Language Learning, Technology, Reading and Narrative Imagination, and Teaching Strategies
Bibliographic record
Abstract
In this issue, we focus on language learning, technology, reading and narrative imagination, teaching strategies, and several additional topics. We begin with two articles related to language learning. The articles explore the perceptions of language-teacher candidates regarding their own English-language learning experiences during primary school, as well as another that discusses how language teachers intend to enhance their instructional practices by using flashcards. Additionally, the effectiveness of a flashcard-based intervention in enhancing French vocabulary and acquisition among Sri Lankan undergraduate learners is examined. Then, we present two articles on technology, including one that discusses an interpretive approach to better understand how digital competency is integrated into teacher-education programs in the Canadian province of Quebec and another that explores how AI can facilitate the integration of technology into educational settings. We then share three articles on reading and narrative imagination, including one that argues that stories equip children with the appropriate dispositions to make meaningful contributions to the societies in which they live, while offering teachers a viable method for making classrooms culturally responsive. Another surveys the recent history and current status of reading reforms in Canada. The third discusses the role of engaged scholarly relationships in enhancing the pedagogy of reading proficiency. Then, we share four articles on teaching strategies, including examination of how the integration of research on teaching and learning can serve as a strategic axis for higher education institutions to respond to these challenges, identification of the views of prospective biology students on the field-trip experience as an outdoor learning activity in both educational and social contexts, discussion of the integration of Indigenous knowledge into science learning programs, and investigation of how cooperative learning fosters the development of abilities essential for collaboration, creativity, interest, engagement, and self-regulation. One additional article is presented on teacher stress, burnout, and turnover. This issue concludes with six book reviews.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".