The role of teachers in facilitating learning is undoubtedly important as they are responsible for selecting appropriate instructional approaches. Project-based learning (PrBL) has gained recognition as an effective teaching approach as it encourages students to think critically, collaboratively, and systematically. PrBL refers to an educational approach that emphasizes student engagement and active learning through the completion of real-world projects. Students are required to acquire information, search, and experiment to solve a specific problem. PrBL is largely adopted by the higher educational level. Limited use in primary schools has been highlighted by much research. The decision to adopt such a method depends on several factors. The main drivers to make such a decision are the teachers’ preference and the readiness for support from the school. The location of the school largely contributes to the readiness, facilities, support, and quality of education. This paper examines the teachers’ point of view on the utilization of PrBL. Comparing the points of view of the teachers who taught in different locations allows us to observe the factors that should be carefully addressed in order to promote the use of PrBL in primary schools. By using both qualitative and quantitative data, this study aims to understand the potential drawbacks preventing from using the PrBL. The data mining techniques were used to discover insights from both types of data including Epistemic Network Analysis (ENA) and sequence mining. ENA employs various mathematical and statistical techniques to analyze and visualize the network structure and dynamics. It can measure the strength of connections, identify central key concepts, and compare the differences in the structure between groups. Sequence mining allows us to observe the pattern of PrBL utilization. The results showed that even though teachers viewed PrBL as a useful approach, not many of them are using it. Also, there are some inconsistencies of knowledge on the steps in the PrBL process. Additionally, teachers often mentioned several problems they faced when using the PrBL. Hence, extra support and knowledge provision are needed, especially for schools located in suburban and rural areas.

Evidence from psychology and behaviour therapy shows that engaging in sports activities at home might help alleviate stress and depression during COVID-19 lockdown periods. A clever virtual coach that provides table tennis instruction at a low cost without invading privacy might be a great way to maintain a healthy lifestyle without leaving the house. In this article, we look at creating the second main constituent of the virtual-coach table tennis shadow-play training scheme: an evaluation system for the effectiveness of the forehand stroke. This research was carried out to demonstrate the efficacy of the suggested bidirectional long-short-term memory (BLSTM) model in assessing the table tennis forehand shadow-play sensory data supplied by the authors in comparison with LSTM time-series investigation approaches. Information was collected by tracking the rackets of 16 players as they performed forehand strokes and assigning assessment ratings to each stroke based on the input of three instructors. The scientists looked at how the hyperparameter values, which are chosen via an optimisation approach, affected the behaviour of DL models. The adaptive learning differential approach has been introduced to enhance the functionality of the standard dragonfly algorithm. Optimal BLSTM settings are selected with the help of the enhanced dragonfly algorithm (IDFOA).  
The experimental findings of this study indicate that the BLSTM-IDFOA is the most effective regression approach currently available.

The growing field of educational data mining seeks to analyse educational data in order to develop models for improving education and the effectiveness of educational institutions. Educational data mining is utilised to develop novel approaches for extracting information from educational databases, enabling improved decision-making within the educational system. The main objective of this research paper is to investigate recent advancements in data mining techniques within the field of educational research, while also analysing the methodologies employed by previous researchers in this area. The predictive capabilities of various machine learning algorithms, namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Random Forest, K-Nearest Neighbour, and XGBoost Classifier, were evaluated and compared for their effectiveness in determining students' academic performance. The utilisation of Random Forest and XGBoost classifiers in analysing scholastic, behavioural, and additional student features has demonstrated superior accuracy compared to other algorithms. The training and testing of these classification models achieved an impressive accuracy rate of approximately (96.46% & 87.50%) and (95.05% & 84.38%), respectively. Employing this technique can provide educators with valuable insights into students' motivations and behaviours, ultimately leading to more effective instruction and reduced student failure rates. Students' achievements significantly influence the delivery of education.

