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Notes from Meeting 3 (Nov 19, 2018 3:00pm -4:00pm)

Faculty Interest Group “Using Data to Support Teaching and Learning”

Notes from Meeting 3 (Nov 19, 2018 3:00pm -4:00pm)

We discussed the results of “Becoming College-Ready,” showing some early findings from CUNY Start evaluation. For your convenience, I will include the links of the reports below:

 

Notes from Meeting 2 (Nov 5, 2018 11:30am -12:30pm)

Faculty Interest Group “Using Data to Support Teaching and Learning”

Notes from Meeting 2 (Nov 5, 2018 11:30am -12:30pm)

During this meeting we continued our discussion on data collection, analysis, interpretation and the importance of data-informed decisions for students, faculty, and institution. We discussed the article of Nadasen and List, “Using Community College Prior Academic Performance to Predict Re-enrollment at a Four-Year Online University” and the following items.

  1. How data analysis helped in estimating the impact that first semester grade has on persistence and graduation?

This study examines student learning characteristics and behavior at a community college of 8200 students who transferred to an online a four-year college and their predictability in “persistence” – meaning re-enrollment in the second semester of a four-year college. This early persistence is considered a strong predictor of graduation and is used as a dependent variable. Data was dis-aggregated into: 1) learners characteristics (age, gender, race, and marital status), 2) course taking features, 3) course efficiency (credits earned/credits attempted), and 4) first-term GPA.

  1. Using Data for Detection: We discussed possible ways that Faculty and Administrators can use to detect student failure before it occurs as well as how to learn from successes of other programs.

Items that were not discussed yet and could in the future are:

 –          How can we apply to education the use of Big Data in private sector?

o   Can we use similar techniques to have early detection of student potential failure similarly to fraud analysis/prevention system that banking system uses?

  • Fraud Analysis/Prevention. Data is collected and analyzed. Estimates have provided a predictive model. Use of prediction to prescribe an action: Transactions that are flagged can then be investigated further before payments are made. Banks use big data to flag potential fraud in credit card use.

o   Can we find complement courses or optimal sequence of courses depending on what students have taken before and their performance and satisfaction similarly to “market basket analysis”?

  • Shopping Cart and “Market Basket” Analysis – Many companies realized that data can show retailers shopping patterns of consumers in their industry. If Amazon knows that customers who purchase a specific item also tend to purchase another item, then it can present that second item when the first is bought. — Could such advising be automated or use artificial intelligence (AI) advising?

–          Review the figure to understand how different end users of data can work together.

Attachments

Nadasen, D. & A. List. (2016). “Using Community College Prior Academic Performance to Predict Re-enrollment at a Four-Year Online University.” Online Learning. 20.2.

 

Notes from Meeting 1 (Oct 22, 2018 11:30am -12:30pm)

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.

  1. 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?

 

  1. 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.

 

  1. 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?

 

  1. 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.

Attachments: