This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting, learner-profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age, and gender. Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%), and a decision tree (70%). However, improvements in model accuracy attributable to non-cognitive factors were not significant. Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance, and selfefficacy. Early modelling of first-year students yielded informative, generalizable models that identified students at risk of failing.Keywords: Learning analytics, learner profiling, academic performance, non-cognitive factors of learning, tertiary education INTRODUCTIONEnrollment numbers to tertiary education are increasing, as is diversity in student populations (OECD, 2013;Patterson, Carroll, & Harvey, 2014); however, significant numbers of students do not complete the courses in which they enroll, particularly courses with lower entry requirements (ACT, 2012;Mooney, Patterson, O'Connor, & Chantler, 2010). Factors predictive of academic performance have been the focus of research for many years (Farsides & Woodfield, 2003; Moran & Crowley, 1979), and continue as an active research topic (Jayaprakash, Moody, Lauria, Regan, & Baron 2014;Cassidy, 2011;Wise & Shaffer, 2015), indicating the inherent difficulty in generating accurate learning factor models (Knight, Buckingham Shum, & Littleton, 2013;Tempelaar, Cuypers, van de Vrie, Heck, & van der Kooij, 2013 331Tertiary education providers collect much data on students, including demographic data, academic activity, and log data from online campus activities. As a result, the application of data analytics to educational settings is an emerging and growing research discipline of data analytics (Campbell, deBlois, & Oblinger, 2007;Mirriahi, Gašević, Long, & Dawson, 2014;Sachin & Vijay, 2012;Siemens & Baker, 2012). The primary aim of learning analytics is to provide learning professionals, and students, with actionable information that can be used to enhance the learning process (Siemens, 2012;Chatti, Dyckhoff, Schroeder, & Thüs, 2012). Much of the published work in learning analytics is based on...
Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and therefore mitigate these challenges. The outcome from such data analysis will be dependent on the range, type, and quality of available data and the type of analysis performed. This study reviewed factors that could be used to predict academic performance, but which are currently not systematically measured in tertiary education. It focused on psychometric factors of ability, personality, motivation, and learning strategies. Their respective relationships with academic performance are enumerated and discussed. A case is made for their increased use in learning analytics to enhance the performance of existing student models. It is noted that lack of independence, linear additivity, and constant variance in the relationships between psychometric factors and academic performance suggests increasing relevance of data mining techniques, which could be used to provide useful insights on the role of such factors in the modelling of learning process.
Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.
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