The Impact of Dashboard Feedback Type on Learning Effectiveness, Focusing on Learner Differences

Han Wang, Tao Huang (), Yuan Zhao and Shengze Hu
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Han Wang: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Tao Huang: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Yuan Zhao: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Shengze Hu: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China

Sustainability, 2023, vol. 15, issue 5, 1-16

Abstract: With the exponential growth of educational data, increasing attention has been given to student learning supported by learning analytics dashboards. Related research has indicated that dashboards relying on descriptive analytics are deficient compared to more advanced analytics. However, there is a lack of empirical data to demonstrate the performance and differences between different types of analytics in dashboards. To investigate these, the study used a controlled, between-groups experimental method to compare the effects of descriptive and prescriptive dashboards on learning outcomes. Based on the learning analytics results, the descriptive dashboard describes the learning state and the prescriptive dashboard provides suggestions for learning paths. The results show that both descriptive and prescriptive dashboards can effectively promote students’ cognitive development. The advantage of prescriptive dashboard over descriptive dashboard is its promotion in learners’ learning strategies. In addition, learners’ prior knowledge and learning strategies determine the extent of the impact of dashboard feedback on learning outcomes.

Keywords: learning analytics; dashboard; personalized feedback; learning effectiveness; individual learner factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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