Do student teachers see what learners see? – Avoiding instructional dissonance when designing worksheets
Bibliographic record
Abstract
Background: The judicious use of worksheets ought to contribute to the establishment of literacy, with a special significance for multilingual classrooms where neither teachers nor learners are mother tongue speakers of the instructional language. Disparity between the pedagogical intention of the worksheet and learners’ interpretation of the message often creates instructional dissonance.Aim and setting: The aim of this nested study was to establish the quality, and (mis)use of worksheets as implemented by student teachers during their work-integrated learning stint in selected urban South African primary schools.Design and methodology: Using a self-designed grid, a qualitative document analysis underpinned by visual ethnography was conducted on 45 worksheets. These texts were prepared by the student teachers for literacy, numeracy and life skills lessons offered to 6- to 9-year-olds. Criteria used for the simple analysis included appropriateness for the age group, visual complexity, accuracy of language use, cultural compatibility, layout, clarity of instructions and alignment with expected learning outcomes.Findings: Findings suggested the hasty conceptualisation and creation, or inappropriate choice of worksheets used as learning support material. Apart from linguistic barriers because of poorly formulated tasks, the worksheets were generally culturally insensitive, and contained grammatical inaccuracies compounded by technical and design shortcomings. These lacunae defeated the pedagogical purpose of most worksheets and generated instructional dissonance.Conclusion and implications for teacher education: The careful crafting and implementation of worksheets coupled with sound content knowledge of language and literacy principles would alleviate learner bafflement and enhance the learning opportunity. We take the position that well-designed worksheets should serve a focused purpose and link directly to literacy and learning of the instructional language.
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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 | 0.019 | 0.056 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".