In recent years, Aspect Based Sentiment Analysis (ABSA) has gained significant importance, particularly for enterprises operating in the commercial domain. These enterprises tend to analyze the customers’ opinions concerning the different aspects of their products. The primary objective of ABSA is to first identify the aspects (such as battery) associated with a given product (such as a smartphone) and then assign a sentiment polarity to each aspect. In this paper, we focus on the Aspects Extraction (AE) task, specifically for the French language. Previous research studies have mainly focused on the extraction of single-word aspects without giving significant attention to the multi-word aspects. To address this issue, we propose a hybrid method that combines linguistic knowledge-based methods with deep learning-based methods to identify both single-word aspects and multi-word aspects. Firstly, we combined a set of rules with a deep learning-based model to extract the candidate aspects. Subsequently, we introduced a new filtering algorithm to detect the single-word aspect terms. Finally, we created a set of 52 patterns to extract the multi-word aspect terms. To evaluate the performance of the proposed hybrid method, we collected a dataset of 2400 French mobile phone comments from the Amazon website. The final outcome proves the encouraging results of the proposed hybrid method for both mobile phones (F-measure value: 87.27% for single-word aspects and 82.38% for multi-word aspects) and restaurants (F-measure value: 78.79% for single-word aspects and 76.04% for multi-word aspects) domains. By highlighting the practical implications of these results, our hybrid method offers a promising outlook for Aspect Based Sentiment Analysis task, opening new avenues for businesses and future research.

Machine learning-based prediction models are valuable prediction tools for assessing university performance as well as decision support tools for university governance and higher education system design. The prediction of student outcomes to enhance learning and teaching quality is one subject that has attracted considerable attention for different purposes. The first objective of this study is to develop and validate a prediction model using Machine Learning algorithms that predict students' outcomes in the case of Moroccan universities based only on the outcomes of courses taken in the previous semesters of university studies. This prediction model can be used as a basis for many subsequent studies on different aspects of higher education such as governance, pedagogy, etc. As a first application, we explore the responses of this prediction tool to analyze the outputs of the online learning experience that took place during the Covid-19 pandemic period. To achieve this, four machine learning algorithms are tested such as J48 decision tree, Random Forest, Multilayer Perceptron, and Naïve Bayes. The experimentations are developed by using Weka and the two metrics “accuracy” and “ROC Area” enable to assess the predictive performance of the models. The obtained results show that the Random Forest-based model provides superior results, as evidenced by its accuracy-ROC area, which reached an accuracy of 90% with a ROC Area of 95%. The use of this model to explore the outcomes of the distance learning experience taken during the Covid-19 pandemic, reveals a failure in the prediction performance of the model during the Covid-19 pandemic period, which indicates a change in the system's behavior during this period when teaching moved to the full online version in the year 2019/2020 and returned fully face-to-face in the 2021/2022 year. The failure in the machine learning algorithms' performance when the system changes its behavior can be a limitation of using prediction models based on machine learning in this context. On the other hand, these models can be used if they are properly designed to identify changes in the behavior of a system as shown in this study. Therefore, the proposed Random Forest-based model has the capability to forecast student outcomes accurately and can be applied for diverse analyses within the Moroccan education system. These analyses include but are not limited to identifying students at risks, guiding student orientation, assessing the influence of teaching approaches on student achievement, and evaluating training effectiveness, among others.

This research study aims to compare the learning achievements of first-year students in an algorithm and programming course before and after participating in cooperative blended learning activities focused on variables, expressions, and control commands. By utilizing problem-based learning methods, the researchers sought to meticulously analyze the profound impact of these activities on students’ academic advancement. The research tools deployed encompassed satisfaction questionnaires and achievement tests. The research cohort encompassed seven experienced specialists within higher education institutions, each endowed with a minimum of ten years of pedagogical experience, along with twenty-five participating students. Employing rigorous statistical analysis via T-tests, the study conclusively revealed a statistically noteworthy enhancement in student achievement post the program, underscoring the affirmative influence of cooperative blended learning activities. Moreover, the overall satisfaction level among learners engaging in the proposed learning activities was remarkably elevated, evident through an average satisfaction rating of 4.54 and a standard deviation of 0.73. These empirical insights succinctly underscore the demonstrable effectiveness of assimilating cooperative blended learning methods within algorithm and programming education, thereby accentuating the pivotal role of these pedagogical approaches in shaping contemporary educational practices.

