The Effectiveness of a Learning Environment based on AI, Connectivism Theory in Developing Programming Skills among the Ninth-Grade Female Students

Abstract

The current study aimed to investigate the effectiveness of an artificial intelligence-based learning environment grounded in Connectivism Theory in developing programming skills among ninth-grade female students. To achieve the study objective, a quasi-experimental approach was adopted. The study sample consisted of 53 female students who were assigned to two groups: a control group consisting of 26 students who were taught using the conventional method, and an experimental group consisting of 27 students who were taught using an artificial intelligence-based learning environment grounded in Connectivism Theory. To collect the study data, a programming skills test was developed, and its validity and reliability indicators were verified. The results revealed statistically significant differences at the significance level (α ≤ 0.05) between the mean scores of the experimental and control groups on the programming skills test, attributable to the teaching method using the artificial intelligence-based learning environment grounded in Connectivism Theory. This confirms the effectiveness of an artificial intelligence-based learning environment grounded in Connectivism Theory in developing programming skills among ninth-grade female students. The effect size was also found to be (η² = 0.218), indicating a large effect size. The study recommended employing artificial intelligence-based learning environments in teaching programming across different educational levels due to their effectiveness in developing programming skills. It also recommended integrating the principles of Connectivism Theory into programming instruction by providing a learning environment that encourages students to interact, participate actively, exchange knowledge, and establish connections among digital learning resources.