Transfer-Learning Methods in Programming Course Outcome Prediction

Abstract

The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in educational contexts, differences between courses set huge challenges for the generalizability of these methods. For example, traditional machine-learning methods assume identical distribution in all data—in our terms, traditional machine-learning methods assume that all teaching contexts are alike. In practice, data collected from different courses can be very different as a variety of factors may change, including grading, materials, teaching approach, and the students. Transfer-learning methodologies have been created to address this challenge. They relax the strict assumption of identical distribution for training and test data. Some similarity between the contexts is still needed for efficient learning. In this work, we review the concept of transfer learning especially for the purpose of predicting the outcome of an introductory programming course and contrast the results with those from traditional machine-learning methods. The methods are evaluated using data collected in situ from two separate introductory programming courses. We empirically show that transfer-learning methods are able to improve the predictions, especially in cases with limited amount of training data, for example, when making early predictions for a new context. The difference in predictive power is, however, rather subtle, and traditional machine-learning models can be sufficiently accurate assuming the contexts are closely related and the features describing the student activity are carefully chosen to be insensitive to the fine differences.