Introductory programming courses at university are currently experiencing a significant dropout and failure rate. Whilst several reasons have been attributed to these numbers by researchers, such as cognitive factors and aptitude, it is still unclear why programming is a natural skill for some students and a cause of struggle for others. Most of the research in the computer science literature suggests that methods of teaching programming and students’ learning styles as reasons behind this trend. In addition to the choice of the first programming language taught.
With the popularity of virtual learning environments and online courses, several instructors are incorporating these e-learning tools in their lectures in an attempt to increase engagement and achievement. However, many of these strategies fail as they do not use effective teaching practices or recognise the learning preferences exhibited by a diverse student population. Therefore this research proposes that combining multiple teaching methods to accommodate different learners' preferences will significantly improve performance in programming.
To test the hypothesis, an interactive web based learning tool to teach Python programming language (PILeT) was developed. The tool’s novel contribution is that it offers a combination of pedagogical methods to support student’s learning style based on the Felder-Silverman model.
First, PILeT was evaluated by both expert and representative users to detect any usability or interface design issues that might interfere with students’ learning. Once the problems were detected and fixed, PILeT was evaluated again to measure the learning outcomes that resulted from its use. The experimental results show that PILeT has a positive impact on students learning programming
