Bibliographic record
Abstract
To be proficient when learning a language, one needs to have an instilled culture to investigate and probe deeper into diversities of the underlying language aspects. An investigative culture and improved critical thinking skills during language learning requires a multifaceted approach that involves both the teacher and learners to engage cooperatively. When this culture has been instilled, teachers can impart to learners skills to analyse information and evaluate sources, yet, drawing conclusions based on evidence. This paper had its main aim of investigating whether instilling an investigative culture can enhance language learning for improved comprehension skills. For this investigation, a qualitative approach was applied and entrenched in a case study design where five conveniently sampled university English language teachers as initiators to implant an investigative culture to enhance language learning were nominated as participants. To collect data, semi structured interviews were administered and this assisted to have better understanding of the underlying factors caused by lacking culture to investigate matters for enhanced language learning. From this study it was divulged that (i) enhanced research skills and (ii) improved memory retention were the major findings as outcomes of an instilled investigative culture when learning languages. It is concluded and recommended by this paper that an investigative culture in language learning can lead to improved language proficiency, with established critical thinking skills that can help to enhance research skills, thereby preparing learners for real-world encounters.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".