Power to the learner! Integrating assistive technology with learning Strategies- reading Part two of a two-part series Assistive Technology for Children and Adults
Bibliographic record
Abstract
In 1997, the govern-ment of Ontario initiated the Learning Opportunities Task Force (LOTF) to investigate supports that would help stu-dents with learning disabili-ties to access and be success-ful in postsecondary studies. Cambrian College was cho-sen as a pilot site to offer a unique program for students with complex learning dis-abilities who were under-prepared for postsecondary studies. Some of these stu-dents had attempted postsec-ondary education, but they were not successful. Since the beginning of the LOTF initiatives, Cambrian College has developed a transition to college program designed to meet the individual learning needs of these students. The curriculum for this program is completely integrated with assistive technology and learning strategies. Students use these tools to enhance their skills for reading, writ-ing, math, and computer use. They learn how to learn effi-ciently, study effectively, and demonstrate their knowl-edge. They become strategic learners. How the integration of learning strategies with as-sistive technology can assist these students with reading difficulties will be discussed. This is the second in a two part series of articles about integrating assistive technology with learning strategies. These articles are based on research and from our experiences of teaching adult students with learning disabilities in a postsecond-ary environment. During the past 10 years, we have worked in a unique postsec-ondary program that incor-porates teaching college level English courses exclusively to students with learning dis-abilities. This curriculum was designed to integrate subject content with learning strat-egies in combination with assistive technology. This is a culmination of our expe-rience in the classroom and beyond that can be beneficial for students in the K-12 sys-tem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".