With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

This article deals with the problem of optimal resources allocation in reference to the area that is currently relevant in the Republic of Kazakhstan, the educational system and management issues in educational organizations in rapidly changing economic and social conditions. A model for the optimal resources’ allocation in the management of business processes that exist in educational organizations has been developed using the example of one of the key business processes. Such research methods as search, survey, Fishbone diagrams and heuristic methods were used. A computational algorithm was developed and the testing results on the suggested example were presented. Comparative analysis shows that the developed computational algorithm based on the application of the linear programming method results in the optimal resources allocation in the considered business process. The analysis of existing methods reveals their limitations, particularly in dealing with dependent operations. The research findings and approaches have practical implications for improving the management system and enhancing the quality of business processes in educational organizations. The algorithms and models developed in this study can be applied not only to solve load distribution issues among teachers but also to address resource allocation problems in other areas of educational institutions.

Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.

There has been a significant rise in internet usage in recent years, which has led to the presence of data theft and the diversity of counterfeit materials. This has resulted the proliferation of cybercrimes and the theft of personal data via social media, e-mail, and phishing websites that are similar to the websites commonly used to grab user data details like that of a credit card or login ID. Phishing, a prevalent form of cybercrime, poses a danger to online security through the theft of personal information, and with the emergence of the COVID-19 virus, which has led to people and organizations being drawn towards the Internet and many people and companies being forced to work remotely, it has led to an increase in the existing phishing threats. Previously, hackers took advantage of the situation to infiltrate the devices of people and companies in numerous ways, which caused huge financial losses and damage to organizations. Based on previous results and research, Machine Learning (ML) is selected by researchers as an efficient method for identifying malicious software web pages from original web pages. This paper presents 30 characteristics of websites, which are analyzed using a correlation matrix to determine the relationship between variables. Feature selection is performed through a wrapper method and Extra Tree Classifiers (ETC) to identify the top-ranked characteristics (Features) for website classification. To evaluate web pages, various machine learning techniques such as Random Forest Tree (RF), Multilayer Perceptron (MLP), Decision Tree (DT), and Support Vector Machine (SVM) are used. The results of monitoring indicate that MLP, a deep neural network, outperforms all other techniques in terms of performance.

Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.

During COVID-19 pandemic, most tertiary institutions in Ghana were compelled to continue delivering of lectures online using internet technologies as was in the case of other countries. Senior high schools in Ghana were, however, not asked to do same, currently, the setting of most literature on blended or online learning in Ghana is focused on tertiary education. This paper situates the blended learning model in a less endowed senior high school to unearth the prospect of its implementation. The research provides an alternative to the traditional face-to-face learning, which is faced with the challenge of inadequate infrastructure, high number of students to class ratio, less compatibility with 21st learning skills and long-life learning in Ghana.
A customed Moodle application as web application tool, hosted students online in both synchronous and asynchronous interactions. Purposive quota sampling size technique was used to select an appreciable sample size to fully go through the traditional face-face model for a term and then study through the blended learning model for another term. Students’ examination performances for both were analyzed with a paired t test statistical model. Interviews with participants were conducted to ascertain their evaluation of the blended learning model and questionnaires were also administered to discover the institutional, technological, and human resource readiness for blended learning in senior high schools. The analysis of the data gathered, proved that blended learning in senior high schools has high prospect and is better alternative to face-to-face learning in Ghana.