration, University of Business Technology, Jeddah 23435, Saudi Arabia
    UTM Big Data Centre, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

  • Ibrahim Arpaci

    (Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Balikesir 10200, Türkiye)

  • Nurulhuda Firdaus Mohd Azmi

    (UTM Big Data Centre, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

  • Liyana Shuib

    (Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Learning analytics (LA) is a rapidly growing educational technology with the potential to enhance teaching methods and boost student learning and achievement. Despite its potential, the adoption of LA remains limited within the education ecosystem, and users who do employ LA often struggle to engage with it effectively. As a result, this study developed and assessed a model for users’ intention to utilize LA dashboards. The model incorporates constructs from the “Unified Theory of Acceptance and Use of Technology”, supplemented with elements of personal innovativeness, information quality, and system quality. The study utilized exploratory research methodology and employed purposive sampling. Participants with prior experience in LA technologies were selected to take part in the study. Data were collected from 209 academic staff and university students in Malaysia (59.33% male) from four top Malaysian universities using various social networking platforms. The research employed “Partial Least Squares Structural Equation Modeling” to explore the interrelationships among the constructs within the model. The results revealed that information quality, social influence, performance expectancy, and system quality all positively impacted the intention to use LA. Additionally, personal innovativeness exhibited both direct and indirect positive impacts on the intention to use LA, mediated by performance expectancy. This study has the potential to offer valuable insights to educational institutions, policymakers, and service providers, assisting in the enhancement of LA adoption and usage. This study’s contributions extend beyond the present research and have the potential to positively impact the field of educational technology, paving the way for improved educational practices and outcomes through the thoughtful integration of LA tools. The incorporation of sustainability principles in the development and deployment of LA tools can significantly heighten their effectiveness, drive user adoption, and ultimately nurture sustainable educational practices and outcomes.

Suggested Citation

  • Mahadi Bahari & Ibrahim Arpaci & Nurulhuda Firdaus Mohd Azmi & Liyana Shuib, 2023. "Predicting the Intention to Use Learning Analytics for Academic Advising in Higher Education," Sustainability, MDPI, vol. 15(21), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15190-:d:1265918
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    References listed on IDEAS

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    1. Nurul Atiqah Johar & Si Na Kew & Zaidatun Tasir & Elizabeth Koh, 2023. "Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    2. Arpaci, Ibrahim, 2023. "Predictors of financial sustainability for cryptocurrencies: An empirical study using a hybrid SEM-ANN approach," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
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