Validating an Online Learning Dexterity Survey of University Students’ Online Learning Competence
Bibliographic record
Abstract
Abstract Competent online learners have dexterity as they can manoeuvre a wide range of learning technologies and online learning strategies to learn successfully. In this study, we approach university students’ online learning competence as ‘online learning dexterity’ or an ability to manage different aspects of online learning with appropriate technological and pedagogical strategies. Aligning with emerging visions of more individualised, flexible, multi-modal, and community-driven experiences through online learning in universities, we operationalised an Online Learning Dexterity Survey instrument that assesses online learning competence through six dimensions: (1) asynchronous learning dexterity (2) synchronous learning dexterity (3) self-directed learning dexterity (4) online collaboration dexterity (5) learning technologies dexterity, and (6) learning access dexterity. Construct validity was established through confirmatory factor analysis of responses from 273 university students and discriminant validity was established through cluster analysis. The three-cluster solution show that learning technologies dexterity and learning access dexterity can be used to identify student profiles with lower confidence in their technical competencies but the other dimensions reveal students’ pedagogical challenges with managing learning, online collaboration, and different online learning modalities. The contributions of online learning dexterity factors to improving the assessment and development of university students’ online learning competency are discussed.
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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 | 0.008 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".