Analyzing student performance in Introductory Programming courses in Higher Education is crucial for early intervention and improved academic outcomes. This study investigates the predictive potential of a Programming Cognitive Test in assessing student aptitude and forecasting success in an Introductory Programming course. Data was collected from 180 students, both freshmen and repeating students, enrolled in a Computer Engineering program. The dataset includes the Programming Cognitive test results, background variables, and final course outcomes. To identify latent patterns within the data, the K-means clustering algorithm was applied, focusing particularly on freshmen students to avoid bias from prior programming exposure. In parallel, six Machine Learning classification models were developed and evaluated to predict students’ likelihood of passing the Introductory Programming course: Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, Support Vector Machine, and Deep Neural Network. Among these, the Deep Neural Network model demonstrated superior performance, achieving the highest values across key metrics—Accuracy, Recall, and F1-score—effectively identifying students at risk of underperformance. These findings underscore the potential of this model in educational settings, where timely and accurate detection of struggling students can enable proactive, targeted interventions. 
This work contributes to the field by combining cognitive assessment with predictive modelling, offering a novel approach to forecasting programming performance. The models and methods described are adaptable for broader educational applications and may assist educators in refining teaching strategies and improving retention and success rates in programming education.

The Admission Point Score (APS) metric, commonly used to admit prospective students into academic programmes, may appear effective in predicting student success. In reality, almost 50% of students admitted to a science programme in a higher education institution failed to meet all the requirements necessary to complete the programme during the period of 2008 and 2015. This had a direct impact on the overall graduation throughput. This research therefore focuses on adopting a probabilistic-graphical approach as a viable alternative to the APS metric for determining student success trajectories in higher education. The purpose of this approach was to provide higher education institutions with a system to monitor students’ academic performance en-route to graduation from a probabilistic and graphical point of view. This research employed a probability distribution distance metric to ascertain how close the learned models were to the true model for varying sample sizes. The significance of these results addressed the need for knowledge discovery of dependencies that existed between the students’ module results in a higher education trajectory that spans three years.

This study delves into the interface between Rhetoric and Artificial Intelligence, with a specific focus on ChatGPT's ability to engage in argumentative dialogues and its potential educational applications. Specifically, the study aims to investigate the feasibility of conducting argumentative dialogues in English between users and ChatGPT, identify suitable instructions that facilitate a flowing debate, and assess the tool's ability to judge and determine the debate's winner. The study's findings indicate that ChatGPT can effectively participate in rhetorical competitions with the provision of specific instructions. While the tool demonstrates proficiency in generating relevant and logical arguments and counterarguments, it faces challenges in sustaining the topic's relevance throughout extended debates unless it assumes a judging role. Moreover, despite occasional violations of the rules of debate, its potential in pedagogical argumentation competitions remains promising. The results of the present research show that ChatGPT can participate in debates with specific rules. This finding suggests that ChatGPT can be used during training sessions in rhetoric educational clubs.

The article presents a formalized, mathematical model of software delivery speed (S-model) in a DevOps environment. It quantitatively describes the interaction between key parameters, including development speed, automation level, CI/CD maturity, resource provisioning, and architectural complexity. The study aims to develop a mathematical structure that can reproduce nonlinear dependencies. The model captures threshold effects and interactions among technical and organizational DevOps factors, demonstrating both practical and educational relevance. The research methodology involves analyzing modern DevOps frameworks, such as DORA, CALMS, SPACE, and Accelerate. We build a functional model using saturation functions and exponential damping. The study also applies scenario modeling and calibrates models using pseudo-real and training empirical data. The results demonstrate that the proposed S-model accurately reproduces the behavior of DevOps processes and describes the influence of technical and organizational factors. Automation and CI/CD have the most significant impact in the early stages of maturity. System complexity exponentially reduces delivery speed. Changes in development speed only affect productivity when the level of automation is sufficient. Model calibration revealed an average deviation of 14.3% between the empirical and model values, confirming the model's applicability even in small learning teams. The scientific novelty of this work lies in creating a formally defined mathematical model of delivery speed in DevOps. The model integrates technical, architectural, and process factors into a unified analytical framework. The model's practical value lies in its ability to perform sensitivity analyses, compare DevOps practices, predict the consequences of technical decisions, and support data-driven DevOps. Educational testing confirmed the model's effectiveness, showing that it promotes analytical thinking in students and fosters a systematic understanding of DevOps processes. Educators can integrate the model into courses on information system deployment, DevOps engineering, and software engineering.

Increased focus on personalized learning has highlighted the need for real-time monitoring of student engagement. Understanding attention levels during instruction helps improve teaching effectiveness and learning outcomes. However, existing methods rely on manual observation or periodic assessments, which are subjective and lack consistency. These approaches fail to capture moment-to-moment variations in engagement. Conventional systems using basic video tracking or facial detection lack robustness in variable lighting, head pose changes, and classroom dynamics. They are also limited in providing timely, actionable insights. This study presents FocusTrack, a real-time engagement monitoring system that utilizes facial cues and behavioral indicators for accurate classification. The system processes video frames locally and provides continuous engagement feedback. Two annotated datasets—EngageFace (150 hours, classroom-based) and StudyFocus (90 hours, home-based)—were developed to capture diverse learning scenarios. Each dataset includes labels for gaze direction, drowsiness, and facial cues. Experimental results show accuracy levels of 97.0% and 95.5% across the two datasets, outperforming conventional models. The system also maintains latency under 60 ms on CPU- based setups. FocusTrack offers a scalable, privacy-aware solution for continuous engagement monitoring in real-world educational environments. It provides instructors with objective feedback to adapt teaching strategies dynamically.

