Predictive Regression Modeling for Forecasting Graduation Duration in Online Offsite Degree Program

The demand for Information Technology (IT) professionals continues to rise across various sectors, where theyplay vital roles. However, the supply of IT graduates often fails to meet industry needs and this is a huge problem for the SriLankan IT Industry (National IT-BPM Workforce Survey – 2019). In this context, this study presents a predictive regressionmodelling approach to predict graduation duration in the Bachelor of Information Technology (BIT) degree program at theUniversity of Moratuwa, Sri Lanka. It integrates demographic data—student district, birth year, AL results, OL maths grade,gender, employability status, occupation, and AL stream—along with academic performance indicators like diplomacompletions and higher diploma completions. After evaluating the suggested features, the key findings indicate thesignificance of certain features, notably the number of semesters taken to complete the diploma, higher diploma, and thedegree. Additionally, demographic factors such as district, birth year, AL results, OL maths grade, gender, and employabilitystatus were found to be important. The regression analysis was carried out using the Orange data mining tool (Orange DataMining). Various algorithms, including random forest, neural network, linear regression, and k-nearest neighbours (kNN),were used to develop predictive models. By adjusting parameters such as metrics, weights, number of neighbours, numberof iterations, and training dataset size, the models were optimised to better fit the dataset. Training and testing the modelsrevealed consistent error metrics, including MSE, RMSE, MAE, and R^2, validating the accuracy of predictions. Byconsidering the least and reasonable error in each model, the most suitable model to fit the given dataset was selected.The prediction model accurately forecasted graduation duration for subsequent academic batches, demonstrating itseffectiveness in predicting student progress in the program. This research contributes to understanding the factorsinfluencing graduation duration in a distance learning context and provides insights for educational institutions to optimiseprogram planning and student support initiatives. Additionally, it is a good indicator to the companies to gain a betterunderstanding of the availability of future workforce.