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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.
Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
(MDPI (Basel, Switzerland), 2022-12) Ramaswami G; Susnjak T; Mathrani A
Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
Learning analytics dashboard: a tool for providing actionable insights to learners
(BioMed Central Ltd, 2022-02-14) Susnjak T; Ramaswami G; Mathrani A
This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

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