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The myUMBC Check My Activity (CMA) feedback tool allows students to compare their own Blackboard Learning Management System (LMS) activity to an anonymous summary of course peers. If instructors use the LMS grade book, students can also compare their own activity with peers earning the same, higher or lower grade on any assignment. Why might they want to do so? Since 2007, UMBC students earning a D or F have used the Blackboard Learning Management System (LMS) about 40% less than students earning a C or higher. In addition, a 2017 study (Fritz) found students using the CMA were 1.5 times more likely to earn a C or higher course grade or 2.0 or higher term GPA (p <.001). More info. |
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To be clear, students' digital footprints in Blackboard are simply a proxy for their engagement in a class. Social science research has used proxies for concepts that are difficult to measure (e.g., well-being, socio-economic status, etc.). Also, while grades in a course are definitive in terms of student success through courses and semesters over time they can occur to late in a current term to be actionable by students – or even faculty, advisors and others who might wish to help them. As such, the CMA merely provides students with a dashboard of how their digital engagement in a tool used by most courses, instructors and other students compares to a class average. For more resources on how to improve one's engagement in a course, please see the Academic Support Center's "Student Resources." Note: Faculty can disable the CMA’s display of anonymous grade distribution for any assessment by enclosing the Blackboard grade book column with double asterisks (**). For example: [**Assignment 1**]. However, research by Educause (2008, 2007) has shown students value checking grades more than any other LMS function. Also, faculty might want to consider how the CMA actually helps amplify the feedback impact of their Bb course designs & grades, without having to assign – and grade – more work. Tell Me - Definitions
Tell Me - Selected ReferencesJaviya, Prachee. “Decoding a Decade of Feedback @ myUMBC’s ‘Check My Activity.’” Webinar presented at the Learning Analytics Community of Practice, UMBC, April 10, 2024. https://doit.umbc.edu/analytics/community/events/event/128478/ . Alpeshkumar Javiya, P., Kleinsmith, A., Karen Chen, L., Fritz, J. (2024). Parsing Post-deployment Students’ Feedback: Towards a Student-Centered Intelligent Monitoring System to Support Self-regulated Learning. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2150. Springer, Cham. https://doi.org/10.1007/978-3-031-64315-6_11 Fritz, John, Thomas Penniston, Mike Sharkey, and John Whitmer. (2021). “Scaling Course Design as a Learning Analytics Variable.” In Blended Learning Research Perspectives, 1st ed. Vol. 3. New York: Routledge, 2021. https://doi.org/10.4324/9781003037736-7 . | UMBC-only version (login req'd). Fritz, John, and John Whitmer. “Ethical Learning Analytics: ‘Do No Harm’ versus ‘Do Nothing.’” New Directions for Institutional Research 2019, no. 183 (May 26, 2020): 27–38. https://doi.org/10.1002/ir.20310 . Forteza, D., Whitmer, J., Fritz, J., & Green, D. (2018). Improving Risk Predictions | Blackboard Analytics [Case Study]. Blackboard.http://www.blackboard.com/education-analytics/improving-risk-predictions.html Fritz, J. (2017). "Using Analytics to Nudge Student Responsibility for Learning." In New Directions for Higher Education, 2017 (179), 65–75. https://doi.org/10.1002/he.20244 | UMBC-only version (login req'd). |