Vocabulary Learning Strategies Employed by Undergraduate EFL Jordanian Students
Bibliographic record
Abstract
The present study investigates the various vocabulary learning strategies (VLSs) used by undergraduate Jordanian students majoring English Language and Literature in Jordanian universities. The five categories of the vocabulary learning strategies (Memory, Determination, Social, Cognitive, and Metacognitive) were used in this study following Schmitt’s taxonomy. For this purpose, a questionnaire containing forty items selected from Schmitt’s (1997) Vocabulary Learning Strategies Questionnaire (VLSQ) was administered to a pool of 110 Jordanian students majoring in English Language and Literature from eight Jordanian universities. This testing instrument was used to reveal the types of vocabulary learning strategies used by the participants, to discover the most and least frequently used VLS employed by them, and to know the main patterns of variation of the participants’ choice of VLSs if they are high, medium, or low VLS users. The descriptive analysis of the study showed that Jordanian EFL learners were “medium” strategy users overall. With regard to strategy categories, the results revealed that Memory strategies were the most frequently employed by them and Metacognitive strategies were the least frequently used strategies among them. Although the participants were medium strategy users, the results of the VLSQ revealed that some individual strategies were employed at a low level. This result leads to adopt the learners’ individual vocabulary learning strategy as an important variable in future research. The findings of this study will be advantageous to language instructors to improve effective vocabulary teaching techniques and curriculum designers to provide learners with preferable vocabulary learning strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".