A latent profile analysis of students’ motivation of engaging in one-to-one computing environment for English learning
Bibliographic record
Abstract
This study used latent profile analysis to cluster students into three groups with homogenous motivational profiles based on self-reported self-efficacy, task value and task anxiety measures obtained from 263 middle school students. The results demonstrated that there were distinct motivation profiles among students while engaging in a one-to-one computing environment for English learning, which resulted in differences on their performance. In general, this eLearning environment had a significant positive effect on students’ learning achievements regardless of various motivation profiles. But students with high self-efficacy, task value while low task anxiety performed better than those in other profiles. This study also suggested that task anxiety impeded students from benefiting from the one-to-one computing environment, but it could not significantly affect students’ learning outcomes. The profiling of student motivation orientations enhanced our understanding of the complex interactions of various motivational components and extended our existing knowledge in this emerging area of student learning. Besides, the findings inform future interventions in curriculum design and effective scaffoldings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".