Adult Learner Perspectives on Skill- and Life-Based Outcomes Following Literacy Remediation
Bibliographic record
Abstract
Using the situated expectancy value theory (SEVT), we explored self-perceived attainment perspectives of adults with low literacy on skill-based (i.e., reading, writing, listening, speaking) and life-based (i.e., management of day-to-day challenges, use of skills in daily living, confidence) improvements following a literacy-focused remediation program. Participants (N = 103; Canadian, urban adults) completed a remediation program for low literacy via one-on-one tutoring over a period of 1 year. Four to six months into the program, participants completed a survey that asked about their perspectives regarding improvements in skill- and life-based areas of functioning. A series of Chi-Square tests provided evidence for self-perceived improvements in skill-based functioning in reading, writing, listening and speaking. Perceived improvements were not noted in areas not targeted by the remediation, namely, math and computer literacy. Further, there was a significant, positive correlation between self-perceived improvement and (1) self-perceived ability to deal with daily challenges, (2) self-reported use of literacy skills in day-to-day activities, and (3) overall confidence. Together, these findings underscore the importance of including activity- and participation-based outcome measures when evaluating adult literacy remediation. In addition, this work demonstrates an application of SEVT to explore changes over time in continuing adult education.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".