Accent and Ugandan Students’ Comprehension of Mathematical Concepts and Terms: An Experimental Study
Bibliographic record
Abstract
The embrace of diversity and multiculturalism in education facilitates the broadening of students’ experiences as they engage with teachers and classmates from backgrounds different than their own. However, while the positive effects of diversity on students are apparent, few studies have examined possible negative challenges that diversity might have on students. Where most subject matter is taught via classroom lectures and the lecture material is presented by a speaker with a different accent than the student is used to hearing, does it make the material harder for the student to understand? On the other hand, could it increase the focus and engagement required by the students in the classroom, and in the process increase their understanding? In this vein, our research sought understand whether students’ learning of the subject matter hindered when they are taught material by a teacher with a different accent. To this end, we designed a simple experiment with a small group of undergraduate students in Uganda, to address this question, the result of which we present in this study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".