Educause Webinar June 4, 2019
Collaboration between Carnegie Mellon University and James Madison University
Assessment for learning improvement is central to higher education
- desire to impact students
We are different, yet
- public university vs. private university
- 23000, primarily undergraduate (Master and PhD) vs. 15K students (split equally between undergraduate and master/PhD
- 5 areas of general education
- 100 academic degree programs vs 200 academic degree programs
- 11 student affairs departments with programs
- Center for Assessment & Research Studies(JMU)
- Teaching Excellence & Education Innovation
- Teaching consultants (design)
- educational technologists (technology)
- assessment team (data informed cycle)
Both centers are dipping their toes into different levels – classroom and programs or programs and classroom as they evolve.
Our approach to assessment is the same
There is a common process and shared principles for assessment for learning improvement for anyone who is doing evidence-based practices.
Outcomes and objectives are used interchangeable in this conversation.
- Outcomes focused – start with measurable objective then building programming and assessment to map to the outcomes
- CMU: wanted to know if VR experiences had a positive effect in learning. Defining learning objectives identifies the data source needed to assess VR experiences
- JMU: information literacy: defining objectives provides the base, upon which everything else is built.
- Alignment & Evidence-based – leverage existing literation on how learning works and collect data on student outcomes to evaluate and feed back into learning strategies
- CMU: students weren’t applying what they learned in the lab to the exams. They might need more “discovery” instead of following directions. Looked to the literature: for evidence-based strategy: inquiry-based learning
- JMU Information literacy embedded into first year communication course to provide substantive content; evidence based through quizzes and through completion of online tutorials
- Ensure the data sources accurately reflect what you want to measure (direct measures are best when possible)