Abstract:

The specialized literature in the field of early computer programming learning has analysed the difficulties that students encounter, including abstract reasoning, problem-solving heuristics, and syntax errors. Studies indicate that the greatest difficulty for beginners is to combine the use of basic programming concepts and their effective use in coding. The use of virtual learning environments (VLE) in face-to-face teaching, eLearning and blending courses is a widely studied subject. However, there is little debate about the use of software engineering to design these virtual environments supported in psychological theories of learning and instruction. This article presents some results that were conceived from the practical application of agile software engineering methodologies, supported in the 4C/ID (Four Components Instructional Design) model that integrates the most conclusive results of two psychological theories about human cognition and learning: The Cognitive Load Theory and the Cognitive Theory of Multimedia Learning. The VLE was used to teach Python to computer-aided design software users. The 4C/ID model was used to try to reduce the difficulties in learning computer programming with Python, for students whose first option was not to learn computer programming, but who must learn it because it is part of the curriculum. The 4C/ID model adopts an approach to learning supported in the framework of information processing or cognitive psychology. In this approach, to test whether learning has occurred in meaningfully way, it is necessary to assess the knowledge acquired and also the ability to apply what was learned to new situations and problems. We used an experimental methodology with a quasi-experimental design with control and experimental groups within the same classes. We measure the acquisition and transfer of acquired knowledge and the mental effort. The mental effort scale was applied in two moments: The first time after applying the knowledge test and the second time after applying the transfer test. Mental effort is considered to be the total amount of processed cognitive control in which an individual is involved. An efficient instructional environment is one in which students are able to successfully solve the problems and learning tasks that are given to them with less perceived mental effort. With this procedure we tried to analyse which of the learning environments (conventional teaching method versus 4C/ID model) was more efficient, that is, where students obtained better results in the knowledge and transfer tests and perceived less mental effort. We used the t-Student for independent samples and ANOVA Kruskal-Wallis according to the assumptions of normality and homogeneity of variances. With the results obtained, we generally conclude that there is a difference in the perception of mental effort in favour of the experimental group; also, that the experimental group obtained better results than the control group in tests of knowledge acquisition and transfer of learning. We concluded that the 4C/ID model is a good choice to develop efficient learning environments. That is why it is important to design online learning environments where students succeed with less mental effort. The 4C/ID model was precisely designed to improve the acquisition of the knowledge, skills, and attitudes involved in this complex learning, how is learning computer programming.

Keywords:

Virtual learning environments, software engineering, mental effort, teaching Python, 4C/ID model.