Bootstrapping: The Emergent Technological Practices of Post-secondary Students with Mathematics Learning Disabilities
Bibliographic record
Abstract
Drawn from an investigation of the emergent technological practices of post-secondary students with mathematics learning disabilities, this case study employs an enactivist framework in considering the bootstrapping processes our participants report engaging in when using personal electronic devices for academic support. Video-recorded, semi-structured interviews were conducted with nine post-secondary participants with mathematics learning disabilities in two western Canadian urban centres. Findings suggest that participants used technology to control and improve sensory input in order to better access mathematics course content and monitor the accuracy of their work, engage with alternate presentations of mathematical concepts to enhance their level of understanding, reduce workload, and improve organization. We discuss how their strategies in using technology relate to Bereiter’s categorization of bootstrapping resources (1985), including imitation, chance by selection, learning support systems, and piggybacking. Grounded in a “learner’s perspective,” this case study identifies technological adaptations and strategies that may be helpful to others with mathematics learning disabilities.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".