Page Comparison

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

• Hits - Every time you view a file, post to a discussion, or read an announcement, that is considered a hit.

• Rank For Students: This is where your activity in a Bb course places you among all students in the course. It is NOT a grade, but a student with a higher rank is more active than one with a lower rank, compared to course peers. You will also see (by color code) if your activity is above, below or within 20% of the Bb course average.
  For Faculty: This is where your Bb course's average student activity places you among all Bb courses in your discipline.
  
  Important: Rank is ONLY a measure of activity, not quality.

• Sessions - Every time you log in to a Blackboard course, that is considered a new session.

• Grade Distribution Report - If your instructor uses the Bb grade book, this report will show how your own activity compares with those who earned the same, higher or lower grade on any assignment.

Tell Me - Selected References

Javiya, 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. (2017). "Using Analytics to Nudge Student Responsibility for Learning." In New Directions for Higher Education, 2017 (179), 65–752024). 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/10.10024324/he9781003037736-7 . 20244 |   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, John, Thomas Penniston, Mike Sharkey, and John WhitmerJ. (20212017). “Scaling Course Design as a Learning Analytics Variable.” In Blended Learning Research Perspectives, 1st ed. Vol. 3. New York: Routledge, 2021"Using Analytics to Nudge Student Responsibility for Learning." In New Directions for Higher Education, 2017 (179), 65–75. https://doi.org/10.43241002/9781003037736-7he.20244 | UMBC-only version (login req'd).