Predicting Higher Education Student's Aptitude to Learn to Program: A Systematic Literature Review
João Pires, Anabela Gomes, Ana Rosa Borges, Fernanda Brito Correia · 2024
The inherent complexity of Introductory Programming contributes to the persistently high failure and dropout rates among Higher Education students. Several studies have been conducted to find possible solutions to this problem. Through this study, a systematic review was carried out where factors that influence the learning of programming concepts were analysed, as well as techniques to anticipate student's aptitude and practical strategies to support student's learning process to contribute to a deeper understanding of this problem and help in the search for a solution, or a path to reach a solution. The systematic review extracted 525 candidate papers, of which 49 were analysed, as the rest did not meet the established inclusion and exclusion criteria. The analysis of the results suggested that self-efficacy and prior knowledge are the most common factors identified that influence student performance. Data mining emerges as the most relevant aptitude prediction technique. At the same time, active learning, pair programming, and flipped classes appear to be promising strategies to help in the process of learning to program. These findings have significant implications for educators and professionals involved in teaching programming in higher education institutions, offering valuable insights for improving the quality and effectiveness of these courses.