The Impact of Technology on the Motivation of English Language Learners in Online Settings
Bibliographic record
Abstract
As online learning continues to gain prominence, understanding the factors influencing learner motivation in virtual environments becomes crucial. This study aimed to investigate the impact of technology and circumstantial factors on the motivation of English language learners engaged in online settings. Through an online questionnaire, data will be collected from approximately 20 participants at Taif University or via crowdsourcing platforms. The questionnaire employed a five-point Likert scale to assess learner motivation and gather ratings on ten factors: five technology-related (device age, speed, display, audio quality, mobility) and five circumstantial (device availability, proficiency, commuting feasibility, external responsibilities, interest in the field). Statistical analysis using Spearman's correlation was conducted to examine the relationships between these factors and motivation. The research revealed that device and internet speed stood out as the most crucial technological determinant of student motivation, with a strong positive correlation coefficient of 0.63. The quality of audio during online lessons also emerged as significant, with a moderate positive correlation of 0.43 with motivation. Interestingly, device mobility did not exhibit a notable correlation with student motivation. Regarding circumstantial factors, students' general interest in pursuing the degree requiring online English learning was the most influential factor affecting motivation, with a strong positive correlation coefficient of 0.53. Students' proficiency in using devices for online learning was another crucial factor, showing a moderate positive correlation of 0.4 with motivation. Unexpectedly, proximity to educational institutions did not correlate significantly with motivation for online learning. However, students with additional responsibilities tended to be more motivated, potentially due to the flexibility of online education.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".