Once you have developed assessment measures for routinely collecting course-level student learning data, the next step is to determine what this evidence tells you about student progress toward the learning goals you have set.
The following questions may be helpful during this stage:
- What does the data suggest about what students know or have learned?
- What does the data reveal about students’ abilities to apply the knowledge they have learned?
- What do these data show about what students value?
- Which students do you still not know much about? (what level, majors/non-majors, etc.)
Consider the following in answering the above:
- Use more than one rater to increase reliability when coding qualitative data
- Consider both quantitative and qualitative analysis techniques
- Take care to ensure student confidentiality throughout this process by taking student names off papers and disassociating names from data
- Return to your original questions and issues of investigation to guide your analysis
Here are some additional suggestions for looking at and interpreting the data you collect:
- Direct evidence in the form of course assignments, whether graded or not, can be evaluated specifically with regards to the achievement of course learning goals.
- The general practice for interpreting data from CATs, which are usually anonymous, informally collected, and often knowledge-based, is to look through it all, sort it into piles, making meaning of it by thinking about how it applies to what students are learning or not learning. Then you can think about making changes to your teaching practice, assignments, or course readings
- For MSGF or survey feedback—both examples of indirect assessment—it’s important to look over the results and discuss them with a colleague or CNDLS staff member. It also helps to talk to your class about what changes (if any) you’re making to the course based on their feedback.