Student Facing Dashboards: One Size Fits All?
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This emerging technology report reviews a new development in educational technology, student-facing dashboards, which provide comparative performance feedback to students calculated by Learning Analytics-based algorithms on data generated from university students’ use of educational technology. Instructor- and advisor-facing dashboards emerged as one of the first direct applications of Learning Analytics, but the results from early implementations of these displays for students provide mixed results about the effects of their use. In particular, the “one-size-fits-all” design of many existing systems is questioned based on findings in related research on performance feedback and student motivation which has shown that various internal and external student-level factors affect the impact of feedback interventions, especially those using social comparisons. Integrating data from student information systems into underlying algorithms to produce personalized dashboards may mediate the possible negative effects of feedback, especially comparative feedback, and support more consistent benefits from the use of such systems.
KeywordsLearning Analytics Dashboards Performance feedback Higher education Social comparison Motivation
- Aguilar, S. (2016). Perceived motivational affordances: Capturing and measuring students’ sense-making around visualizations of their academic achievement information. (Doctoral Dissertation) University of Michigan, Ann Arbor, MI.Google Scholar
- Arnold, K. E. (2010). Signals: Applying academic analytics. ECUCAUSE Quarterly, 33(1), 1.Google Scholar
- Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270). ACM.Google Scholar
- Bodily, R., & Verbert, K. (2017). Trends and issues in student-facing learning analytics reporting systems research. In Proceedings of the 7th international conference on learning analytics and knowledge. Vancouver, CA: ACM.Google Scholar
- Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In Proceedings of the ascilite 2014 conference (pp. 629–633). Dunedin, NZ.Google Scholar
- Corrin, L. & de Barba, P. (2015). How do students interpret feedback delivered via dashboards? In Proceedings of the international conference on learning analytics and knowledge (pp. 430–431). Poughkeepsie, NY: ACM.Google Scholar
- Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realizing the affordances of real-time SNA within networked learning environments. In Proceedings of the 7th international conference on networked learning. http://www.networkedlearningconference.org.uk/past/nlc2010/abstracts/PDFs/Dawson.pdf
- Durall, E., & Gros, B. (2014). Learning analytics and a metacognitive tool. In Proceedings of the 6th international conference on computer supported education (CSEDU) (pp. 380–384).Google Scholar
- Duval, E. (2011). Attention please! Learning analytics for visual recommendation. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 9–17). New York, NY: ACM.Google Scholar
- Khan, I., & Pardo, A. (2016). Data2U: Scalable real time student feedback in active learning environments. In Proceedings of the international conference on learning analytics and knowledge (pp. 249–253). Edinburgh, Scotland: ACM.Google Scholar
- Koester, B. P., Grom, G. & McKay, T. A. (2016). Patterns of gendered performance difference in introductory STEM courses. Physical Review Physics Education Research. arXiv:1608.07565 [physics.ed-ph].
- Krumm, A., Waddington, R. J., Teasley, S. D., & Lonn, S. (2014). A learning management system-based early warning system for academic advising in undergraduate engineering. In Learning analytics: From research to practice (pp. 103–119). New York, NY: Springer.Google Scholar
- Lonn, S., Aguilar, S. & Teasley, S. D. (2014). Issues, challenges, and lessons learned when scaling up a learning analytics intervention. In Proceedings of the international conference on learning analytics & knowledge (pp. 234–239). Leuven, Belgium: ACM.Google Scholar
- Major, B., Testa, M., & Bylsma, W. H. (1991). Responses to upward and downward social comparisons: The impact of esteem-relevance and perceived control in social comparison. In J. Suls & T. A. Wills (Eds.), Contemporary theory and research (pp. 237–260). Hillsdale, NJ: Erlbaum.Google Scholar
- Pardo, A., & Dawson, S. (2015). How can data be used to improve learning? In P. Reiman, S. Bull, M. Kickermeier-Rusy, R. Vatrapu, & B. Wasson (Eds.), Measuring and visualizing learning in the information-rich classroom. London: Routledge.Google Scholar
- Reimers, G., & Neovesky, A. (2015). Student focused dashboards—An analysis of current student dashboards and what students really want. In Proceedings of the 7th international conference on computer supported education (CSEDU) (pp. 399–404).Google Scholar
- Suls, J. E., & Wills, T. A. E. (1991). Social comparison: Contemporary theory and research. MAHWAH: Lawrence Erlbaum Associates Inc.Google Scholar
- Teasley, S. D., Haley, S., Oster, M., Haynes, C., & Whitmer, J. (2017). How am I doing?: Evaluating student-facing performance dashboards in higher education (Manuscript in preparation).Google Scholar
- Tufte, E. R. (1990). Envisioning information. Cheshire, CT: Graphic Press.Google Scholar
- Young, J. R. (2016). What clicks from 70,000 courses reveal about student learning. Chronicle of Higher Education, 63(3). http://www.chronicle.com/article/What-Clicks-From-70000/237704.