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How patterns of students dashboard use are related to their achievement and self-regulatory engagement

Published: 23 March 2020 Publication History

Abstract

The aim of student-facing dashboards is to support learning by providing students with actionable information and promoting self-regulated learning. We created a new dashboard design aligned with SRL theory, called MyLA, to better understand how students use a learning analytics tool. We conducted sequence analysis on students' interactions with three different visualizations in the dashboard, implemented in a LMS, for a large number of students (860) in ten courses representing different disciplines. To evaluate different students' experiences with the dashboard, we computed chi-squared tests of independence on dashboard users (52%) to find frequent patterns that discriminate students by their differences in academic achievement and self-regulated learning behaviors. The results revealed discriminating patterns in dashboard use among different levels of academic achievement and self-regulated learning, particularly for low achieving students and high self-regulated learners. Our findings highlight the importance of differences in students' experience with a student-facing dashboard, and emphasize that one size does not fit all in the design of learning analytics tools.

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LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
March 2020
679 pages
ISBN:9781450377126
DOI:10.1145/3375462
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 23 March 2020

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  1. academic achievement
  2. self-regulated learning
  3. sequential pattern mining
  4. student-facing dashboard

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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  • (2024)The influence of learning analytics dashboard information design on cognitive load and performanceEducation and Information Technologies10.1007/s10639-024-12606-129:15(19729-19752)Online publication date: 1-Apr-2024
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