Pre-service teachers’ experience with writing lesson outcomes at a South African university: an emerging reflective awareness
Bibliographic record
Abstract
Although lesson planning is widely regarded as a crucial skill that pre-service teachers must master, writing clear and measurable lesson outcomes remains a persistent challenge. This study investigates the experiences of 150 second-year Bachelor of Education students at a South African university as they engaged in writing lesson outcomes and reflecting on their practice. In this qualitative phenomenological study, data were collected through a formal assessment that required students to design and reflect on their lesson plans. Thematic analysis revealed four key themes: lack of clarity, ambiguous verb selection, challenges in curriculum implementation, and difficulties in applying knowledge of Bloom’s taxonomy and SMART criteria to practice. A significant finding is that, despite these challenges, participants demonstrated a growing awareness of the importance of reflection in writing lesson outcomes. They expressed the need for a more scaffolded approach and practical opportunities to translate theory into practice. Integrating iterative feedback, peer review, and contextualised exemplars could empower pre-service teachers to design authentic, engaging, and practical lesson outcomes as a foundational step in lesson planning.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".