Emotional intelligence (EI), the ability to manage emotions, empathize, and regulate one's behavior, is essential for every member of society nowadays. Various social institutions, including these at the educational institutions, influence its development. The paper examines the relationship between solving communicative tasks and developing students' emotional intelligence and describes the features of students' native language training. Here, we are providing examples of analytical, associative, and research communicative tasks. The tasks involve communicative activities, emotional and sensory perception of the text, and the involvement of analytical and creative abilities. A pedagogical experiment was conducted to confirm the development of EI in the process of solving communicative tasks. Hall's methodology determined the level of EI formation. The total number of respondents was 156 (control group – 77 people, experimental group – 79 people). Three control surveys were conducted in March (at the beginning of the experiment), June (in the middle of the experiment), and December (at the end of the experiment) of 2022 to track changes in the development of EI. The empirical results were subjected to statistical analysis. Pearson's criterion confirmed the normality of the distributions, and Student's criterion showed the statistical significance of changes at the end of the experiment for all indicators and for the indicators "emotional awareness," "managing one's emotions," and "empathy" in the middle of the experiment. The study confirmed a new method of developing EI - the use of  
communicative tasks in teaching the native language, which extends the existing research results on the development of EI in learning foreign languages. The study lays grounds for several conclusions, including that solving communicative tasks may model "life situations" and indirectly form models of the future behavior of young people. It also identifies an urgent need for special courses for teachers on developing skills to create communicative tasks; it is essential to modernize teacher training programs, which should include the development of skills to model situational emotionally colored tasks that do not have an unambiguous answer and require analysis, comparisons, and evaluations.

This work proposes a Query Searching Based Ranking Summarization Data Retrieval (QS-RSDR) method for document summarization based on information retrieval. QS-RSDR ranks query-based retrieval of important information, enabling the creation of a detailed report of information requirements using generated sections of sample documents. Relating Keyword Query Search Summarization (RKQSS) generates the main summary from the most relevant document in the query and then the summary from the other documents. The method resolves similarity terms related to the query using the Word Frequency (WF) method. Sentence ranking weights and sentence frequency improve the accuracy of the retrieved documents. Simulation results show improved accuracy in information retrieval. The proposed method can help address unclear and short queries and understand the nature of the required information behind the query. The paper concludes that QS-RSDR is an effective solution for document summarization based on information retrieval using the query search ranking method. 

This paper delves into the transformative potential of artificial intelligence (AI), particularly focusing on ChatGPT, within educational realms. By conducting an exhaustive review across various scholarly publications and case studies, this research unveils ChatGPT’s multifaceted role in redefining educational landscapes–ranging from enhancing programming proficiency and fostering creativity in writing, to augmenting student engagement. Our findings illuminate the dual-edged influence of ChatGPT in education, showcasing not only its ability to tailor learning experiences and facilitate programming and creative writing but also its capacity to fortify student-teacher interactions. However, the study does not shy away from highlighting the intricate challenges that accompany the integration of AI in education, including concerns over academic integrity, ethical considerations, and the need for a balanced amalgamation with traditional pedagogical methods. Innovatively, this research proposes a forward-thinking, ethical framework for AI integration in educational settings, advocating for a harmonious blend of ChatGPT’s capabilities with human educators' insights to foster a more engaging, effective, and equitable learning environment. By introducing groundbreaking strategies for integrating interactive learning technologies with ChatGPT, and emphasizing the development of personalized educational trajectories, our study sets a new benchmark for future AI applications in education. The paper’s exploration into the innovative integration of ChatGPT with Virtual Reality (VR) offers a glimpse into the future of immersive learning experiences, opening new avenues for engaging and experiential learning. Through empirical validation and a nuanced discussion on the ethical deployment of AI tools in education, this study marks a significant contribution to the discourse on AI’s role in education, providing valuable insights for educators, policymakers, and technologists alike.

The article is dedicated to solving the problem of modeling and developing a computer simulator with the creation of working scenarios for training operating personnel in object detection. The analysis of the features of human operator activity is carried out, the model of his behavior is described, and it is shown that for the presented task, the following three levels must be taken into account: behavior based on abilities (skills), behavior based on rules, behavior based on knowledge. User models that are used in man-machine systems were created, and their use in the process of modeling operator activity from the point of view of regular and irregular exposure was shown. This made it possible to create a prototype of a graphical window using a user-friendly interface. A system model of human-machine interface for processing and recognition of visual information is mathematically described and a model of image representation based on three possible scenarios of their formation is formed. The result of the study was the software implementation of an effective educational tool prototype that accurately replicates real-world conditions for the formation of working scenarios. The conducted experimental research showed the possibility of general image recognition tests, selection of different test modes, and support for arbitrary sets of image test tasks. Further research will be aimed at expanding the  
functionality of the created prototype, developing additional modules, automatically generating scenarios and verifying work.