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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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | http://eprints.utm.my/107355/1/MahadiBahari2023_PredictingtheIntentiontoUseLearningAnalytics.pdf |
| _version_ | 1811132522124279808 |
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-09-24T00:05:37Z |
| format | Article |
| id | utm.eprints-107355 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-24T00:05:37Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-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 |
| 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 |
| title | 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_short | 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 |
| topic | L Education (General) T58.6-58.62 Management information systems |
| url | http://eprints.utm.my/107355/1/MahadiBahari2023_PredictingtheIntentiontoUseLearningAnalytics.pdf |
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