A1 Journal article – refereed
Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model




List of Authors: Veerasamy Ashok Kumar, Daryl D’Souza, Rolf Lindén, Mikko-Jussi Laakso
Publisher: AJEEL
Publication year: 2019
Journal: Asian journal of education and e-learning
Journal acronym: AJEEL
Volume number: 7
Issue number: 1
eISSN: 2321 – 2454

Abstract

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.


Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2019-21-08 at 21:19