A Decision Support System for predicting Retention, eXcellence, & Success: The Dashboard for ReXS:
Late November, I began a journey exploring how I could apply data mining/machine learning to help predict students’ performance throughout their semester of studies. The intention is to gain information within the first few weeks of the semester and develop prognoses for their future performance.
Over the last few weeks I have been testing, calibrating and experimenting with a spreadsheet-based Decision Support System assembled on the relationships I identified from my analyses of last semester’s student performance data.
The image shows the dashboard that a student uses to ‘drive’ the DSS. The cells in green show where the user enters data, including:
- The grade they achieved for a specific assignment
- The actual class mean and standard deviation for a specific assignment. If the data is not available, the statistics from a prediction dataset are used.
The DSS displays several outputs for the user:
- Under MY GRADES (in orange): the per cent mark, letter grade, and weighted contribution to the final course grade - nothing special there!
- Under CLASS PERFORMANCE (in blue): an estimate of the student’s relative placing in the class, measured by the per cent of students below them in the class. This is derived simply from their mark and the class statistics (assuming a normal distribution of grades)
- Under PREDICTED GRADES (Yellow): an estimate for the student’s grades for all outstanding assignments in the course. This also includes a prediction of the student’s overall relative placing in the class. This is the ‘magic’ part of the DSS.
In addition, the student can enter their actual grades and conduct ‘what if’ analyses to explore what grade for assignment components they need to achieve to gain their target grade for the course as a whole.
Tabs at the bottom of the DSS dashboard permit the user to explore the inner workings of the DSS. These inner workings include the prediction equations resulting from the machine learning data analysis: Predictors, Class stats, etc. The Babbage tab is a giant spreadsheet that shows all the calculations in full.
Mellalieu, P. J. (2010, December 6). A Decision Support System for predicting success, excellence, and retention from students’ early course performance: a machine learning approach in a tertiary education programme in innovation and entrepreneurship: Part 1: Project summary. Innovation & chaos … in search of optimality. Retrieved January 10, 2011, from http://pogus.tumblr.com/post/2110849868/a-decision-support-system-for-predicting-success
Download the Rexs .xls spreadsheet here
Mellalieu, P. J. (2011, April 26). ReXS: Decision Support System for Retention, eXcellence, and Success (.xls spreadsheet). Peter Mellalieu - Teacher
. Retrieved April 26, 2011, from http://preview.tinyurl.com/rexsdss1-2
Culver, T. (2010, January 21). Setting student retention goals and developing student retention strategy. Noel-Levitz. Retrieved January 23, 2011, from http://blog.noellevitz.com/2011/01/21/6-keys-setting-realistic-student-retention-goals-developing-successful-student-retention-strategy/ Culver, T. (2011). Mid-Year Retention Indicators Report for Two-Year and Four-Year, Public and Private Institutions: Benchmark Research Study Conducted Fall 2010. Higher Ed Benchmarks. Noel-Levitz. Retrieved from https://www.noellevitz.com/NR/rdonlyres/C72E0608-2024-4175-9A94-9669C394253E/0/2011MIDYEARINDICATORSREPORT.pdf