Factors Influencing Self-Regulation in E-learning 2.0: Confirmatory Factor Model | Facteurs qui influencent la maîtrise de soi en cyberapprentissage 2.0 : modèle de facteur confirmative
Bibliographic record
Abstract
The importance of self-regulation in e-learning has been well noted in research. Relevant studies have shown a consistent positive correlation between learners’ self-regulation and their success rate in e-learning. Increasing attention has been paid to developing learners’ self-regulated abilities in e-learning. For students, what and how to learn are largely predetermined by the learning environment provided by their institutions. Environmental determinants play a key role in shaping self-regulation in the learning process. This paper reports a study on the influences of the e-learning 2.0 environment on self-regulation. The study identified the factors that influence self-regulation in such an environment and determine the relationships between the factors and self-regulation. A theoretical model to categorize the success factors for self-regulated learning was proposed for this kind of environment. Based on the model, a questionnaire was designed and administered to more than two hundred and fifty distance learning students in Beijing and Hong Kong. Through structural equation modeling (SEM) technique, relationships between environmental factors and self-regulation were analyzed. Statistical results showed that several factors affect self-regulation in the e-learning 2.0 environment. They include system quality, information quality, service quality, and user satisfaction. L’importance de la maîtrise de soi en cyberapprentissage a été bien étudiée. Les études pertinentes ont démontré une corrélation positive uniforme entre la maîtrise de soi des apprenants et leurs taux de réussite en apprentissage en ligne. Une attention croissante a été portée au développement des aptitudes de maîtrise de soi des élèves en cyberapprentissage. Pour les élèves, quoi apprendre et comment sont des questions principalement prédéterminées par l’environnement d’apprentissage qu’offrent leurs établissements. Les déterminants environnementaux jouent un rôle clé pour modeler la maîtrise de soi dans le processus d’apprentissage. Cet article rapporte une étude sur les influences de l’environnement de cyberapprentissage 2.0 sur la maîtrise de soi. L’étude a cerné les facteurs qui, dans un tel environnement, influencent la maîtrise de soi et déterminent les relations entre les facteurs et la maîtrise de soi. Un modèle théorique de catégorisation des facteurs de réussite pour l’apprentissage autogéré a été proposé pour ce type d’environnement. Un questionnaire a été conçu selon ce modèle et plus de deux cent cinquante élèves en téléapprentissage à Beijing et à Hong Kong y ont répondu. À l’aide d’une technique de modélisation par équation structurelle, les relations entre les facteurs environnementaux et l’autogestion ont été analysées. Les résultats statistiques ont démontré que plusieurs facteurs affectent l’autogestion dans l’environnement de cyberapprentissage 2.0. Ceux-ci comprennent la qualité du système, la qualité de l’information, la qualité du service et la satisfaction de l’usager.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".