51²è¹Ý

As part of 51²è¹Ý in Four, we intend to broaden faculty participation in both Early Progress Reports (EPRs) and Midterm Progress Reports (MPRs). We will begin to disseminate the faculty participation data in a user-friendly dashboard to deans and department chairs to make it easier to identify those faculty and departments who need improvement in reporting their grades. Deans will be more motivated to act upon this information with the implementation of performance-based funding put in place by the new provost. Additionally, plans are in place to add a comment box to the current EPR and MPR reporting system. The boxes are intended to help elevate the quality of information obtained from faculty regarding students. Training and awareness will be provided to faculty on this once it is implemented.

A consensus across members of staff, faculty and students is that additional more-holistic data, beyond EPR and MPRs, is needed to inform 51²è¹Ý’s early-alert efforts. Piloted on campus in 2020, Dropout Detective, a retention tool based on Canvas (our learning management system), will serve as a supplement to current grade reports. Drawing on faculty-inputted and student-usage Canvas data, Dropout Detective provides real-time dashboards to faculty and staff to identify at-risk students. This data will be viewed in tandem with EPRs and MPRs to help inform student outreach efforts.

We will also work with academic support services in the A-LEC and 51²è¹Ý Libraries as well as with staff in Residence Life and Student Housing (RLSH) to provide academic support to students who need it and to actively encourage them to take advantage of the opportunities. The director of academic initiatives within RLSH is a critical member of the early-alert team, and will have access to EPR/MPR data sorted by Residential Commons so that additional programming might be made available where on-campus students live. In addition, a joint initiative between RLSH and A-LEC has already piloted an expanded midterm query to all students, asking students how they view their academic performance in the term. Thus, we hope to identify those students who are not performing as well as they expect of themselves. In this way, we hope to reach out to students who are frustrated, yet motivated and looking for ways to be better scholars at 51²è¹Ý.

We will expand outreach from advisors to students with two or more deficiencies and actively work with major advisors to encourage them to use the grade reports to identify majors at risk. We will begin to disseminate the student grade report data in a user-friendly dashboard to department chairs, advisors and records offices to make it easier to identify students at risk, and to look for patterns related to sex, race, Pell-grant eligibility and indicators of academic risk such as probationary status. Training will be provided to advisors. Department chairs will be trained on the reasons driving the data and how to use the new dashboards. This effort will directly connect the early-alert pillar with the advising pillar.

Finally, we want to integrate information from across the University to create a real-time system of early alerts that brings together data from across the University. The first phase
will involve evaluating the quality of the data currently available in Canvas – our learning management system – and comparing deficiencies identified in Canvas at the same time we identify deficiencies reported by faculty in EPR/MPR. A concerted effort will be made to work with the first-year and gateway pillar initiative to build capacity and interest among the faculty to use Canvas. The second phase of data integration will focus on bringing together data from RLSH and the Office of the Dean of Students to consider students holistically.