Faculty Interest Group “Using Data to Support Teaching and Learning”
Notes from Meeting 1 (Oct 22, 2018 11:30am -12:30pm)
The meeting started with the question “Why the growing focus on big data analysis?” The digital revolution has been changing the mode and accessibility of learning and teaching, as well as how and what type of data is now collected or could be collected.
- SoTL: We briefly discussed how SoTL projects could start from faculty to analyze their questions. Such projects could bring about changes even lead to standardization of certain projects or data collection. We might not wait for standardization to happen from above, but faculty could start this bottom up approach by running these independent SoTL projects. Things to be discussed in the future is what questions do you (faculty or administrator) have? What data do you need to investigate this? Can other departments help in collecting, aggregating, sharing, and complementing the data?
- Blackboard: Other points for further discussion are what data does Blackboard collect. We briefly discussed that Blackboard measures the time students spend on the completion of a test. There could be more to be discussed regarding data collection through Blackboard in future meetings.
- Detecting Student Failure in Advance (Starfish as an example): The group shared an example of using data to identify potential students that may need help. Such identification will be performed through the collection of student participation (through Starfish) to flag any potential students who might be at risk of failing. Prior research seems to have indicated that lack of participation (in terms of student absence, etc.) would be positively correlated with student lack of success. Based on such revelations from historical data, we will be proactive and try to prevent failure by early identification. Potential future questions are: can we use similar techniques to have early detection of student potential failure similarly to fraud analysis/prevention system that banking system uses? Can we create a success index based on students’ grades and characteristics similarly to credit scores? What data do we need to collect to answer these questions and how would we disaggregate it during our analysis?
- Draft Survey: We shared a draft survey for the group to review and make edits. The objective of this task is to create a survey with simply articulated questions to capture KCC faculty’s needs and understanding on institutional collection of student data and how it would be most meaningful for them; how can data from classroom be integrated with other systems; and how faculty perceives such data aggregation from different systems and then disaggregation during the analysis will improve teaching and learning objectives and decision making in the classroom, department, and college level.
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