A workshop for the 2019 Bonner Summer Leadership Institute at Waynesburg University. Presented by Rachayita Shah (Bonner Foundation) and Katie Turek (Ursinus College). This workshop introduces various forms of outcome development, common student learning outcomes for community engagement, and the inquiry-based data lab process.
Introduction to ArtificiaI Intelligence in Higher Education
Outcomes and Assessment for Bonner and Campus Centers
1. Outcomes and Assessment
Rachayita Shah, Community Engagement Scholarship Director, the Bonner Foundation
and
Katie Turek, Assistant Director, UCARE, Ursinus College
5. Today’s Session
● Why do/should we use assessment?
● Recommended assessment practices
● Data Labs
6. Assessment: Rationale
What does it tell us about
students’ intercultural
competence?
What do we still do not
know about students’
intercultural competence?
What could be our next
steps?
7. ● To gauge student learning
• Focus on student “growth” (not
labeling)
● To inform Instruction
• Modify instructional approaches to
maximize learning (E.g. revisiting a
concept)
● To reflect on our effectiveness as
“mentors” “educators”
• Identify ways to democratize and
diversify assessment
Assessment: Rationale
8. Recommended Assessment Practices
1. Aligned
• Linked to short-term and long-term goals / learning outcomes
2. Democratic
• Incorporates student voice: self assessment, peer assessment
• Adopts dialogical approach (dialogue around feedback)
3. Authentic
• Helps explore real-life connections (Capacity-building projects, CBR)
4. Personalized
• Integrates constructive feedback (comments with specific guidance for next steps)
9. What are some
learning outcomes
that you envision
for your Bonners?
1. Assessment Linked to Learning Goals
10. 1. Assessment Linked to Learning Goals
Action-oriented
• Students will understand . . .
• Students will identify two elements of . . .
• Students will learn . . .
• Students will demonstrate learning by describing . . .
12. 2. Democratic Assessment: Student Voice
Reliable for meetings Group Member 1 Group Member 2 Group Member 3
Contributed ideas to the
group
Respected each group
member’s opinions
Fulfilled his/her assigned
role throughout the project
If given the opportunity,
would you work with this
team member again?
In one sentence, what is your
overall impression of each
member’s performance
Peer-Assessment
Do you use it?
In what context?
14. 3. Authentic Assessment
Real-Life Connection
Capacity-Building Project
Community-based
Research
How does your program
promote capacity-
building projects?
16. 4. Assessment: Personalized
How do you make
assessment
personalized?
Informal conversations?
Written feedback?
One-on-one meetings?
17. Data Labs: Assessment “Experts” at Play
Goals
● Reclaim assessment as
something we do and
own
● Reimagine assessment
in the context of the
values we already hold
in our community-
engaged work
18. In Data Labs...
… stakeholders with a common investment (in a program, center, class,
experience, etc.) come together to look carefully at artifacts (data) that
emerge from the program, and to ask a series of guided questions about
these artifacts
Considerations
● Themes mark data labs as a different space
● Anyone can participate
● Can be very focused
● Spend time digesting artifacts
19. What if . . .
Assessment were a High Impact Practice?
•Involves meaningful effort
•Helps staff and faculty build substantive relationships with each other
•Engages staff and faculty across differences
•Provides staff and faculty with rich feedback about their work
•Helps staff and faculty apply and test what they are learning about student
learning to their programming and teaching
•Provides opportunities for staff and faculty to reflect on the people their
students are becoming
22. Instructions
● There are three stations, each one containing reflections on a different
prompt
● Visit at least 2 stations
● At each station, follow the printed instructions using the artifacts (student-
generated reflections)
● Keep your scrap paper and worksheets with you. This independent inquiry
will last for 20 minutes.
● Move between stations at your own pace.
23. Closing/Reflection
● Main themes/takeaways from Data Labs
● Can you see yourself implementing Data Labs
or something similar?
● Best practice sharing, concerns, questions