Cognitive skills predict academic performance, so schools that try to improve academic performance might also improve cognitive skills. The purpose of this study was to determine the effect of achievements in mathematics on cognitive ability in primary school. Methods: Participants: 100 girls and 102 boys aged 9–10 years (the fourth grade) were selected from three schools. A diagnostic test of cognitive abilities (DTCA) was created by the authors of the article for the assessment of primary school students’ cognitive abilities. The diagnostic cognitive ability test was based on Reuven Feuerstein’s theory of dynamic cognitive modality assessment, the problem-solving model, and followed the mathematics curriculum for grade 4. The tasks of the test were distributed according to the cognitive function: systematic exploration, spatial orientation, sequencing, image recognition, recognizing and understanding relationships, collecting and processing information, algorithm development, data management (classification), and construction of combinations. Achievements in mathematics: they were collected systematically using short- and medium-term mathematics tests, and the levels of achaievement were defined of grade 4 primary school students to assess individual learner performance, anticipate their learning strengths and weaknesses, and shape their subsequent learning process. Results: With regard to the relationships between cognitive functions and achievement level, Spearman’s correlation analysis revealed the relationships between the following cognitive functions: systematic exploration and spatial orientation (Spearman q = 0.276, p = 0.022), systematic exploration and designing an algorithm development (Spearman q = 0.351, p = 0.003), spatial orientation and data management (Spearman q = 0.274, p = 0.023), sequencing and combination construction (Spearman q = 0.275, p = 0.022), and sequencing and recognizing and understanding relationships (Spearman q = 0.243, p = 0.044). Conclusions: (1) The internal validity of the diagnostic test of cognitive abilities was supported by significant correlations between cognitive functions and mathematics achievement. This suggests that this methodology of the diagnostic cognitive ability test can be used to assess the cognitive abilities of primary school students. (2) The diagnostic test of cognitive abilities showed that the majority of primary school students reached higher levels of achievement in a systematic inquiry (systematic, non-impulsive, planned behavior when collecting data or checking information). A difference was observed in the ability of students to navigate in space and follow directions for primary school students at a satisfactory or higher level. Primary school students’ performance in identifying the rule for the sequencing of elements, finding missing elements, and extending the sequences was at the basic and advanced levels. (3) The results of the study showed the reciprocal correlation between achievements in mathematics and cognitive function of primary school students. The two phases that caused difficulties for students were revealed: understanding the problem and carrying out the plan phase.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatics in Education, Title: A Formation Method of Flexible Learning Objects, Authors: Ramunas KUBILIUNAS, Eduardas BAREISA , A brief overview of formation method of flexible learning objects is presented in this article. The basis of this method is e-learning material that is structured and separated from display rules. The learning objects that have such e-learning material may be adapted to individual needs and may be used in different learning contexts without changing e-learning material. To change the presentation form of e-learning material of such objects, it is enough to change display rules of this material. However, if the e-learning material must be adapted too, it is much easier to do this as the material is structured and contains less technical information of representation. The adaptation of such learning objects is more effective and needs less work time input, therefore they are called as flexible learning objects.

This study analyses the application of games and gamification to increase student motivation and ensure an effective and sustainable learning process in the modern sustainable environment of digitalisation and e-learning. Recent changes have revealed that traditional teaching methods do not always sufficiently engage students, increasing the risk of decreased motivation to learn and poorer achievement. Gamification, which is based on the integration of game design elements – points, levels, badges, challenges – into learning activities, can help reduce these risks by promoting student autonomy, a sense of competence and social engagement. The study presents the application of a gamification system based on case analysis using an educational platform that allows the creation of educational games and the assessment of student engagement in real time. The results show that gamification methods significantly increase student activity and motivation to learn, while integrated game elements improve student’s motivation and learning experience. Based on the analysis, the paper presents conclusions and recommendations for the effective, high-quality, and sustainable organisation of the modern education process based on gamification.

Artificial Intelligence (AI) based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative research demonstrates that AI-driven systems can significantly improve academic performance, teacher productivity, and learner motivation, offering a scalable and equitable solution for the future of education in both traditional and digital environments.

Artificial intelligence (AI)-based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This study presents a comprehensive literature review and a methodology grounded in constructivist learning theory to develop an AI-based educational support framework. The study is situated within the context of a higher education course integrating AI-supported learning. The proposed framework is developed by synthesizing theoretical and empirical evidence and is subsequently evaluated by experts in educational technology and artificial intelligence. Data are collected through structured expert questionnaires and qualitative feedback. Quantitative data are analyzed using descriptive statistics, while qualitative responses are examined through thematic analysis to inform framework refinement. The study adheres to established ethical principles, including informed consent, voluntary participation, confidentiality, anonymity, and secure data management. Moreover, the paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative expert evaluation suggests that an AI-based educational support framework has the potential to improve teaching support, learner engagement, and personalized learning while providing a scalable and equitable approach for higher education.