Motivational and demotivational factors affecting a teacher’s decision on whether to do research
Bibliographic record
Abstract
One of the teacher’s basic tasks should be to ensure that the quality of his/her educational work is continually enhanced by the application of practitioner research, as a recognised genre of educational research. The aim of this study was to explore factors that can motivate or demotivate teachers to include research in their educational practice. An online questionnaire was addressed to all Slovenian primary and secondary school teachers and full responses were received from 325 teachers. Although the teachers expressed high perceived self-confidence in their research abilities, this did not transfer to research activity, as only about one quarter of the respondents reported that they performed research. The main drivers of research are an intrinsic motivation for research and career goal orientation, followed by the relatively low influence of important others. Practitioner research is highly valued among teachers, so there must be other factors at work preventing more teachers from starting research activities. Among the leading factors recognised as obstacles are those that can be regarded as facilitating conditions in terms of motivational theory. Work overload, lack of time, school bureaucracy and family life can be regarded as competing with research for the teacher’s time, along with other important issues. The school climate cannot be regarded as the main obstacle to research. The findings call for the reallocation of at least some work duties in favour of research as part of regular employment. (DIPF/Orig.)
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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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".