Creating Wraparound Supports for Students through Internal Partnerships
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Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
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Creating Wraparound Supports for Students through Internal Partnerships
- 1. Wraparound Supports through Internal Partnerships
- 2. Jeremy Anderson Deputy Chief of Analytics & Technology Transformation @ATechJAnderson Low-cost, high quality Mainly online, 1600 students Adult, working, parents Socially & financially diverse Outcomes +20% over average
- 3. If you hear something you like, or have an idea #QMEastRegional #StudentSupport @ATechJAnderson
- 4. Our session objectives (1) Identify the relative areas of expertise and strength of advising, IT, and IR teams in supporting student success. (1) Summarize two projects that leveraged the strengths of these groups to produce wraparound student supports at an institution. (1) Consider potential for such collaboration in diverse contexts.
- 9. Icons from FlatIcons Student Support Advise students Intervene Mentor Design programs Connect to services
- 10. Icons from FlatIcons IR Govern data Analyze Report Predict
- 13. Framework in Practice Predictive Dashboard
- 14. We’d like to be able to reach out to students most at risk so that we can maximize our limited staff and improve persistence rates.
- 15. We have longitudinal LMS, SIS, and ALS data to help us determine what makes a student “at risk” and predict this state.
- 16. We can extract the source data, connect it to student ID, run the predictive calculations, and present the risk scores in a dashboard.
- 18. MLSDev
- 19. Questions on example 1
- 20. Framework in Practice Course Performance
- 21. We designed a bunch of courses and they’ve been running for a while. How well are students learning?
- 22. We can aggregate students’ performance on graded work across multiple sections and apply descriptive statistics.
- 23. We can extract the grading information and connect it to course identifiers, run the calculations, and present in a dashboard.
- 25. Questions on example 2
- 26. Let’s brainstorm and share!
- 27. bit.ly/datasourceQM What are your data sources?
- 28. bit.ly/it-irQM How can IR and IT help us?
- 29. bit.ly/StartSmallQM Where can we start?
Editor's Notes
- My span: Oversee ID, analytics/data, IR, and online support TAWC 90 to 95% of enrollments are online Very diverse: first-gen, Pell-eligible, racially and ethnically diverse 6-year grad rates in 65% range, which is over 20% above NSC averages for this market segment 60% report a raise, 40% a promotion A big reason for our success has been data-driven wraparound student supports
- IR can work with stakeholders to determine what data we have to answer our key questions about students and courses IT knows databases SMEs like advising team know local data sources IT will help bring it all together, structure it, build a relational database, prepare data to push out IR and analysts can work with SMEs to develop dashboards, reports, statistical evaluation, predictive models
- Team of dedicated professional advisors Caseload around 250 students Working in a CRM - Salesforce Students think of them as their primary points of contact for 4 years Helps with: Orientation and onboarding All matters of registration Moral support Tips and tricks and best practices of learning OL Facilitate Fb community Connect students to services This group needs to be very efficient given all of the roles it plays Especially important to know which services a student might need since first gens can find it difficult to express Even if your institution relies on academic advisors, the same types of insights will be helpful.
- IR knows what kind of quantitative data we have that could be useful for understanding which students most need help and when Data like: Post counts in discussions - fewer posts compared to class or fewer posts compared to your usual behavior, e.g. Timeliness of assignment submissions Missing assignment counts Post length Assignment scores Mastery levels and completion percentage in adaptive assignments Predictions made via SPSS, SPSS Modeler, Veera, Orange Analytics Your IR office may prefer more open source approaches like developing in R or Python
- Connected source tables of discussions, assignments, etc. Built a view that was comprised of calculations that would produce each of the the predictors A dashboard was then built on top of the data view Users can sort and focus on different data elements Absent a CRM, faculty or advisors would use this to shape advising meetings and to direct attention to students who could benefit from outreach
- Presents the six powerful predictors broken out for each student and then each student gets an aggregate score
- Key concept for us is agile development Was designed by collaboration of the three teams. All three teams gather, advisors explained their goals: requirements IT and IR collaborate on a wireframe: design Advisors react, IT and IR tweak: develop Advisors try it out, IT and IR fix: testing Example was predictive scores weren’t making sense, so IR helped translate the scores into predictive ranges and redesigned visual Advisors use it and give feedback IT and IR review and make the next improvement In this case, we want to be able to download data to bulk create cases in CRM
- Another place where advisors, other frontline staff, instructional designers, and faculty raise their hands is to ask how well our courses are designed. Starts with defining what we mean. We use the QM rubric and CAST rubric for design evaluations We gather evidence of student engagement and learning We consider cost savings and access
- IR helped hone in on the most easily measured elements: engagement and performance in courses Basic descriptive stats were useful: Mean, distributions, SDs Median, max, min, IQRs Helped define KPIs in relation to staff input
- Develop a dashboard IT already had the data in the warehouse, just needed to recontextualize it Built a course-driven view using the course ID rather than the student ID Translated the statistics and KPIs that IR and staff shared Built a dashboard that rides over the data model
- Walk through Distribution of grades Average grades for course and assignment Counts of participations by discussion Frequency of on-time and late submissions Total enrollments