Teaching Strategies that Motivate English Language Adult Literacy Learners to Invest in their Education: A Literature Review
Bibliographic record
Abstract
Canadian English language programs have seen a recent increase in enrolment by English as a Second Language adult literacy learners. To date, minimal research has been conducted with these learners, leaving literacy teachers with little guidance. In our literature review we found that, because learners often lose motivation due to their lack of or limited education, building motivation and investment must be at the heart of lesson design when teaching adult literacy learners. Thus, we adopted a transformative and post-structuralist framework to extend proven sociocultural theories to the adult literacy learner population. Our article reviewed past literature, incorporated the autobiographical narratives of experienced literacy teachers and provided six teaching strategies for increasing investment and motivation in adult literacy learners: providing relevance, addressing settlement needs, incorporating life experiences, encouraging learner autonomy, promoting collaborative learning, and building self-efficacy. Our article will demonstrate that further research is required in the arena of adult low literacy English language learners. Keywordsmotivation, investment, post-structuralist and transformative framework, teaching strategies, ESL adult literacy learners, limited formal education, English language learner, literature review.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".