Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

undefined

Student Learning Assessment

Interpreting Assessment Results

Make Data-Driven Recommendations

 

Tips for Analyzing SLO Data

  • How does the data compare to the last time it was collected?
  • What are the strengths represented in the data?
  • Have there been any major changes in the data since the last report?
  • What area(s) failed to meet the anticipated goal(s)? If If assessment data results suggest a need for change, consider the following areas when deciding on what kinds of improvements to make:
    • Course-level changes: Such as increasing guidance for students on a course assignment, revising learning activities, modifying course rubrics, providing more frequent or effective feedback on student progress, increasing assignment rigor
    • Program-level changes: Such as changing program outcomes, adding a prerequisite, aligning to industry certifications
    • Student support: Such as recommending changes to tutoring services, library services, and/or student advising
    • Faculty support: Such as recommending a faculty development course or other type of professional development, supporting faculty in getting updated credentials or industry-relevant training
    • Other: Such as auditing course assignments and activities to ensure alignments between course and program outcomes is accurate, asking a colleague for feedback, collecting more data

Tips for Analyzing Student Course Evaluations

  • How does the data compare to last year?
  • What was the action plan for improvement in the prior year and what is its status?
  • Consider the number of student responses. 
  • Consider where course falls in the sequence of the program.
  • Is the course sequenced within the curriculum appropriately? Is it current and relevant?
  • Look for qualitative comments to provide insights into qualitative data. Look for trends and themes.