Strengthening Deeper Learning Through Virtual Teams in E-learning: A Synthesis of Determinants and Best Practices
Bibliographic record
Abstract
Globally, e-learning is gaining popularity as its potential contributions to economic and social development are recognised. However, its full potential has not been realised, as most e-learning practices merely replicate traditional existing teaching methods and have not fully exploited the interactive and social components of peer learning. Recently, there has been an increased focus on learning in higher educational settings, in particular, a focus on the skills and knowledge that reinforce each other and together promote learning (Chow, 2010). In other words research shows that to be successful all students must have access to educational opportunities that foster learning. Virtual teams (VT) are said to foster deeper learning, but have not been empirically studied in the academic sphere, and little is known about their effectiveness as a learning mechanism in e-learning. In this paper the findings of a systemic review and interpretive synthesis of the body of literature on e-learning and VT are presented. The objective of the study was to identify the core skills and knowledge from research that reinforce each other and together promote learning. The results from this study will strengthen e-learning program planning and delivery within higher education centres that are already engaged in e-learning, as well as convey important best practices for learning centres at the beginning stages of e-learning development. Presented is an e-learning framework, which may serve as the foundation of future empirical studies in e-learning. Resume Renforcer l'apprentissage plus approfondi en passant par des equipes virtuelles dans l'apprentissage en ligne : Une synthese des determinants et des meilleures pratiques A l'echelle mondiale, l'apprentissage en ligne gagne en popularite puisque ses contributions eventuelles au developpement economique et social sont reconnues. Cependant, son plein potentiel n'a pas ete realise, car la plupart des pratiques d'apprentissage en ligne ne font que simplement reproduire les methodes d'enseignement traditionnelles existantes et n’ont pas pleinement exploite les composantes interactives et sociales de l'apprentissage par les pairs. Recemment, il y a eu une focalisation accrue sur l’apprentissage plus approfondi dans des milieux d'enseignement superieur, en particulier, l'accent sur les competences et les connaissances qui se renforcent mutuellement et, ensemble, favorisent un apprentissage plus approfondi (Chow, 2010). Autrement dit, la recherche montre que pour reussir, tous les etudiants doivent avoir acces a des possibilites educatives qui favorisent un apprentissage plus approfondi. Les equipes virtuelles (EV) sont dites de favoriser l'apprentissage « plus approfondi », mais elles n'ont pas ete empiriquement etudiees dans la sphere academique, et on en sait peu sur leur efficacite en tant que mecanisme d'apprentissage en apprentissage en ligne. Dans cet article, les resultats d'une revue systematique et d’une synthese interpretative de la litterature sur l'apprentissage en ligne et les equipes virtuelles sont presentes. L'objectif de l'etude etait d'identifier les competences de base et les connaissances issues de la recherche qui se renforcent mutuellement et, ensemble, favorisent un apprentissage plus approfondi. Les resultats de cette etude permettront de renforcer la planification de programme et la livraison d’apprentissage en ligne dans les centres d'enseignement superieur qui sont deja impliques dans l'apprentissage en ligne, ainsi que de transmettre d'importantes meilleures pratiques pour les centres d'apprentissage qui en sont aux premiers stades du developpement de l'apprentissage en ligne. On presente un cadre de reference d'apprentissage en ligne, qui peut servir de base a de futures etudes empiriques en apprentissage en ligne.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".