SP22 Data Science & Learning Analytics Workshops Schedule

For the Spring 22 semester, DoIT's Analytics & Instructional Technology groups will continue to offer student success and learning analytics-related workshops for the UMBC community. 

All workshops will start at noon, and be available via video conference (virtual meeting URL to be announced on the day of the event). To register and view more information, check the links below:

"How Do UMBC Bb Course Designs Correlate to Final Grades?" (2/17, Noon). This workshop will demo a UMBC Tableau dashboard prototype recently featured in Blended Learning Research Perspectives.

"Introducing HelioCampus Playbooks for Retention Modeling" (2/25, Noon). In this session, DoIT's Robert Carpenter and Len Mancini will lead a demo & discussion about the importance of predictive scores for each student using a series of dashboards that describe the risks associated with a particular score and how the model comes to that decision.

"Do Students Carry 'Lessons Learned' From One Course to the Next?" (3/10, Noon). Chemistry Principal Lecturer Tara Carpenter will share preliminary results from her Spring 21 learning analytics "mini-grant." FYI: The Spring 22 call for proposals is due 5/27/22.

"Revisiting UMBC's 6-year Graduation Rate" (3/18, Noon). UMBC's Data Science team developed a predictive model to predict the likelihood a First Time Full Time Freshman would graduate in 6 years. In this session, DoIT's Robert Carpenter and Len Mancini will discuss how student predictions tend to change from semester 2 to semester 3, some of the observed trends from cohort to cohort and the implications of those trends.

"Nudging Students to Success" (4/22, Noon). In this analytics workshop, DoIT's Robert Carpenter and Len Mancini will break down the nudges sent to students by demographic group, college, and program, and show how student predictive scores change from week 4 to week 7. Moreover, we will talk about how the importance of engagement features increases as the semester progresses.