How people learn in an asynchronous online learning environment: The relationships between graduate students’ learning strategies and learning satisfaction | Comment apprennent les gens dans un environnement d’apprentissage en ligne asynchrone
Bibliographic record
Abstract
The purpose of this study was to examine the relationships between learners’ learning strategies and learning satisfaction in an asynchronous online learning environment, in an attempt to shed some light on how people learn in an online learning environment. One hundred and sixteen graduate students who were taking online learning courses participated in this study. The result revealed that ‘metacognitive strategy’ and ‘time and study environment’ had positive correlations with learners’ satisfaction, while ‘help seeking’ had a negative correlation. The findings of a multiple regression analysis showed that ‘metacognitive strategy’ and ‘peer learning’ led to learners’ satisfaction in an online learning environment. The findings of this study contribute to a better understanding of how successful learning occurs in an online learning environment, and provide recommendations on designing an effective online learning. L’objet de cette étude était d’examiner les relations entre les stratégies d’apprentissage des apprenants et la satisfaction liée à l’apprentissage dans un environnement asynchrone d’apprentissage en ligne, dans le but de faire la lumière sur les façons dont les gens apprennent dans un environnement d’apprentissage en ligne. Cent seize étudiants aux cycles supérieurs qui suivaient des cours en ligne ont pris part à cette étude. Les résultats ont révélé que la « stratégie métacognitive » et « le moment et l’environnement pour l’étude » avaient des corrélations positives avec la satisfaction des apprenants, alors que « demander de l’aide » avait une corrélation négative. Les conclusions d’une analyse de régression multiple ont démontré que la « stratégie métacognitive » et « l’apprentissage entre pairs » avaient des corrélations positives avec la satisfaction des apprenants dans un environnement d’apprentissage en ligne. Les conclusions de cette étude contribuent à une meilleure compréhension des façons dont un apprentissage réussi se produit dans un environnement d’apprentissage en ligne et fournissent des recommandations sur la conception d’un apprentissage en ligne efficace.
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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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".