Interplaying Factors of Students Personal Characteristics in Online Learning Modality: Evidence in Asian Context
Bibliographic record
Abstract
Mapping the multidimensional impact of learner attributes on behavior demonstrates the importance of models in learning. To this purpose, we examined the correlations between strategies and student characteristics and utilized regression analysis to determine how learner attributes affect strategy selection. A cross-sectional study of 258 students demonstrated widespread strategy use, as well as statistically significant connections within and between the Strategy Inventory for Language Learning and Student Characteristics of Learning measures. Regression analysis found distinctions in the types of learner characteristics associated with strategy adoption, most notably between direct and indirect strategies. Instrumental motivation predicted both direct and indirect Strategy Inventory for Language Learning scores, but self-efficacy affected memory, cognitive, and compensatory strategies, and perseverance predicted reported metacognitive and emotional strategy choice levels. Additionally, a negative route coefficient occurred between persistence and compensation techniques and between competition and memory strategies, implying mediation and a high degree of complexity in the way learner traits impact behavior. The present study's findings have implications for prospective instructor techniques for motivating students to become fully involved in language learning via the online procedure.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".