The Effects of Technology on Learning English as a Foreign Language Among Female EFL Students at Effatt College: An Exploratory Study
Bibliographic record
Abstract
Technology offers modern educational tools for learning English as a foreign language. For the current study, the effects of technology on learning English as a foreign language among female EFL students at Effatt College were investigated. To achieve this aim, questionnaires with Likert scale questions were used in the study. The questionnaire was divided into two parts. The first part began with two general questions about age and educational level. The second part included four specific questions regarding technology tools that enhance learning the English language. Thirty-Six participants were included in this study. The data was analyzed using the Statistical Package for Social Sciences (SPSS) to obtain accurate results. The findings clearly indicate that computer software, social networking websites, online videos, audio tools (i.e., YouTube, Skype, MP3 players), and smart phone and tablet apps have a positive impact on learning English as a foreign language. Therefore, technology tools can clearly be effective in improving the students’ language and communication skills.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".