Graduate Program Assessment Report

Graduate Certificate in Applied Statistics

PROGRAM LEARNING OUTCOMES	Data Source*	Outcome
Demonstrate advanced statistical thinking and data collection.	Completion rate of STAT 511	Met (100%)
Apply advanced statistical methodology.	Completion rate of STAT 512	Met (100%)

5. What Was Learned.

a)  Based on the analysis of the data, and compared to the threshold values established, what was learned from the assessment? What areas of strength in the program were identified from this assessment process?

Our PhD programs are successful at meeting program learning outcomes, though sometimes the timeline takes longer than our Milestones. Still, we find that our recent focus pairing timelines with PLOs has helped keep our attention on supporting students to make timely progress through the degree.

Our MS Math, Math Ed, and Stat programs are successful at meeting program learning outcomes and at meeting timelines.

Our MS Data Science program requires more attention. For the second consecutive biennial assessment report, we have not met the learning outcome threshold for the computer science Algorithms foundation. We will provide focused attention on how to address this in the upcoming two years, with a particular look at admissions standards and prerequisite fulfillment for that course.

 

b) What areas were identified that either need improvement or could be improved in a different way from this assessment process?

We are continually aware that our core graduate programs in mathematics, mathematics education, and statistics receive focused attention in a way that our data science program, as the newest program, does not. As documented in our most recent program review, the dispersed nature of the coursework across three disciplines, and two departments & colleges, means that the program needs more focused attention. 

6. How We Responded.

a)  Describe how “What Was Learned” was communicated to the department, or program faculty. How did faculty discussions re-imagine new ways program assessment might contribute to program growth/improvement/innovation beyond the bare minimum of achieving program learning objectives through assessment activities conducted at the course level?

The Graduate Program Committee met and discussed the assessment process and results and led a discussion among the department. The Department has engaged in various program updates over the past two years, adjusting requirements, updating learning program outcomes, adjusting admissions processes by involving more faculty, adjusting advising practices, adjusting comprehensive and qualifying exam requirements and practices, and maintaining a focus on our students as those who are part of our vision of using the tools of our disciplines to promote human flourishing. In particular, to help students meet milestones, we have focused our attention on supporting students as individuals and engaging in collective conversations to discuss milestones in relation to each student’s academic progress and overall wellbeing. 

 

b)  How are the results of this assessment informing changes to enhance student learning in the program?

Reflecting on these results gives us confidence that our practice of ongoing discussion and adjustment is a positive for our programs. It reminds us to continue to focus on the Data Science program.

 

C)  If information outside of this assessment is informing programmatic change, please describe that.

See part a.

 

d)  What support and resources (e.g. workshops, training, etc.) might you need to make these adjustments?

We took part in a discussion with the assistant provost responsible for assessment and found that informal discussion useful.

7. Closing the Loop(s).

Reflect on the program learning outcomes, how they were assessed in the previous cycle (refer to #1 of the report), and what was learned in this cycle. What action will be taken to improve student learning objectives going forward?

 

a)  Self-Reporting Metric (required answer): Based on the findings and/or faculty input, will there any curricular or assessment changes (such as plans for measurable improvements, or realignment of learning outcomes)?

X Yes

b) In reviewing the last report that assessed the PLO(s) in this assessment cycle, what changes proposed were implemented and will be measured in future assessment reports?

We updated a program learning outcome in the PhD mathematics program: we replaced “Demonstrate a solid understanding of core graduate level real and complex analysis” with “Demonstrate competence in graduate-level real analysis and linear algebra”, to reflect the shift we made in reflecting on our last set of PLOs and required coursework for the degree. 

We adjusted how we use threshold values in the case of low numbers of students, and specifying a reliance on qualitative analysis has given us a useful mechanism for meaningful reflection in these circumstances. 

Going forward, we intend to update the course requirements of our Math MS program for the following effects.

1. Better reflect existing faculty expertise.
2. Better prepare Math MS students for a PhD program in a mathematically-related 
   field.
3. Align the experiences of typical 1st year Math MS and typical 1st year Math PhD 
   students.

In the M.S. Statistics program, we intend to examine the alignment between comprehensive exam practices and course content and learning outcomes. 

In the PhD Math Ed program, we intend to examine coursework to align it with current trends among peer programs and with faculty expertise.

c)  Have you seen a change in student learning based on other program adjustments made in the past? Please describe the adjustments made and subsequent changes in student learning.

This assessment process also lets us reflect on how and which students move from the MS to the PhD (in non-published data tables). This is useful, because recruiting for graduate programs remains a priority for the department.

PDF of Graduate Program Assessment Report