DEVELOPMENT OF CLASSIFICATION OF LEARNING TASKS

Belikov Sergey Nikolaevich

The article presents a comprehensive analysis of the evolution and effectiveness of learning task classifications in educational systems, including a multidimensional approach covering cognitive, affective and psychomotor domains; a research methodology combining quantitative measures (standardised tests, performance measures) and qualitative measures (interviews, classroom observations) allows us to understand how task classifications affect learning outcomes. The main findings demonstrate a significant increase in student engagement and academic achievement, as evidenced by a significant increase in standardised test scores (15% on average), supported by qualitative feedback from teachers and students who report increased satisfaction and deeper engagement. Based on these results, the article proposes several promising recommendations for improving task classifications: in particular, the integration of advanced educational technologies (e.g., artificial intelligence, real-time data analysis) to develop adaptive, learner-centred task frameworks; the article also suggests the need for an interdisciplinary approach, incorporating insights from cognitive science and psychology, to enrich and universalise task classifications. Future research directions are outlined, emphasising the need for longitudinal studies to assess the broader impact of these innovations on comprehensive educational outcomes, including the development of emotional and social skills. The findings of this study not only highlight the critical role of complex task classifications in improving pedagogical practice, but also predict a future in which educational systems will dynamically respond to the changing needs of society, thereby fostering adaptive and transformative educational environments.

Keywords:classifications of learning tasks, learning outcomes, cognitive complexity, pedagogical effectiveness, quantitative and qualitative methods, competency-based education, adaptive learning technologies, interdisciplinary approaches, longitudinal educational research, educational innovations.