There appears to be a tendency for the strategies and methods that have been offered in OOP course learning to affect the improvement of individual skills only. There is a significant need for learning strategies which are relevant and able of improving collaborative working skills. The purpose of this study is to develop a Collaborative Learning and Programming model suitable for Object-Oriented Programming courses and assess its validity, practicality, and effectiveness. The implementation of the CLP model was conducted using the ADDIE development procedure by involving 7 experts, 35 experimental class students, 23 control class students and 4 lecturers of the Object-Oriented Programming course. The survey results showed that the CLP model was valid, practical, and effective in achieving these goals. The validity test results were verified based on experts' assessment, indicating that the aspects contained in the CLP model were valid with an Aiken's value V =0.89. The practicality test results indicated that the model was highly practical with the practicality value of 89.95% from students and 89.67% from lecturers. Finally, using the CLP model demonstrated its effectiveness in reducing the abstraction and complexity of OOP courses and improving student collaboration, particularly in programming tasks. The significance of conducting this survey is that it provides evidence for the effectiveness of the CLP model in achieving its intended goals and can inform the development of future OOP courses and programming tasks. The survey was conducted well, as it used both expert assessment and student and lecturer feedback to assess the validity, practicality, and effectiveness of the CLP model.

Given the significance of online education, a recommendation system provides a good opportunity to advise the most suitable courses according to their interest and preferences. This study proposes an academic training course recommendation that applies machine learning algorithms to provide the most appropriate 21st century learning based on individual preferences.  To address the issue of imbalanced classification, the eight development skills are grouped into three skill categories during the preprocessing stage. In the classification step, several machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, and Backpropagation Neural Network, are used to create a predictive model, which is then compared to the results of Logistic Regression. These machine learning algorithms predict the skill group based on the teacher preference data, which results in the suggestion of training courses that are customized to the teacher's profile. According to the experimental results, all machine learning algorithms showed superior prediction performance than Logistic Regression. The Backpropagation Neural Network exhibits high precision, reaching up to 78%, and demonstrates the best performance for the testing data. This research demonstrates that machine learning algorithms significantly improve the accuracy and efficiency of the training course recommendation. On this basis, this training course recommendation system will be advantageous to both the teachers looking for up- and reskilling training courses for 21st century learning. Additionally, it will be appropriate for training course designers to establish training courses that develop 21st-century learning in accordance with participants’ interests and professional development.

This article introduces a novel approach to data clustering based on the oscillatory chaotic neural network with dipole synaptic connections. The conducted research affirms that the proposed model effectively facilitates the formation of clusters of objects with similar properties due to the use of a slowly decreasing function of the dipole synaptic strength. The studies demonstrate that the degree of neuron synchronization in networks with dipole synaptic connections surpasses that in networks with Gaussian synaptic connections. The findings also indicate an increase in the interval of the resolution range in the model featuring dipole neurons, underscoring the effectiveness of the proposed method.

The Question-Answering (QA) approach represents one of the most significant Natural Language Processing (NLP) tasks that depends on language input. In terms of morphology & adhesive structure, Malayalam is a resource-constrained indigenous language of India. These linguistic features make QA in Malayalam particularly difficult. This study uses a subset of 5 tasks from the Facebook bAbI dataset to present a subset of five assignments from the Facebook bAbI dataset; this study presents a Malayalam Question Answering Solution that utilizes a Deep Learning (DL) hybrid framework combining CNN and Bi-LSTM Methods. We believe this is the initial time a hybrid-based deep learning framework has been used for the Malayalam question-answering technology. In the first iteration of the method, high-level semantic characteristics are extracted utilizing a Convolutional Neural Network (The Bi-LSTM tier then extracts the contextual feature representation of the text using the feature extraction result. Finally, use the softmax activation function to predict correct answers for corresponding questions. The proposed model is both functional and systemized in terms of classification accuracy, precision, recall, and F1 scores. The simulation results show that the proposed hybrid CNN and Bi-LSTM model outperform the existing models in terms of classification with more than 91 % accuracy for all five tasks.

