Learning analytics : on effectiveness of dashboarding for enhancing student learning : a thesis with publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology, School of Mathematical & Computational Sciences, Massey University, Auckland, New Zealand
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Date
2022
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Massey University
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Abstract
Ongoing advancements in learning analytics have provided institutions with immense opportunities to identify and discern student learning patterns across different course offerings. These patterns can help identify those students who may be at some risk of course failure (or of course completion) as soon as possible, which further allows institutions in timely offering them guidance and support for overcoming their learning difficulties. Learning Analytics Dashboards (LAD) are currently used to deliver graphical representations of data-driven insights timeously to support management teams, instructors, and students. LADs provide a comprehensive overview on current learning environments with much use of visualizations for displaying learning patterns that can capture various aspects of the student learning experience. Hence, LADs are increasingly being used as a pedagogical approach for motivating students and supporting them in meeting their learning goals.
This research study has developed a student-facing LAD that shows a snapshot of students' online learning behaviors by implementing descriptive analytics components and also incorporates machine learning in a way that enables both predictive and prescriptive analytics.
The study is divided into two parts. First, a generic predictive model has been developed to identify the at-risk students across a wide variety of courses. After generating the predictive model, model explainability using anchors has demonstrated the reasoning behind predictive models to enable transparency of the predictive models and increase students’ trust as they interact with the LAD. Machine learning models used in this study have implemented prescriptive components by prioritizing which changes in learning behaviors and which learning strategies adopted by a student will most likely translate into favorable results.
Second, a LAD is developed. The dashboard provides visualizations that incorporates graphical and statistical information of online behavioral student patterns as they engage with the coursework. An online student survey that gauged LAD effectiveness for its usefulness and the motivational impact of its prescriptive output to better engage students with the coursework has shown promising results. The LAD design, as far as we know, is the first in the learning analytics domain that has combined all three analytics, namely descriptive, predictive, and prescriptive. This thesis has investigated an active area of research and has paved the way for more meaningful LAD design and implementation, thereby contributing to both theory and practice.
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