Predicting the intention to use learning analytics for academic advising in higher education

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...

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Main Authors: Bahari, Mahadi, Arpaci, Ibrahim, Mohd. Azmi, Nurulhuda Firdaus, Shuib, Liyana
Format: Article
Language:English
Published: MDPI 2023
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Online Access:http://eprints.utm.my/107355/1/MahadiBahari2023_PredictingtheIntentiontoUseLearningAnalytics.pdf
http://eprints.utm.my/107355/
http://dx.doi.org/10.3390/su152115190
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spelling my.utm.1073552024-09-03T06:23:46Z http://eprints.utm.my/107355/ Predicting the intention to use learning analytics for academic advising in higher education Bahari, Mahadi Arpaci, Ibrahim Mohd. Azmi, Nurulhuda Firdaus Shuib, Liyana L Education (General) T58.6-58.62 Management information systems 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. MDPI 2023-10 Article PeerReviewed application/pdf en http://eprints.utm.my/107355/1/MahadiBahari2023_PredictingtheIntentiontoUseLearningAnalytics.pdf Bahari, Mahadi and Arpaci, Ibrahim and Mohd. Azmi, Nurulhuda Firdaus and Shuib, Liyana (2023) Predicting the intention to use learning analytics for academic advising in higher education. Sustainability (Switzerland), 15 (21). pp. 1-22. ISSN 2071-1050 http://dx.doi.org/10.3390/su152115190 DOI:10.3390/su152115190
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic L Education (General)
T58.6-58.62 Management information systems
spellingShingle L Education (General)
T58.6-58.62 Management information systems
Bahari, Mahadi
Arpaci, Ibrahim
Mohd. Azmi, Nurulhuda Firdaus
Shuib, Liyana
Predicting the intention to use learning analytics for academic advising in higher education
description 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.
format Article
author Bahari, Mahadi
Arpaci, Ibrahim
Mohd. Azmi, Nurulhuda Firdaus
Shuib, Liyana
author_facet Bahari, Mahadi
Arpaci, Ibrahim
Mohd. Azmi, Nurulhuda Firdaus
Shuib, Liyana
author_sort Bahari, Mahadi
title Predicting the intention to use learning analytics for academic advising in higher education
title_short Predicting the intention to use learning analytics for academic advising in higher education
title_full Predicting the intention to use learning analytics for academic advising in higher education
title_fullStr Predicting the intention to use learning analytics for academic advising in higher education
title_full_unstemmed Predicting the intention to use learning analytics for academic advising in higher education
title_sort predicting the intention to use learning analytics for academic advising in higher education
publisher MDPI
publishDate 2023
url http://eprints.utm.my/107355/1/MahadiBahari2023_PredictingtheIntentiontoUseLearningAnalytics.pdf
http://eprints.utm.my/107355/
http://dx.doi.org/10.3390/su152115190
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score 13.214268