Exploring the Influence of a STEM CPD Programme on the STEM Self-Efficacy of Northern Irish Primary Teachers
Bibliographic record
Abstract
Abstract STEM education is widely recognised as beneficial in enabling learners to develop the knowledge, skills and dispositions needed to navigate the challenges and opportunities of the modern world. Primary educators are fundamental in supporting the STEM learning of their students through appropriate continuing professional development (CPD), which provides teachers with ongoing professional learning activities to support improvements in knowledge, skills, confidence and understanding relating to teaching. While post-primary STEM contexts receive considerable scholarly attention, primary STEM teacher CPD remains under-explored by comparison. This paper shares empirical insights from a study which explored the influence of a CPD programme on Northern Irish primary teachers’ ( n = 22) self-efficacy in STEM. Analysis of data from surveys, focus group interviews and participants’ reflective diaries indicated that the programme enhanced teachers’ overall self-efficacy and facilitated development of their STEM knowledge. Participants also identified prevailing issues which constrained their capacity to facilitate effective STEM learning experiences for their students. These included resourcing, along with wider issues of policy, curriculum and assessment priorities in the Northern Irish education context. For practitioners, researchers and policymakers, this paper offers fresh evidence and original insights into how teachers can be supported in STEM teaching and learning through effective CPD. We contend that teachers and schools must be supported appropriately through leadership, policy and resourcing at all levels if the educational, economic and social benefits of STEM for learners is to be realised in practice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".