Situated Learning Theory: The Application of Grammatical Structure (Gerunds and Infinitives) in Developing EFL Students’ Cover Letters
Bibliographic record
Abstract
Focusing on integrating teaching and learning grammatical structures in real actions and practices has been critical and one of the cornerstones in the Teaching English as a Second Language (TESOL) domain. This paper emphasizes exposing EFL learners to actual tasks and opportunities they can benefit from in their future careers, along with understanding the meanings and functions of gerunds and infinitives. The situated learning theory was used to frame the current study on EFL students taking the grammar 1 course. The number of students was 60, but the study sample was seven students, which selected randomly. Students were guided to use gerunds and infinitives to develop a cover letter for one of the agencies they selected rather than apply those rules to artificial tasks. The sampled data of the qualitative research was used to analyze students’ cover letters by investigating two main concepts. First, their use of gerunds and infinitives; second, the elements of writing professional cover letters. The findings showed students’ accurate usage of gerunds and infinitives in different phrases, such as opening, closing, and body paragraphs, to present their educational background and experiences. Thus, the findings showed that the cover letters that have been written by the participants were not the best, but they achieved the general guidelines of creating cover letters and applied the targeted rules. EFL teachers are recommended to enhance their students with variety of authentic tasks that integrate the use of grammatical structures and the practice of using these structurers in real tasks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".