Formative assessment as ‘formative pedagogy’ in Grade 3 mathematics
Bibliographic record
Abstract
Background: Formative assessment, as an integral component of teaching, has recently gained prominence in educational environments globally. Poor performances in mathematics by learners in early grades, and its negative effect on later learning, have been an ongoing concern in South African schools. Several former studies tend to generalise the pedagogical reasons for learners’ underperformance in Foundation Phase teaching.Aim: This case study of selected Grade 3 teachers examined how the teachers integrated formative assessment into their pedagogy, with the purpose of gaining insight into teachers’ understanding of the developmental aspects of learning in mathematics.Setting: This study was conducted at four schools in a selected district in the Gauteng Province.Methods: Data were mainly collected through focus group interviews and observations of at least three classroom sessions for each teacher of mathematics, thereby gaining an overview of their formative assessment practices.Results: This article reports on the two strongest themes to have emerged from the case study, which were teachers’ tokenistic use of ‘Assessment for Learning’ strategies and teachers’ awareness of learning processes and curriculum requirements.Conclusion: The study’s main conclusion was that teachers are likely to practise formative assessment more intuitively if they had a sound knowledge of children’s mathematical cognition and conceptual development. This study pointed out that formative assessment is a co-constructed activity involving the teacher, the learner and peers rather than a teacher-directed activity. The study recommends how continuous professional learning initiatives can design initiatives that integrate research-based knowledge of children’s learning of early grades mathematics.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".