Varying Impacts: The Role of Student Self-Evaluation in Navigating Learning Analytics
Pages 535 - 538
Abstract
The enthusiasm for student-facing analytics as tools for supporting student self-regulation is overshadowed by uncertainties about their actual impact on student outcomes. This study aims to fill the gap in experimental evidence concerning student-facing analytics by implementing a randomized control trial. Specifically, we investigated the effects of data visualizations that display student level of content mastery in comparison to their peers, alongside recommendations for learning strategies. The preliminary results reveal that the intervention impacts student attribution and motivation in varying ways, based on their self-evaluation of their current course performance. Further analysis, including coding students' interpretation of the data visualization, will be conducted to uncover the diverse ways students might interpret the analytics.
References
[1]
Robert Bodily and Katrien Verbert. 2017. Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, Vol. 10, 4 (2017), 405--418.
[2]
Dan Davis, Ioana Jivet, René F Kizilcec, Guanliang Chen, Claudia Hauff, and Geert-Jan Houben. 2017. Follow the successful crowd: raising MOOC completion rates through social comparison at scale. In Proceedings of the seventh international learning analytics & knowledge conference. 454--463.
[3]
Xiaojing Duan, Chaoli Wang, and Guieswende Rouamba. 2022. Designing a learning analytics dashboard to provide students with actionable feedback and evaluating its impacts. In Proceedings of International Conference on Computer Supported Education.
[4]
Carol S Dweck. 2013. Self-theories: Their role in motivation, personality, and development. Psychology press.
[5]
Jacquelynne S Eccles and Allan Wigfield. 2002. Motivational beliefs, values, and goals. Annual review of psychology, Vol. 53, 1 (2002), 109--132.
[6]
Annelie J Harvey and Helen Keyes. 2019. How do I compare thee? An evidence-based approach to the presentation of class comparison information to students using Dashboard. Innovations in Education and Teaching International (2019).
[7]
Sami Heikkinen, Mohammed Saqr, Jonna Malmberg, and Matti Tedre. 2023. Supporting self-regulated learning with learning analytics interventions--a systematic literature review. Education and Information Technologies, Vol. 28, 3 (2023), 3059--3088.
[8]
Dirk Ifenthaler, Clara Schumacher, and Jakub Kuzilek. 2023. Investigating students' use of self-assessments in higher education using learning analytics. Journal of Computer Assisted Learning, Vol. 39, 1 (2023), 255--268.
[9]
Jeonghyun Kim, Il-Hyun Jo, and Yeonjeong Park. 2016. Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, Vol. 17 (2016), 13--24.
[10]
Mehmet Kokocc and Arif Altun. 2021. Effects of learner interaction with learning dashboards on academic performance in an e-learning environment. Behaviour & Information Technology, Vol. 40, 2 (2021), 161--175.
[11]
Qiujie Li, Yeonji Jung, Alyssa Friend Wise, Sophie Sommer, and Victoria G Axelrod. 2021. Designing analytics to support team learning. Perspectives on Digitally-Mediated Team Learning (2021), 147--165.
[12]
Qiujie Li, Di Xu, Rachel Baker, Amanda Holton, and Mark Warschauer. 2022. Can student-facing analytics improve online students' effort and success by affecting how they explain the cause of past performance? Computers & Education, Vol. 185 (2022), 104517.
[13]
Steven Lonn, Stephen J Aguilar, and Stephanie D Teasley. 2015. Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, Vol. 47 (2015), 90--97.
[14]
Yeonjeong Park and Il-Hyun Jo. 2015. Development of the Learning Analytics Dashboard to Support Students' Learning Performance. J. Univers. Comput. Sci., Vol. 21, 1 (2015), 110--133.
[15]
Anouschka van Leeuwen, Stephanie D Teasley, and Alyssa Friend Wise. 2022. Teacher and student facing learning analytics. Handbook of learning analytics (2022), 130--140.
[16]
Olga Viberg, Mohammad Khalil, and Martine Baars. 2020. Self-regulated learning and learning analytics in online learning environments: A review of empirical research. In Proceedings of the tenth international conference on learning analytics & knowledge. 524--533.
Index Terms
- Varying Impacts: The Role of Student Self-Evaluation in Navigating Learning Analytics
Recommendations
Unpacking the ‘Learning' in Student-Facing Analytics: Metacognition and the Zone of Proximal Development
Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to ...
Research on Visualized Design for Role-Based Online Learning Analytics
Revised Selected Papers of the ICWL 2015 International Workshops on Current Developments in Web Based Learning - Volume 9584The in-depth application of new technologies have brought about an explosion of educational data and people are placing more and more emphasis on learning analytics. Against this background, how to make better use of these data and display them in a ...
Establishing an ethical literacy for learning analytics
LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And KnowledgeThis paper borrows multiple frameworks from the field of technical communication in order to review theory, research, practice, and ethics of the Learning Analytics and Knowledge (LAK) discipline. These frameworks also guide discussion on the ethics of ...
Comments
Information & Contributors
Information
Published In
July 2024
582 pages
ISBN:9798400706332
DOI:10.1145/3657604
- General Chair:
- David Joyner,
- Program Chairs:
- Min Kyu Kim,
- Xu Wang,
- Meng Xia
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 July 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
Conference
L@S '24
Acceptance Rates
Overall Acceptance Rate 117 of 440 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 226Total Downloads
- Downloads (Last 12 months)226
- Downloads (Last 6 weeks)23
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in