THE DEVELOPMENT OF SCIENTIFIC, ACADEMIC AND PRACTICAL CAPACITY OF RESEARCHERS AND EDUCATORS IN THE FIELD OF TRANSFORMATIVE DIGITAL LEARNING
Bibliographic record
Abstract
Transformative Digital Learning (TDL) is a progressive form of fully online, problem-based, constructivist learning, developed by research associates of the Educational Informatics Lab (EILAB), University of Ontario Institute of Technology, Canada. TDL transforms higher educational practices from: authoritarian instruction to democratized learning, individualistic learning to socially distributed and tool-mediated, knowledge building, a mechanism of the status quo to a catalyst for social progress, offer a truly novel approach to the theory of teaching and education, introduce new methodology and practical techniques to learners of all ages. Moving from lecture- and book-based, classroom learning to TDL proved to be a significant adjustment requiring a good level of participant readiness. The main aim of the project “Implementation of Transformative Digital Learning in Doctoral Program of Pedagogical Science in Latvia” is to create new pedagogical knowledge and technological know-how in the field of transformative digital learning (TDL) in higher education in Latvia based on Canadian experience and to ensure transfer of knowledge and skills in the further development of the doctoral study program "Pedagogy", as well as the development of scientific, academic and practical capacity of researchers and educators.The specific objectives of the research meet the main challenges and priorities of the project, originality and novelty in the field of research:Implementation of interdisciplinary research, capacity building and the creation of a new knowledge base by studying the situation and transferring innovation to implement TDL in doctoral studies;Development of new prototypes: e-platform, innovative methodologies (approaches, methods, techniques) for completing experiments, approbation, implementation and provision of new services in the context of higher education, which will provide world-wide recognized knowledge transfer in the development of innovative and advanced didactic materials which meet the most topical priorities of the Smart Specialization Strategy set out in Latvia:Productive innovation system; modern ICT; Modern education; Knowledge base; Information and Communication Technologies for Education and Economics.
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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 | 0.046 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".