Learning is a lifelong process. The saying 'You learn as long as you live' exists for a reason. Acquiring new and unfamiliar knowledge, preparing for exams, completing assignments, and writing research papers can be challenging and time-consuming. As educators, we always aim to give our best, simplifying complex concepts, providing clear visual aids, and even offering simulations where possible. Our main goal is to engage students effectively and maintain their attention throughout the learning journey. This is why the teaching materials provided by professors and the way they communicate to students are of great significance. In this paper, we will explore students' perspectives on teaching materials through a brief discussion, and we will draw conclusions to guide the future direction of the learning process.

In recent days, we have largely adopted Advanced Large Language Models (LLMs) in educational settings, where we use them as content creators, teaching assistants, and interactive conversation agents. However, the responses generated by these models are often monotonous, verbose, and ambiguous, which can hinder their effectiveness in educational contexts. Addressing these shortcomings, we introduce EduAgent, a multimodal chatbot framework specifically designed to enhance interactive learning in Electrical and Electronics Engineering (EEE) education. EduAgent can respond with pedagogically enhanced answers to electronics-related queries, complemented by relevant images and detailed explanations. It is designed to provide complete, concise, step-by-step responses, ensuring that foundational knowledge is clearly mentioned before diving deep. To develop EduAgent, we constructed a dataset comprising 596 four-turn conversations and a collection of 118 images covering a wide range of EEE concepts. The conversation dataset was used to fine-tune the open-source LLMs and facilitate in-context learning. Both images and their corresponding explanations were integrated into a knowledge base for efficient retrieval. Finally, we evaluated multiple text generation and image retrieval methods using both automatic metrics and human assessments, demonstrating the effectiveness and engagement of our approach.

Cooperative training plays a pivotal role in the educational process, particularly at the university level, where students dedicate a certain period to work within governmental or private organizations. In this context, cooperative training enables students to acquire practical experience in their fields of specialization and to meet distinguished professional standards. In this study, the foundations and standards for organizing cooperative training will be presented to enhance the cooperative training efficiency. Given that the organization of cooperative training is closely tied to students' academic disciplines, particular focus will be placed on computer engineering and science fields. However, the proposed model can be adapted to suit other engineering and scientific fields. Organizing cooperative training in the domains of computer engineering and science is, to some extent, more complex than it is in other disciplines. This complexity arises due to the multiplicity and geographical distribution of training entities, the diversity of activities carried out during the training period, and the increasing number of participating students. To ensure the effective management of cooperative training and overcome the difficulties facing higher education institutions, such as ineffective supervision, management procedures, and the lack of databases, it is necessary to develop an electronic platform that facilitates interaction among the various stakeholders involved. In this regard, the platform should offer a set of services supported by smart technologies capable of regulating activities according to the approved training programs. Accordingly, this research will present the specification and analytical overview and propose initial designs necessary for the development of such a platform.

Early prediction of students' placement outcomes is critical for aligning curricula with industry demands, optimizing academic planning, and providing focused career support. It also enhances institutional reputation, strengthens employer partnerships, and supports data-driven decision-making. However, predictive modeling in this context is challenged by data heterogeneity, evolving market factors, subjective evaluations, and bias mitigation. This study proposes an AI-driven framework that integrates Gated Recurrent Unit (GRU) networks with Modified Dwarf Mongoose Optimization (MDMO) to address these challenges. GRU effectively captures temporal patterns in academic and behavioral data, while MDMO ensures optimal hyperparameter tuning through advanced search strategies. Model performance was rigorously evaluated using multiple metrics including accuracy, false positive rate (FPR), false negative rate (FNR), sensitivity, specificity, and Matthews Correlation Coefficient (MCC). The proposed GRU-MDMO model achieved an accuracy of 98.5%, sensitivity of 97.78%, specificity of 99.09%, and MCC of 96.97%, outperforming other baseline models such as SVM, ANN, RF, and traditional GRU variants. These results demonstrate the model’s robustness, reliability, and suitability for early placement prediction. This approach empowers institutions to improve placement rates, enhance curriculum design, attract admissions, and ultimately foster better student career outcomes through AI-guided educational intelligence.

One of the main concerns in educational research is the effect of computer games on students' psychological well-being and academic achievement. Three hundred students from the State Academic University for the Humanities and Kazan Federal University participated in this study to look at these consequences. Using a mixed-methods approach, quantitative data from standardized questionnaires (GHQ, STAI) and qualitative data from semi-structured interviews were combined. The two universities had GHQ ratings of 15.0 and 17.6 and STAI values of 40.5 and 38.7, respectively, indicating moderate anxiety and general health concerns. Self-reported GPA indicated generally strong academic achievement, with no significant disparities between universities. Significant support was found for educational games in the survey replies, with 75% citing higher motivation and 80% agreeing that they had a favorable influence on learning. Nonetheless, 50% of educators reported difficulties incorporating games into their courses. These results demonstrate how educational games may improve learning and engagement. According to the study, more investigation is required to examine long-term impacts, variances among populations, and aspects of game design that maximize learning results.