EFL Learners’ Perceptions Regarding the Use of Busuu Application in Language Learning: Evaluating the Technology Acceptance Model (TAM)
Bibliographic record
Abstract
After the exceptional circumstances the whole world has experienced due to the COVID-19 pandemic, students have become digital natives who seek learning that overcomes the barriers of place and time. This mixed-methods study aimed at investigating EFL learners' perceptions regarding the application of Busuu in language learning, by applying the Technology Acceptance Model (TAM). The participants were 58 first secondary stage students in a Saudi private school. An online survey and a semi-structured interview were used to collect data. The results revealed that EFL learners have a positive attitude towards Mobile Assisted Language Learning (MALL). Additionally, according to the TAM, the participants found the language application Busuu useful and easy to use. They also thought of Busuu as a valuable resource for language learning, which increases their motivation to be autonomous learners. However, in addition to the TAM components, the results also showed that affordance and joyfulness could be strong indicators of learners' acceptance of a particular technology. Moreover, the results revealed that mobile applications might be more helpful for beginner learners than advanced ones. On the other hand, the findings also showed that Busuu might distract learners from doing their homework. Besides, the regular use of Busuu might be harmful to their eyes due to the small mobile screen size. For further research, the recommendation includes a large sample size in addition to a long-term study of EFL learners' perception towards Busuu in normal classroom circumstances.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".