A STEM project was implemented, which is intended for students of technical specialties to study the principles of building and using a computer system for segmentation of images of railway transport using fuzzy logic. The project consists of 4 stages, namely stage #1 "Reading images from video cameras using a personal computer or Raspberry Pi microcomputer", stage #2 "Digital image pre-processing (noise removal, contrast enhancement, contour selection)", stage #3 "Segmentation of images", stage #4 "Detection and analysis of objects on segmented images by means of fuzzy logic". Hardware and software tools have been developed for the implementation of the STEM project. A personal computer and a Raspberry Pi 3B+ microcomputer with attached video cameras were used as hardware. Software tools are implemented in the Python language using the Google Colab cloud platform. At each stage of the project, students deepen their knowledge and gain practical skills: they perform hardware and software settings, change program code, and process experimental images of vehicles. It is shown that the processing of experimental images ensures the correct selection of meaningful parts in images of vehicles, for example, windows and number plates in images of locomotives. Assessment of students' educational achievements was carried out by testing them before the start of the STEM project, as well as after the completion of the project. The topics of the test tasks corresponded to the topics of the stages of the STEM project. Improvements in educational achievements were obtained for all stages of the project.

Through the Blended Learning model, this study explores the factors affecting fostering of information and communications technology (ICT) applications in teaching for teachers in the Northern midland and mountainous provinces of Vietnam. The survey is designed to be uploaded to the Learning Management System (LMS) system and requires learners to answer online after completing the course. Influential factors are considered, including 9 groups formed from 45 questions (independent variables). The independent variables are evaluated based on the 5-level Likert score bar. This study uses Cronbach's alpha to determine the reliability of the questions. The survey received responses from 1484 teachers who completed the course in the northern midland and mountainous provinces. After removing the answers with no statistical significance, the remaining samples included in the analysis through SPSS software were 558. The results of the EFA analysis retain 29 observed variables and indicate 7 factors affecting the fostering of ICT application in teaching for teachers. Next, using CFA (Confirmatory Factor Analysis), the study removed 02 more observed variables and pointed out 7 factors affecting the effectiveness of fostering information technology application in teaching for teachers, includes (i) Training methods; (ii) Organization of training courses; (iii) Online learning management system; (iv) Facilities for face-to-face learning; (v) Training content; (vi) Training objectives; (vii) Impact of training content. The results will help researchers and educational administrators find ways to improve the quality of professional development for teachers in high schools in this content and other similar contents.

The study solves the problem of improving the methodological and algorithmic support of the decision-making process by developing a model of the preference criterion for interval evaluations of alternatives. The aim of the study is to improve the efficiency of decision-making based on interval expert data under conditions of uncertainty and risk by developing a criterion for the preferences of interval evaluations of overlapping alternatives. The object of the study is the decision-making process based on the classical efficiency matrix with interval elements, the subject is the model of decision maker's (DMP) preference criteria for interval evaluations of alternatives. The relevance of the task is stipulated by the urgency of the problem of adapting classical decision-making methods and models to practical problems of gray analysis, in particular, with interval uncertainty of primary expert data. A multifactorial model of the normalized preference criterion for interval evaluations of alternatives is proposed. Due to the additional consideration of the degree of preference of the DMP for the width of interval estimates, it allows ranking interval estimates of alternatives that overlap and are considered classically incomparable. A single analytical form of the normalized criterion model for ranking interval, weighted interval and point estimates makes it possible to increase the degree of automation of processing interval expert estimates in the decision-making process. Recommendations for the practical application of the proposed model are formulated. The developed model and corresponding algorithms can be used in automated expert decision support systems.

Influence Maximization (IM) is an optimization problem that deals with identifying the most valuable individuals/ nodes present in the network to attain the maximal information spread when they are activated. Evolutionary Algorithms (EAs) inspired from nature are one of the most powerful methods to solve an optimization problem. This paper attempts to solve the IM problem using few of the popular EAs such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Differential Evolution (DE). These algorithm’s performance is evaluated using four comparative metrics, that deal with assessing solution quality, computational efficiency, and scalability. The experimental results of these EAs when tested on several real-world networks reveal that the GE and PSO algorithms were found to produce relatively poorer quality of seed nodes and also with higher computational costs, making it less preferrable. DE was able to obtain the best seed sets (in terms of influence spread value) than other algorithms and ACO, the fastest among all the considered algorithms in terms of execution time, was found to obtain seed set with appreciable influence spread with a slightly higher computational cost than DE.