MOTIVATION FOR LEARNING IN THE DIGITAL AGE: EFFECTIVENESS STRATEGIES FOR GENERATIONS Z AND ALPHA
Bibliographic record
Abstract
In the context of the rapid development of digital technologies and the transformation of the educational environment, the problem of maintaining and developing learning motivation is of key importance for the effectiveness and sustainability of educational systems. Generations Z (1995–2010) and Alpha (2010 to the present), being digital natives, demonstrate qualitatively different cognitive, communicative and value models compared to previous generations. Their educational experience is formed in the context of high-speed information flows, multimedia content, the integration of social networks and gamified platforms into everyday learning. These generations require fundamentally new approaches from teachers and educational institutions that take into account the fragmentation of attention, the need for interactivity, instant feedback and personalized development trajectories. The article reveals the theoretical and methodological foundations of motivation for learning in the digital era, analyzing the transformation of classical motivational concepts ( behaviorist , cognitive, humanistic) in the digital environment. Based on the theory of self-determination , it is considered how basic psychological needs - autonomy, competence and involvement - are reflected in digital educational ecosystems and influence the formation of sustainable learning motivation. The article presents the features of value and cognitive attitudes of generations Z and Alpha , identifies key barriers that hinder long-term engagement (information overload, superficial assimilation, shift towards external motivation). Comprehensive strategies for increasing motivation are proposed, including personalization of learning based on artificial intelligence, semantic gamification , microlearning , project-based collaborative formats and integration of media literacy. The synthesis of modern pedagogical approaches, neuropsychological research and EdTech practice allows us to offer teachers, administrators and developers of digital educational platforms recommendations for designing learning environments that can not only support but also develop internal motivation, ensure depth and awareness of learning, and prepare students for successful activities in the conditions of an ever-changing technological reality. For the first time, a three-level model of recommendations (strategic, organizational, methodological levels) has been proposed, ensuring a holistic adaptation of educational processes to the characteristics of digital generations and minimizing the risks of a decrease in internal motivation in the digital environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".