Capability Approach in Technology-Enhanced Tertiary Education: Looking for New Directions
Bibliographic record
Abstract
The Latvian-Ukrainian project "Gender aspects of digital readiness and development of human capital in regions" (LV-UA/2018/3) highlighted some peculiarities in educator and student attitude to Information Technologies (IT) that is positive in major but currently their appropriate usage lacks behind the possibilities Digital Technologies (DT). This study, among others, raised two questions that are addressed in this article: "Does gender significantly affect educator and student attitude to DT?" and "Is educators' current digital competence a comprehensive and sufficient target to meet modern rapid changes?" Some findings have pointed out essentialities in competence development and attracted the researcher attention to sources of attitudes, as well as challenged looking for a new direction to an appropriate pedagogical provision for further development of educator and tertiary student digital competence. The aim is to provide a theoretically-based introduction to the capability approach in using DT while building the capacity of the internal and external environment of higher education. The theoretical investigation draws on the theory of attitude sources and capability approach of educators and students; the empirical data illustrate the theoretical statements of attitude to IT. The empirical research methods and tools to illustrate theoretical considerations are questionnaires "Personal cultural orientations", "Cultural values scale", and "Scale to measure attitudes toward IT"; data processing followed the procedure suggested by the methodology of each tool. The research base is made up of 1013 respondents (n = 260 in Latvia; n = 753in Ukraine). The article advances arguments in favour of the capability approach to be discussed as a possibility to introduce a new pedagogical direction to further improve educators' competencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".