Student-Facing Educational Dashboard Design for Online Learners

Arta Farahmand, M. Ali Akber Dewan, Fuhua Lin

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

6 Citations (Scopus)

Abstract

The current shift from traditional classrooms to online learning in higher education calls for more attention to self-regulated learning. This research is motivated by the growing interest in potential of using learning analytics dashboard (LAD) to increase individuals' self-regulation by creating visibility into their performance in various applications. This study explores how data visualization can be integrated with online learning to improve learners' performance through enhancing their skills in planning and organization. We are working on the design of a comprehensive LAD, focusing on micro-level of learning analytics to support learning activities of students. The LAD includes the following two features to enhance students' self-regulation in online learning: (1) a function to track students' progress compared to other students' over time; (2) reminders to help students with upcoming deadlines and auto-generating to do lists. The hypothesis is that the LAD will increase students' engagement, motivation, and self-regulation in an online learning environment. This study is significant because it contributes to the body of knowledge by exploring how student-generated data can be used to improve self-regulated learning. The practical contribution of this study is to create a personalized LAD for students based on the learner-generated data to benefit students' organization skill, planning skill, and motivation.

Original languageEnglish
Title of host publicationProceedings - IEEE 18th International Conference on Dependable, Autonomic and Secure Computing, IEEE 18th International Conference on Pervasive Intelligence and Computing, IEEE 6th International Conference on Cloud and Big Data Computing and IEEE 5th Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020
Pages345-349
Number of pages5
ISBN (Electronic)9781728166094
DOIs
Publication statusPublished - Aug. 2020
Event18th IEEE International Conference on Dependable, Autonomic and Secure Computing, 18th IEEE International Conference on Pervasive Intelligence and Computing, 6th IEEE International Conference on Cloud and Big Data Computing and 5th IEEE Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020 - Virtual, Calgary, Canada
Duration: 17 Aug. 202024 Aug. 2020

Publication series

NameProceedings - IEEE 18th International Conference on Dependable, Autonomic and Secure Computing, IEEE 18th International Conference on Pervasive Intelligence and Computing, IEEE 6th International Conference on Cloud and Big Data Computing and IEEE 5th Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020

Conference

Conference18th IEEE International Conference on Dependable, Autonomic and Secure Computing, 18th IEEE International Conference on Pervasive Intelligence and Computing, 6th IEEE International Conference on Cloud and Big Data Computing and 5th IEEE Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020
Country/TerritoryCanada
CityVirtual, Calgary
Period17/08/2024/08/20

Keywords

  • data mining
  • information visualization
  • learning analytics dashboard
  • learning management system
  • online learning
  • self-regulated learning

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