It's not my subject!:Issues relating to the preparation of primary student teachers to teach computer science
Bibliographic record
Abstract
Computing has been part of the National Curriculum since 2014. Prior to this, the subject was known as Information and Communication technology (ICT) and had been compulsory for all pupils aged 5 to 16 in maintained schools, since 1988. This means that the requirement for pupils to learn about technology is not something new. However, a key development within the revised curriculum subject, was the introduction of computer science, with the necessity for pupils, even in primary schools, to design, write and debug computer programs. Evidence suggests that many primary schools have not yet adapted to the requirements of the Computing programme of study (Royal Society, 2017; Larke, 2019). Computing modules within the initial teacher education (ITE) programmes at the focus institution include significant input on computer science, yet students report that they lack confidence and feel underprepared to teach this aspect of the curriculum during professional practice. The issue is compounded by the lack of opportunities to observe computer science being taught in schools. Despite input at university, students often do not see this translated into practice in schools. One outcome of this situation is that the students appear to start to determine the ‘worth’ of Computing in the National Curriculum and in primary schools. The aim of this research project is to gain an in-depth understanding of the complex and inter-related issues surrounding the preparation of student teachers to teach computer science. In our conference presentation we will share the initial findings from interviews, questionnaires, document analysis and professional reflectio
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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 | 0.024 | 0.076 |
| 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 | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".