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Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards
- 1. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards Automated Insightful Drill-DownRecommendations for Learning Analytics Dashboards Dr Hassan Khosravi [email protected] Professor Marta Indulska [email protected] Shiva Shabaninejad [email protected] Dr Aneesha Bakharia [email protected] Dr Pedro Isaias [email protected]
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- 7. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 7 Finding Insights In LADs Predictive LADs q Provide automatic decision-making. qe.g., label students based on their estimate of success.
- 8. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 8 Finding Insights In LADs Exploratory LADs q Allow users to navigate through multi- dimensional data sets. qRelies on user’s judgement for understanding and interpreting the results. Curiosity Driven Exploration q Can be used to answer specific questions q e.g. ‘how do new students that have failed the midterm compared to other students’) Operations in Online Analytical Processing (OLAP) Source::https://senturus.com/blog/reporting-multidimensional-olap-data-sources/
- 9. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 9 Finding Insights In LADs Exploratory LADs q Allow users to navigate through multi- dimensional data sets. qRelies on user’s judgement for understanding and interpreting the results. Operations in Online Analytical Processing (OLAP) Source::https://senturus.com/blog/reporting-multidimensional-olap-data-sources/ Data Driven Exploration q Finding insightful features for drill-downs q e.g. ‘Identify the student attributes of students that have failed the midterm”.
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- 11. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 11 All Possible Drill-down ActionsSample Students Dataset Finding Insightful Features Student # Residential Status Video Engagement Assessment Score S1 DomesticHigh High S2 Domestic High High S3 Domestic High High S4 Domestic High High S5 Domestic High High S6 Domestic High Mid S7 Domestic Low Low S8 Domestic Low Low S9 Domestic Low Low S10 International High Low … Target Insightful drill-downs
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- 19. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 19 Approach q AID process steps: 1.A decision tree classification method to examine the insightfulness of a set of promising drill-down paths 2. KL-divergence to rank and select the highest ranked path
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- 21. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 21 The Course Insights Platform • Course Insights is a LAD that provides filterable and comparative visualisations •Course Insights aims to provide actionable insights for teachers by linking data from several sources
- 22. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 22 Insights: Manual Drill-downs Aim: To study how manual drill-downs are used in LADs. Research Questions • RQ1 How complex are the manual drill-downs applied by users? •RQ2. Which manual drill-downs are commonly applied? Data set: 356 manual drilldown actions performed by 71 teaching staff members who were involved in teaching courses that used Course Insights at The University of Queensland in 2019.
- 23. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 23 Insights: Manual Drill-downs – Results RQ1 How complex are the manual drill-downs applied by users? Results: 84% of drill-downs were performed on a single attribute and the average number of attributes for each drill-down was 1.35 ±0.55 RQ2 Which manual drill-downs are commonly applied? Mostly used to investigate simple curiosity driven questions.
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- 25. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 25 Insightful Drill-Downs in Action – Case Study Main Findings 1.AID can generate insightful (high significance score) drill-down recommendations instantly. 2. The average length of a drill-down action is not a good indicator of its insightfulness. 3. 𝛼 plays a very important role in the quality of the generated recommendations.
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- 28. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards 28 Future Work •Extend AID to suggest promising values of 𝛼 • Partner with academics to investigate: • criteria for determining insightfulness • best ways to present recommendations
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- 30. Automated Insightful Drill-Down Recommendations for Learning Analytics Dashboards Automated Insightful Drill-DownRecommendations for Learning Analytics Dashboards Dr Hassan Khosravi [email protected] Professor Marta Indulska [email protected] Shiva Shabaninejad [email protected] Dr Aneesha Bakharia [email protected] Dr Pedro Isaias [email protected]