Predicting success, excellence, and retention from students' early course performance

 

DOMM Associate Professor Peter Mellalieu will be travelling to San Francisco, Boston and Miami: 21 July – 4 August 2011. As part of his journey he is presenting two papers at: XXIX International Conference of the International Council for Higher Education. The conference theme is Innovation and Development in Higher Education, Miami/Ft Lauderdale. http://icie.net/v2/miami.php

Peter summarises his presentation:

Higher educational institutions are focussing increased attention on identifying which students are likely to succeed – or fail – in their tertiary studies. Culver (2010, 2011), for instance, reports on the business case for, and services provided by the Noel-Levitz consultancy for improving institutional retention in North America. In New Zealand, government funding for higher education is increasingly being redirected towards a focus on outputs (such as course completions) rather than inputs (student enrollments) (Ministry of Education, 2010).

Anticipating this context, I constructed a prototype Decision Support System (ReXS) to provide my students the means to predict their personal academic success and final grade as they progressed through a first-year (freshman) course ‘Innovation and Entrepreneurship’. Data mining of previous semesters’ course results identified the crucial importance of a student’s ability to write formal academic English as demonstrated in a written case study assignment.

ReXS Decision Support System

Outline of the ReXS Decision Support System

Several students’ immediate reaction to a presentation introducing them to ReXS was unexpectedly enthusiastic and they became ‘early adopter’ users. ‘Late adopter’ users of ReXS also gained confidence in identifying the degree of effort they needed to apply to complete the course succesfully through their Final Test.

Whilst ReXS is a bespoke solution tailored to the particular assessment regime of a particular course, I believe the principles of its design and construction can be applied to any assessed course in higher education. Certainly, my students indicated they would welcome widespread adoption of the approach in other courses in their study program. The presentation provides an opportunity to discuss: Reactions from the student users of ReXS; Illustrations of the predictions made by the ReXS; How the principles underlying the Decision Support System can be extended to other courses; Opportunities for improving the utility of ReXS for students, academic, and administrative staff.

Dashboard for ReXS

The students' dashboard for using the ReXS Decision Support System

 

Citation and full paper

Mellalieu, P. J. (2011). Predicting success, excellence, and retention from students’ early course performance: progress results from a data-mining-based decision support system in a first year tertiary education programme. [Forthcoming] XXIX International Conference of the International Council for Higher Education. Presented at Innovation and Development in Higher Education, Miami/Ft Lauderdale: International Council for Higher Education. Retrieved from http://tinyurl.com/4935qol

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