Using the 3E Framework in Promoting Adult Learners’ Success in Online Environments
Bibliographic record
Abstract
The growth of technology has facilitated a rapid expansion of online learning opportunities in postsecondary education, where adult learners are availing themselves of these classes and programs. To facilitate the success of adult learners in online environments, instructors must consider the learning characteristics of adult learners and intentionally design courses that leverage their strengths while addressing their challenges. The purpose of this paper is to utilize the 3E framework to analyze the implementation of e-learning technologies in order to achieve those goals. In this conceptual paper, first the 3E framework is described, and then the literature on adult learning in online environments is presented using a qualitative meta-synthesis approach. The 3E framework is then applied to examine e-learning technologies to highlight the implications this lens may have in the design and implementation of these technologies. La croissance de la technologie a facilité l’expansion rapide des occasions d’apprentissage en ligne dans le monde de l’éducation postsecondaire, où les adultes se prévalent de ces cours et ces programmes. Afin de promouvoir la réussite des apprenants adultes dans ces environnements en ligne, les instructeurs doivent tenir compte des caractéristiques d’apprentissage des apprenants adultes et ensuite concevoir des cours qui exploitent leurs forces tout en abordant leurs points faibles. À cette fin, la présente recherche emploie le cadre 3E (en anglais enhance, extend, empower: améliorer, accroitre, autonomiser) pour analyser la mise en œuvre de l’apprentissage électronique. Cet article conceptuel commence par décrire le cadre 3E pour ensuite présenter, par une synthèse méta-analytique et qualitative, la recherche portant sur l’apprentissage par les adultes dans les environnements en ligne. Par la suite, le cadre 3E est appliqué à l’étude des technologies en ligne pour mettre en valeur les incidences que pourraient avoir cette perspective sur la conception et la mise en œuvre de ces technologies. Mots clés : apprentissage en ligne, apprenants adultes, éducation postsecondaire
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".