Bootstrapping: The Emergent Technological Practices of Post-secondary Students with Mathematics Learning Disabilities
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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