Assessment of Instructors’ Technology Competency to be Used in the Settings of Formal and Non-Formal Education
Bibliographic record
Abstract
The purpose of this study was to test the model of diagnostic assessment of the level of formedness of the competency of pedagogical staff to use teaching technologies when applied in the settings of formal and non-formal education and to see whether the model creates opportunities for designing a strategy for professional lifelong development of teaching staff. A convergent (computer-oriented and traditional) methodology was used in this study including: the methodology for examination of the motivation drivers of professional activity (K. Zamfir); “Square of Values” methodology by O. Murzina; methodology of self-assessment of vocational and pedagogical motivation, Bass’s questionnaire on orientations entitled “Personality orientations”, Henning’s methodology called “Structure of Interests”, Criteria Cognitive Aptitude Test. It has been proved that the majority of teaching staff demonstrated the imitative and reproductive level of formedness of the professional competency consistent with all criteria. It seems that educators strive for self-improvement and self-development in using teaching technology in formal and non-formal education settings. This study results can be well used either to develop the structure and content of the professional development or post-graduation programs for the teaching personnel at the instructional institutions or to assess of the level of formedness of the professional competence of pedagogical staff to use teaching technologies in the settings of formal and non-formal education. The study attempts for the first time ever to assess professionalism-related competency in using teaching technology in formal and non-formal education settings and to specify the value orientations, motives, needs, spectrum of knowledge, skills, and abilities comprising the level of proficiency in this area as it is viewed through the needs of the labour market.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".