Student perspectives on group work in support of the learning of mathematics at high school and at a university of technology
Bibliographic record
Abstract
Debates on improving performance in science and engineering at higher education institutions have stressed the need for institutions to adopt pedagogic practices appropriate for the setting. In this paper we contribute to this debate by presenting the results of empirical research conducted in a first-year foundation mathematics course for Civil Engineering students at a University of Technology in South Africa. Using the perspective of learning as participation in a community as a theoretical framework, the paper focuses on a particular type of student learning community, that is, small group work for the learning of mathematics. We use individual interviews to investigate students' perspectives on small group work in support of their learning of mathematics at high school and in the foundation mathematics course. The results suggest that students have considerable experience of working in groups inside and outside the classroom at school, and they identify conditions conducive for group work, including having a sense of belonging in a group. They value group work for providing support that may not be provided by the lecturer, for example, by obtaining alternative explanations (often in their home language), sharing ideas on problem solving, and getting immediate feedback. We argue that higher education institutions should draw on students' experience of group work and create the space for this type of student learning community both inside and outside the mathematics classroom. We also use the empirical results to develop the notion of “community” as described in the theoretical perspective of learning.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".