A Case Study on Learning visual programing with TutoApp for Composition of Tutorials: An approach for Learning by Teaching

Abstract

Teaching programming is a topic that has generated a high level of interest among researchers in recent decades. In particular, multiple approaches to teaching visual programming have been explored, from the use of tools such as Scratch, robots, unplugged programming or activities for the development of computational thinking. Despite the wide range of resources used, students generally tend to perform poorly academically and perceive learning visual programming as a complex and demotivating task. In this article, the TutoApp system is proposed together with a new methodology based on “Learning by Teaching”, where students create tutorials in their mobile devices to explain programming concepts to their peers. The hypothesis of this paper is that the use of this tool improves learning outcomes and the level of student satisfaction. An experiment with a pre-post-test design has been carried out with 57 university students in an introductory programming course, 30 belonging to a control group (did not use TutoApp) and 27 belonging to the experimental group (used TutoApp). The findings indicate that the creation of tutorials with TutoApp significantly improved students’ academic performance over those who did not use it, specifically in learning the loops and conditional control structures. However, it was observed that anxiety increased in all students while learning visual programming. The results of this study open the door to the validation of the use of systems and methodologies for creating tutorials for teaching visual programming to university students.

Isaac Lozano-Osorio

Phd in Artificial Intelligence

Isaac Lozano graduated with a double degree in Computer Engineering and Computer Engineering from the Universidad Rey Juan Carlos, where he was awarded the prize for the Best Final Project. Subsequently, he completed a Master in Artificial Intelligence Research (UIMP). His main research interests are focused on the interface between Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristic procedures for graph modeled optimization problems.