TeRMEd: a framework for educators to aid in the design and evaluation of technology-enhanced resources in mathematics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we describe a classification framework which we developed and that practitioners find useful and usable in the design and evaluation of technology-enhanced resources and that incorporates factors which impact on student engagement with such resources. The classifications in the TeRMEd framework were derived from an evaluation of technology-enhanced resources, trialed within non-specialist first-year undergraduate mathematics modules. The theoretical foundation included a literature review, detailed analysis of resource trials and outcomes of the resource evaluations. Subsequently, the TeRMEd framework was evaluated by lecturers involved in the resource trials. Using the TeRMEd framework for technology integration was shown to be beneficial in terms of both design and evaluation. By carefully considering the classifications, practitioners can also encourage student engagement with resources.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 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 it