Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies
    (Springer Nature BV, 2023-09-01) Ramaswami G; Susnjak T; Mathrani A; Umer R
    Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing learning difficulties. While LADs have gained popularity, existing LADs are still far behind when it comes to employing predictive analytics into their designs. Our systematic literature review has revealed limitations in the utilization of predictive analytics tools among existing LADs. We find that studies leveraging predictive analytics only go as far as identifying the at-risk students and do not employ model interpretation or explainability capabilities. This limits the ability of LADs to offer data-driven prescriptive advice to students that can offer them guidance on appropriate learning adjustments. Further, published studies have mostly described LADs that are still at prototype stages; hence, robust evaluations of how LADs affect student outcomes have not yet been conducted. The evaluations until now are limited to LAD functionalities and usability rather than their effectiveness as a pedagogical treatment. We conclude by making recommendations for the design of advanced dashboards that more fully take advantage of machine learning technologies, while using suitable visualizations to project only relevant information. Finally, we stress the importance of developing dashboards that are ultimately evaluated for their effectiveness.
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    Rural–Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study
    (MDPI AG, 9/05/2023) Mathrani A; Umer R; Sarvesh T; Adhikari J
    This study explores digital divide issues that influenced online learning activities during the COVID-19 lockdown in five developing countries in South Asia. A multi-layered and interpretive analytical lens guided by three interrelated perspectives—structure, cultural practices, and agency—revealed various nuanced aspects across location-based (i.e., rural vs. urban) and across gendered (i.e., male vs. female) student groups. A key message that emerged from our investigation was the subtle ways in which the digital divide is experienced, specifically by female students and by students from rural backgrounds. Female students face more structural and cultural impositions than male students, which restricts them from fully availing digital learning opportunities. Rich empirical evidence shows these impositions are further exacerbated at times of crisis, leading to a lack of learning (agency) for women. This research has provided a gendered and regional outlook on digital discriminations and other inequalities that came to the forefront during the COVID-19 lockdown. This study is especially relevant as online learning is being touted as the next step in digitization; therefore, it can inform educational policymaking and help build inclusive digital societies and bridge current gender and regional divisions.