Creating a Space for Self-Transformation: Factors of Success for Adult Literacy Learners with Specific Learning Difficulties
Bibliographic record
Abstract
A significant number of adults in adult literacy programs in Ontario have \nspecific learning difficulties. This study sought to examine the holistic factors \nthat contributed to these learners achieving their goals. Through a case study \ndesign, the data revealed that a combination of specific learning methods and \nstrategies, along with particular characteristics of the instructor, participant, and \nclass, and the evidence of self-transformation all seemed to contribute to the \nparticipant's success in the program. Instructor-directed teaching and cooperative \nlearning were the main learning methods used in the class. General \nlearning strategies employed were the use of core curriculum and authentic \ndocuments, and using phonics, repetition, assistive resources, and using \nactivities that appealed to various learning styles. The instructor had a history of \nboth professional development in the area of learning disabilities as well as \nexperience working with learners who had specific learning difficulties. There \nalso seemed to be a goodness of fit between the participant and the instructor. \nSeveral characteristics of the participant seemed to aid in his success: his positive \nself-esteem, self-advocacy skills, self-determination, self-awareness, and the fact \nthat he enjoyed learning. The size (3-5 people) and type of class (small group) \nalso seemed to have an impact. Finally, evidence that the participant went \nthrough a self-transformation seemed to contribute to a positive learner identity. \nThese results have implications for practice, theory, and further research in adult \neducation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".