Approaches to learning in computer programming students and their effect on success

Abstract

Within education research there has been sustained interest in developing models that can predict, or alternatively explain, student success. In computing education, attempts have been made to predict success in programming courses. Models previously used in this area have included a range of demographic, cognitive and social factors. These models emphasise presage factors. Biggs' 3P general model of student learning, by comparison, measures attitudinal factors. This multi-national, multi-institutional study investigates the effectiveness of an attitudinal measure, deep and surface approaches to learning (Biggs R-SPQ-2F questionnaire), to explain the success of students in introductory programming courses. This is then compared to both a cognitive and a demographic measure. The results indicate that across the eleven institutions in three countries the strongest correlation to success was found with the learning approach.