Some of the most common and typical issues in the field of intelligent tutoring systems (ITS) are (i) the correct identification of learners’ difficulties in the learning process, (ii) the adaptation of content or presentation of the system according to the difficulties encountered, and (iii) the ability to adapt without initial data (cold-start). In some cases, the system tolerates modifications after the realization and assessment of competences. Other systems require complicated real-time adaptation since only a limited number of data can be captured. In that case, it must be analyzed properly and with a certain precision in order to obtain the appropriate adaptations. Generally, for the adaptation step, the ITS gathers common learners together and adapts their training similarly. Another type of adaptation is more personalized, but requires acquired or estimated information about each learner (previous grades, probability of success, etc.). Some of these parameters may be difficult to obtain, and others are imprecise and can lead to misleading adaptations. The adaptation using machine learning requires prior training with a lot of data. This article presents a model for the real time automatic adaptation of a predetermined session inside an ITS called AI-VT. This adaptation process is part of a case-based reasoning global model. The characteristics of the model proposed in this paper (i) require a limited number of data in order to generate a personalized adaptation, (ii) do not require training, (iii) are based on the correlation to complexity levels, and (iv) are able to adapt even at the cold-start stage. The proposed model is presented with two different configurations, deterministic and stochastic. The model has been tested with a database of 1000 learners, corresponding to different knowledge levels in three different scenarios. The results show the dynamic adaptation of the proposed model in both versions, with the adaptations obtained helping the system to evolve more rapidly and identify learner weaknesses in the different levels of complexity as well as the generation of pertinent recommendations in specific cases for each learner capacity.

In the context of growing and widespread demand for higher professional education, an important aspect is the quality of the educational process and the effectiveness of its results. The educational process is a complex socio-economic phenomenon involving at least two parties – the teacher and the students. It is these subjects of the process that are the key elements and interact, as a result of which there is an increase in the amount of information that is transformed into a competency-based form. It is the acquisition by students of the competencies they need in their professional activities that is the goal of the educational process. As a result of the study, it was found that it is possible to optimize the educational process and fill it with the necessary educational elements through fuzzy modeling of the transmission and perception of educational information. The article proposes a model of fuzzy interaction between a teacher and a student group. The model is designed to determine the possibilities of optimizing the educational process and the optimal combination of educational elements. Educational elements include teaching materials, communication tools, and digital technologies. With the help of these elements and the pedagogical strategy, the transfer of educational information to students is carried out. The same means and motivation are used for the perception of educational information. It is important to find the optimal combination of educational elements and tools that contribute to the effective transfer and assimilation of educational information. Improving the effectiveness of interaction between teachers and students affects the quality of education. The quality of the educational process determines the level of students' training, the success of graduates' professional activities, and the professional self-realization of teachers. The interaction model makes it possible to improve the quality of the educational process. This is confirmed by the results of its application at Tver State Technical University.

This paper provides a comprehensive overview of Deaf Education in Morocco documenting its historical evolution and systematically assessing current instructional methodologies. With a focus on learning and teaching environments, the study aims to offer a wide understanding of the educational opportunities, teaching methods, and teacher training programs within Moroccan schools serving the Deaf community. The research questions guide the inquiry addressing historical paths, the influence of teaching methods, and common challenges. By identifying challenges and evaluating practices, the research makes methodological and theoretical contributions to the fields of special education and Deaf education in Morocco. This foundational resource, which is lacking in Moroccan research, serves as a basis for future investigations into instructional approaches. The study navigates through Morocco’s educational history from colonial impact to post-independence reforms emphasizing challenges like pedagogical strategies, infrastructure limitations, and social integration issues. The findings confirm the importance of shifting negative attitudes, fostering inclusivity, and reassessing policies to enhance the educational journey for Deaf learners in Morocco.