The Development of New Teaching Strategies of Speaking and Reading Skills among EFL Learners in Jordan
Bibliographic record
Abstract
This study aims to present effective teaching strategies for speaking and reading skills among EFL learners at Al-Zaytoonah University in Jordan. In order to achieve this study, a quasi-experimental research design was employed focusing on the development and evaluation of active learning strategies for instructional lessons for first-year EFL students. Deeply, it aims to involve the creation of active learning strategies for these EFL learners by implementing an intervention to incorporate interactive activities and real-world problem-solving tasks to engage students actively in the learning process. It is designed to align with the common curriculum standards and learning objectives for reading and speaking EFL skills among these students. Then, an interview and a questionnaire were conducted among participants. The study revealed teaching reading and speaking skills with active and digital approaches improves English learning. According to the post-test findings, questionnaires, and interviews, digital learning tools like TED Talks and online courses like Perfectly Spoken help students improve their language skills, especially when combined with active classroom teaching. The results showed that EFL students value instructor input for developing speaking and reading. Setting personal learning goals and picking instructional materials boosts motivation and skill development. Grouping and achieving academic objectives assist students in building speaking and reading skills by allowing them to communicate with peers. The interviews showed that choosing the right time and place to study improves learning, and the test results showed that the experimental group that used active/innovative teaching methods made more progress than the control group. in regard to the development of new teaching strategies of speaking and reading skills among EFL learners in Jordan, it is important to improve EFL learners' reading and speaking skills, internet courses and educational video sites should be used alongside traditional teaching techniques. Such findings may help academic institutions e.g., universities and schools in Jordanian context. Policy-makers and EFL learners and teachers can combine technology, self-paced learning, and collaborative learning to improve student performance and enable real-world language use. They emphasise supporting learners in developing personal goals and creating a collaborative and participatory atmosphere.
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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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".