Sparking Pre-service Teachers’ Self-Awareness through a Strengths-Based Psychometric Tool: A Pilot Study in a Christian University
Bibliographic record
Abstract
The pilot study outlined in this article aimed to explore the relevance and value of a strengths-based psychometric tool, Lumina Spark©, in a teachers’ education program. The study involved pre-service teachers from a Christian university and was conducted over a six-week winter teaching practicum. The psychometric assessment and workshop were designed to help pre-service teachers explore their personality and develop self-knowledge, social awareness, and stress management skills. The study found that the Lumina Spark© tool was effective in helping pre-service teachers understand their strengths and weaknesses and how they relate to others. The study also found that the tool helped pre-service teachers develop self-efficacy and increased their motivation to learn and improve. The findings suggest that the use of a strengths-based psychometric tool can enhance pre-service teachers’ self-knowledge, build rapport, and value diversity in the teaching profession.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".