An Exploration of Teachers’ Approaches to Positive Education and Character Development: Curriculum Implementation, Assessment, and Outcomes
Bibliographic record
Abstract
Adolescence is highly influenced by significant physical, biological, and psychological changes, as well as by one’s environment; in turn, negative emotions such as depression, anxiety, and violent behaviour can arise if these influences are not constructive. Additionally, not all children have family support or are raised in an environment that fosters their positive development. In this context, schools may play a vital role, especially through the implementation of positive education and character development programs. Although the current literature presents several studies in this area, there is no consensus in the scholarship regarding the most appropriate practice for implementing positive education and character development initiatives; moreover, there is scarce literature on assessment measurements in this domain. Based on this premise, I explored how an elementary and a secondary teacher at a private boarding school in Southern Ontario implement and assess positive education in the curriculum. Further, I examined the outcomes achieved through positive education by these teachers, as well as the related impact on students’ character development. Using a qualitative thematic analysis, I was provided with substantial data through in-depth interviews with the participants. Findings indicate that teachers are the main individuals responsible for the development of positive education initiatives, and their preparedness and motivation to teach promotes positive outcomes. Furthermore, although no formal strategies to measure their outcomes were reported, the study findings reveal that participants’ approaches to positive education – either through character strengths or the promotion of positive states, such as positive relationships, engagement, and positive emotions – have constructively influenced the development of students’ characters.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".