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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".