Interplay of Motivating and Demotivating Factors in an Online English Language Learning Classroom in Light of the Self-Determination Theory Continuum
Bibliographic record
Abstract
The online world has brought us together on one diverse platform, and communicating in a language that is acceptable to all becomes crucial, thus making English language a priority for students and professionals alike. However, the success of online English learning depends on a lot of factors that can motivate or demotivate the students in the learning process. This study aims to identify such factors and develop models to depict how the interactions among them can lead to success or failure of online learning courses. The paper applies method of literature review, focus group discussion, and the technique of interpretive structural modelling (ISM) to analyse the interplay between the factors which have been analysed in light of the self-determination theory continuum. Further, the analysis also supports an understanding of which ones are the primary drivers of student success, thus providing insights on ways to maximize those that motivate student learning and minimize those that demotivate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".