Due to the substantial development of information and communications technology, the use of E-learning in higher education has become essential to boost teaching methods and enhance students' learning skills and competencies. E-learning in Software Engineering turns out to be increasingly interesting for scholars. In fact, researchers have worked to enhance modern Software Engineering education techniques to meet the required educational objectives. The aim of this article is to analyse the scientific production on E-learning Software Engineering education by conducting a bibliometric analysis of 10,603 publications, dating from 1954 to 2020 and available in the Scopus database. The results reveal some scientific production information, such as the temporal evolution of the publications, the most prolific authors, institutions and countries, as well as the languages used. Besides, the paper evaluates additional bibliometric parameters, including the authors' production, journal productivity, and scientific cooperation, among other bibliometric parameters. The subject of the current study has not been treated by any previous bibliometric studies. Our research is deeper and more specific; it covers a long period of 66 years and a large number of publications, thanks to the chosen search string containing the different spellings of the used terms. In addition, the literature is analysed using several tools such as Microsoft Excel, VOSviewer, and Python. The research findings can be used to identify the current state of E-learning Software Engineering Education, as well as to identify various research trends and the general direction of E-learning research.

Digital technologies and innovative methods have shown a significant impact on educational systems, and have made work easier for both learners and teachers. Additionally, they have improved the quality and the capability to digitize the assessment of student work produced during a learning process. Assessing and scoring students' UML diagrams has become a challenging task for teachers, especially with the growing number of students, as well as the necessity to better manage their time. Consequently, there will be a necessity to automate the assessment of these learners. This paper presents an approach for assessing and grading automatically the student's UML diagrams. The approach uses an algorithm implemented in Java, which takes the tutor's and student's solution diagrams as input, then provides the student's scores and identifies differences and errors made. Our algorithm was tested and evaluated in a real case within a web platform, and the results obtained demonstrate the effectiveness of our solution.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users’ demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users.

The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student autonomy and efficiency. However, their actual impact on academic performance remains understudied, and the existing research often presents contradictory findings. To address this gap, the present study is the first to employ a Genetic Algorithm (GA) and Multi-Layer Perceptron Neural Networks (MLPNNs) to evaluate the influence of Generative AI Tools (GAITs) on students’ academic outcomes. A structured questionnaire was administered to 294 students from three Moroccan engineering schools in order to collect data on their use of these tools. An initial attempt to predict their grades using a statistical approach showed that familiarity with GAITs contributed positively to academic performance but had limited accuracy (39%), highlighting the need for more robust methods. Therefore, a hybrid model based on neural networks optimized with a GA was developed to better capture the complex relationships between the explanatory variables and academic performance. The results indicate that the GAIT-related variables considered in this study, taken in isolation, have a limited predictive capacity for students’ academic outcomes. This finding suggests that the available data does not capture the full complexity of the factors shaping academic success in contexts involving GAITs use.

The unified modeling language (UML) represents an essential tool for modeling and visualizing software systems. UML diagrams provide a graphical representation of a system's components. Comparing and processing these diagrams, for instance, can be complicated, especially as software projects grow in size and complexity. In such contexts, deep learning techniques have emerged as a promising solution for solving complex problems. One of these crucial problems is the measurement of similarity between images, making it possible to compare and calculate the differences between two given diagrams. The present work intends to build a method for calculating the degree of similarity between two UML class diagrams. With a goal to provide teachers a helpful tool for assessing students' UML class diagrams.