Additional-Language Learning, Interdisciplinary Instruction, and Technology
Bibliographic record
Abstract
In this issue, we focus on additional-language learning, interdisciplinary instruction, and technology. We begin with two additional-language articles, including one on foreign language teachers’ and students’ attitudes toward oral-corrective feedback in traditional and technology-enhanced classes, and another on the relationship between English-medium instruction (EMI) effectiveness factors and students’ EMI course satisfaction. Then, we present three interdisciplinary teaching articles, including one on fostering interdisciplinary cooperation and integration of technology in the teaching about socio-scientific issues, another on teaching literacy in an elementary-school classroom while incorporating Indigenous literacy scholarship, and a third on integrating political, social, and contemporary paradigms to teach biology. We then share two technology-oriented articles, including one on evaluating the readiness of students to use Virtual Reality technology to learn physics, and another describing gender differences regarding the level of emerging digital technologies’ competencies of STEM pre-service teachers. Five additional articles are presented on the topics of intersectional pedagogy, student engagement, transition from pre-service to in-service teaching, educational leadership, and teaching in low-economic background schools. This issue includes two book reviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".