Offer acceptance prediction of academic placement. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Shrestha, Raj; Orgun, Mehmet; Busch, Peter
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2351
• Page End: 2368
• Publication Date: 2016-11
• Subjects: Educational data mining -- Enrolment prediction -- Classification; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2085-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract The paper examines the validation of prediction models of acceptance of academic placement offers by students in the context of international applications at a large metropolitan Australian University using data mining techniques. Earlier works in enrolment management have examined various classification problems such as inquiry to enrol, persistence and graduation. The data and settings from different institutions are often different, which implies that in order to find out which models and techniques are applicable at a given university, the dataset from that university needs to be used in the validation effort. The whole dataset from the Australian university comprised 24, 283 offers made to international applicants from the year 2008 to 2013. Every year around 2000–2500 new international students who accept offers of academic placement commence their studies. The important predictors for the acceptance of offers were as follows: the chosen course and faculty, whether the student was awarded any form of scholarship, and also the visa assessment level of the country by the immigration department. Prediction models were developed using a number of classification methods such as logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, k-nearest neighbour, neural networks and their performances compared. Overall, the neural network prediction model with a single hidden layer produced the best result.