Personal Epistemic and Learning Approaches as Predictors of Pre-service Teachers use of Strategies to Counter Cognitive Dissonance from Supervisor Feedback
Bibliographic record
Abstract
This study investigated how epistemic and learning approaches of pre-service teachers (PRESETs) in Obafemi Awolowo University, Southwestern Nigeria, predict their use of strategies to counteract cognitive dissonance arising from incongruent feedback from supervisors. The study adopted the descriptive survey research design. The population comprised 192 PRESETs in the third and fourth year of their teacher training. Findings revealed that the PRESETs possessed sophisticated personal epistemic approaches and utilised the deep approach to learning more than the surface approach. It was also revealed that the PRESETs are likely to utilise multiple strategies to counteract cognitive dissonance that may arise from conflicting feedback from university assigned supervisors during teaching practice. Findings revealed a function with coefficients as follows: deep approach (0.78), simple knowledge (0.21), surface approach (0.22), innate ability (-0.015), quick learning (-0.09), omniscient authority (0.17) and certain knowledge (0.24). The structure was maximised for 77% of PRESETs with high use of strategies to counteract dissonance arising from incongruent supervisors’ feedback; 36.7% and 67.6% of PRESETs with moderate and low dissonance reduction strategy users respectively. The conclusion reached was that teacher educators and other stakeholders should be made aware of these findings. Also, these findings should be incorporated in the implementation of course contents on sources of cognitive dissonances during teaching practice and how to counter them.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".