Students' difficulty with proportional reasoning in a university quantitative literacy course : Part 2 : HELTASA 2012 Special Section
Bibliographic record
Abstract
The ability to reason about changes in quantities expressed in relative terms is essential for a critical awareness of data use in society. The authors regard this way of thinking, which they term 'proportional comparison', as a threshold concept (Meyer and Land 2003) for academic quantitative literacy. The extent and nature of students' difficulties with learning this concept in a quantitative literacy course for Law students were studied using a phenomenographic type of analysis of students' responses to four test questions, written at different times. Less than a quarter of the students could be said to have learned this concept at any time and improvements in students' ability to reason about relative differences were very slight. This supports the view that the learning of 'troublesome' (Meyer and Land 2003) quantitative literacy concepts takes a long time. Thus, it should be addressed in a sustained way, and integrated into students' programme of 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 | 0.001 | 0.001 |
| 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".