The Efficacy of the Concept-Rich Instruction with University Pre-Service Teachers in a Tanzanian Context Using Vygotskian Perspective
Bibliographic record
Abstract
This paper presents the findings of a pilot study on concept-rich instruction with university pre-service teachers in a Tanzanian context. The concept-rich instruction is an instructional approach which is used to develop students’ understanding of a mathematical concept (Ben-Hur, 2006). I conducted a pilot study to determine the efficacy of the concept-rich instruction to university pre-service teachers in Tanzania using Vygotskian perspective. I used a reflective journal and pre-test questionnaire to collect data while implementing the CRI in a daylong research meeting. After the pilot study, it was found that the concept-rich instruction helped preservice teachers to develop their understanding of a concept taught at schools in different ways, including defining a concept in multiple ways and relating a concept with local materials available in their daily environment. The findings have implications in the teaching and learning of the mathematical concepts that are taught at schools to the university pre-service teachers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".