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Collaborative learning has been widely acknowledged as a successful teaching method within the education field, with research indicating its positive impact on student outcomes. During the COVID-19 pandemic, when all courses transitioned online due to lockdown measures, many universities employed learning management systems to facilitate continued group work among students. However, forming effective student groups remained challenging, particularly given the large number of enrolled students. To address this issue, this study proposes the application of an artificial intelligence (machine learning) solution to automatically group students based on their behaviours and interactions within an e-learning environment. This paper explores the potential of machine learning (ML) algorithms in assisting educators to create heterogeneous groups, considering various student attributes, such as behaviour and performance, to optimise collaborative learning outcomes. Students' performance within a module was compared using a wiki activity that employed group work over the course of two academic years. In the first experiment, groups were formed randomly, while in the second experiment, students with similar behaviours were firstly identified using a clustering algorithm and then organised by an additional algorithm into heterogeneous groups. The results demonstrate the efficacy of the machine learning solution compared to the random approach in assisting educators with group formation for a collaborative activity such as the wiki, confirmed by a comparative analysis showing an improvement in student performance and satisfaction. This research contributes to the advancement of online education through the creation of more effective group dynamics using machine learning algorithms, thereby improving overall student learning. [ABSTRACT FROM AUTHOR]

University students enrolled in the first year of the Computer Science degree may have problems approaching programming, negatively affecting their study during the course. Tutoring programming projects are very important in helping students with difficulty in learning by providing the right approach to study, improving their knowledge and skills in computing. The aim of this work is to realize a new Java Programming tutoring online course that allows students to have an effective online tool to achieve the learning goals of the course and this will enhance the programming exam pass rate. The course we have designed consists of tools to help students with video tutorials, self-assessment quizzes, code evaluations and exercises to solve using an online Java editor. Because the Moodle platform lacks tools to check the quality of the code syntax, a new software was created. It performs a syntax analysis of the Java code and, as a tutor, automatically provides feedbacks and tips to the students to improve the quality. For each online tool the immediate feedback technique is used to amplify students' engagement. A Clustering Machine Learning technique is performed to identify different students' behaviors. A correlation between them and the final performance showed the most influential features of the completed activities. Quantitative analysis highlighted the effectiveness of the tutoring system and the online course designed in this work to enhance the final exam pass rate. At the end, students filled a questionnaire to report their perception and satisfaction about the course. [ABSTRACT FROM AUTHOR]