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    Can learning analytics provide useful insights? An exploration on course level
    (Australasian Society for Computers in Learning in Tertiary Education, 2019-01-01) Heinrich E
    This concise paper reports on an analysis of access logs of a first year university course that was delivered in a blended format. This analysis is an initial step in a wider project aimed at investigating if learning analytics can provided useful insights on course level, targeting both student learning and the needs of teachers. Preliminary findings show potential in noting when students need targeted help, a lack of correlation between access logs and grades, and insights into the degree by which course completion rates are affected by the lack of student engagement.
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    Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT
    (Springer Nature in conjunction with the International Artificial Intelligence in Education Society (IAIED), 2024-06) Susnjak T
    A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ~7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.
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    Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
    (Elsevier Ltd, 2021-11-20) Mathrani A; Susnjak T; Ramaswami G; Barczak A
    Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive models to enhance their instructional services. However, questions remain on how the proposed LA system is suitable, meaningful, and justifiable. In this concept paper, we examine generalizability and transparency of the internals of predictive models, alongside the ethical challenges in using learners’ data for building predictive capabilities. Model generalizability or transferability is hindered by inadequate feature representation, small and imbalanced datasets, concept drift, and contextually un-related domains. Additional challenges relate to trustworthiness and social acceptance of these models since algorithmic-driven models are difficult to interpret by themselves. Further, ethical dilemmas are faced in engaging with learners’ data while developing and deploying LA systems at an institutional level. We propose methodologies for apprehending these challenges by establishing efforts for managing transferability and transparency, and further assessing the ethical standing on justifiable use of the LA strategy. This study showcases underlying relationships that exist between constructs pertaining to learners’ data and the predictive model. We suggest the use of appropriate evaluation techniques and setting up research ethics protocols, since without proper controls in place, the model outcome would not be portable, transferable, trustworthy, or admissible as a responsible outcome. This concept paper has theoretical and practical implications for future inquiry in the burgeoning field of learning analytics.