The Impact of Strategic Notetaking on EFL learners’ Academic Performance in Jordan
Bibliographic record
Abstract
This study investigates the impact of strategic notetaking on English as Foreign Language (EFL) learners’ academic performance among university students in Jordan. Thus, we hypothesized that there is a significant and positive impact of strategic note-taking on EFL learners’ academic performance. To confirm this hypothesis, descriptive research design was applied in this study. 384 (three hundred and eighty-four) respondents were randomly selected from the four public universities in Jordan. This study adapted instruments which include strategic note taking and students’ academic performance measurement items and the data obtained was analysed through Statistical Package for the Social Science (SPSS-22). The result showed that the strategic note taking (i.e. independent variable) has significant effects on EFL learners’ academic performance (R2 =.919). Moreover, the strategic notetaking made the significant contribution (Beta= .449; t= 18.714; P <0.05) to the prediction of EFL learners’ academic performance. In line with the findings, this study emphasised and explained the impact of strategic notetaking and how to improve EFL learners’ level of notetaking for better academic performance in Jordan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".