Utilizing WebQuests for Enhancing Teaching Skills of Saudi Pre-Service Teachers of English as a Foreign Language
Bibliographic record
Abstract
Purpose: There is need to focus on extensive use of technology in teaching and learning process, since the teachers are provided with well-organized WebQuests that are beneficial for developing effective teaching skills. The study aims to investigate the extent of the effects of WebQuests on the teaching skills and performance of pre-service teachers of English at the College of Education of King Khalid University. Methodology: The study sample, which included 35 students of the general diploma in English, were divided into two groups: experimental and control. The members of the control group were supervised in the traditional way during their teaching practice, and the members of the experimental group were given WebQuests so that they could surf the internet under the guidance of their supervisor and find the information they needed about teaching skills. The teaching performance of the teachers of both groups was assessed via a teaching performance observation form. The data collected through classroom observation was analyzed using SPSS. The differences between the teachers of both the groups in terms of the teaching skills were calculated using Mann-Whitney U test. Findings: Statistically significant differences were found in the rank means of the participants of the control and the experimental groups regarding their lesson planning and teaching skills. The results were favorable for the teachers of the experimental group; however, no significant difference was found between the scores of the experimental and the control groups in terms of lesson evaluation skills. Originality: The use of WebQuests significantly enhances the teaching skills of the students of the general diploma in English.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".