In 21st-century learning, students' second-language (L2) writing competence should be grounded in the principle of lifelong learning, with teachers recognising the urgency of self-directed learning. By encouraging autonomy, this principle and praxis enable students to improve their writing abilities outside the conventional classroom, helping them succeed in various situations, including international communication. This mixed-method study involved 40 participants from one university in Indonesia. This study aimed to (1) describe the implementation of self-directed learning through automated written corrective feedback and (2) reveal the students' perceptions of automated written corrective feedback in EFL writing classrooms. Classroom observation, writing test, and semi-structured interviews were used to collect research data. The quantitative data were analyzed statistically using SPSS version 25. In contrast, the qualitative data were analyzed using triangulation following the concurrent activity flows comprising data condensation, data display, and drawing and verifying conclusions. The study's findings revealed that teachers and students intensely monitored the implementation of self-directed learning through automated written corrective feedback during writing activities. Students' writing scores met the minimum completeness criteria. The subsequent finding reflected students' positive perceptions of self-directed learning style in a writing project that could improve their proficiency.
Self-regulated learning capabilities determine academic success in higher education, yet traditional instruction shows limited effectiveness in developing these essential competencies. This quasi-experimental study investigated how intelligent AI agents influence SRL development in 384 university students across three Polish institutions during the 2022-2023 academic year. We assigned 196 students to an intervention group using an integrated AI system with adaptive pathways, metacognitive scaffolding, analytics dashboards, and automated feedback, while 188 controls received conventional instruction. Both groups completed the Motivated Strategies for Learning Questionnaire before and after the 16-week intervention, with behavioral data continuously logged throughout. Results showed the intervention group achieved 34.7 % higher metacognitive awareness (Cohen's d = 1.23, p < 0.001), demonstrating students became substantially better at reflecting on and directing their learning processes. Self-monitoring behaviors improved by 41.2 % (d = 1.38, p < 0.001), meaning students tracked their progress and adjusted strategies more effectively. Goal-setting effectiveness rose 28.9 % (d = 0.97, p < 0.001), indicating clearer learning objectives and better planning. Learning analytics revealed intervention students visited the platform 2.3 times more frequently and accessed 2.3-fold more diverse resources than controls. Multiple regression showed that AI-mediated metacognitive support predicted nearly half the variance in academic achievement (R2 = 0.473, F(4,379) = 85.42, p < 0.001). However, cluster analysis identified concerning dependency patterns in 23.6 % of intervention participants who over-relied on AI guidance despite receiving the same intervention as more autonomous peers. These findings demonstrate intelligent agents can powerfully enhance self-regulatory capacities while highlighting the need for designs that prevent maladaptive dependency and maintain student agency.
Introduction. The article is devoted to examining the practices involved in shaping the cultural identity of university students. Aim. This study aims to identify tradition as a form of cultural capital and as a factor in the formation of young people's cultural identity. Methodology and research methods. An analysis of the literature, a secondary analysis of sociological research findings on the value orientations of Russian youth, and the results of conducted focus groups led to the conclusion that students associate cultural identity with traditions that have historically developed across various spheres of life. Eighty per cent of focus group participants defined tradition as a cultural code, whereas the students' stance was ambivalent and syncretic. Results and scientific novelty. It has been established that young people do not perceive traditions as an immutable cultural framework; rather, in the context of an "identity crisis", they actively create and recreate their identities, including by drawing on cultural traditions. Russian youth (the digital generation, encompassing both younger and older millennials) regard a strong family as a traditional value. However, their evaluation of other values traditionally associated with Russian cul-ture is ambivalent. Reflexive traditionality is highlighted as important, involving the comprehension and analysis of the role of traditions in culture and their connection to both personal and collective cultural identity. An example of this reflexivity is the model of reproducing the spiritual personality of the teacher (with its characteristic system of activities and motivations) in the student. Such examples of reproduc-tion exist in various cultures, including our own national culture. The educational and socio-cultural environment of educational institutions can and should serve as a space for reflexive traditionality. The formation of traditional cultural values should also be the outcome of a purposeful state youth policy. Practical significance. The results obtained can serve as a valuable tool for university lecturers in evaluat-ing the role of tradition in culture and in shaping students' cultural identity.
The article addresses the problem of analyzing large corpora of scientific texts. It is noted that traditional scientometric approaches based on citation analysis do not always capture the substantive proximity of ideas or trace the intellectual continuity of scientific knowledge. The paper proposes an algorithm that combines the capabilities of large language models (LLMs) with quantitative analytical methods. The approach proceeds in two stages. At the first stage, LLM is used to automatically structure the text of a scientific paper and convert it into a machine-readable format. This yields, for each work, a vector representation of its thematic profile. At the second stage, thematic profiles of individual works and of pre-specified groups (schools of thought) are compared using centroid distances and clustering techniques. The ultimate goal is to move beyond formal relational analyses among researchers (e. g., “supervisor — dissertation candidate”) toward constructing an intellectual genealogy that tracks the evolution of schools of thought on the basis of the substantive proximity of their ideas. It is emphasized that the proposed approach does not replace expert assessment but serves as an auxiliary tool for navigating the scientific literature and identifying connections between studies.
The strategic management of library policy at the regional level, designed to ensure more integrated and harmonious territorial development, calls for a balance between the rate of digitalization and the digital transformation of libraries. The relevance of the research lies in highlighting regional disparities in the digital transformation of libraries. Its objective is to demonstrate the potential of clustering and visualization for managing library policy based on official state statistics. The study employed statistical methods, along with content analysis of relevant publications, comparative analysis, and ranking. Dashboards (visualization panels) were also developed. The main results include a typology of the constituent entities of the Russian Federation based on their digitalization outcomes. The data was sourced from the 2024 state statistics on the activities of public libraries, available on the official website of the Main Information and Computing Center of the Ministry of Culture of the Russian Federation. The article provides a point-rating assessment of the success of regional digitalization of libraries. It also presents a user satisfaction calculator that measures the following metrics: overall customer satisfaction; the likelihood that a customer will recommend the library to others (Net Promoter Score, NPS); the customer effort score (CES); the average handling time (AHT); the first contact resolution rate (FCR); the customer acquisition cost (CAC); the customer retention rate (CRR), etc. The practical significance of the study lies in its application to planning the digital transformation of libraries in Russia’s regions. The paper describes the methodology for analyzing statistical data, developing tile grid maps (heat maps) in Microsoft Excel, and creating a user satisfaction calculator.
