Exploring the Efficacy, Attitude, and Challenges of Experiencing the Current EdTech Trends in English Language Learning
Bibliographic record
Abstract
This study investigated the usefulness and effectiveness of incorporating and engaging technology in second language learning and the problems encountered by students utilizing modern educational technology tools at Majmaah University. The study used a mixed-methods approach. To assess the preparedness and eagerness of EFL learners to utilize current educational technology (EdTech) in their language learning and to examine the attitudes of EFL learners towards various Ed Tech tools, a Likert questionnaire consisting of seven points, ranging from very frequent to never, is disseminated to students at various academic levels. To determine the attitudes of EFL learners, a questionnaire was prepared, ranging from exceptional to very poor. Furthermore, the research identifies the obstacles faced by EFL students. 75 students from various academic disciplines completed the questionnaire, while a semi-structured interview was conducted with seven students to get their genuine and sincere opinions and ideas. The study primarily examined the implications of technological advances on English as a Foreign Language (EFL) learners. Consequently, it was found that EFL learners were entering a new era of digital learning and were undoubtedly benefiting from it, as long as it was not utilized for nonsensical goals. It is essential, however, to tailor the use of Ed Tech tools to the unique learning goals and the level of competence of the learners. Although the study was done on a limited premise, however, the Ed Tech pedagogical implications could be generalized to all EFL learners.
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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 | 0.004 | 0.014 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 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".