Teaching Competencies for the Online Environment | Enseigner les compétences pour l’environnement en ligne
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goals of this study are to identify key competency areas that lead to success in online instruction and to develop a framework that supports professional development and self-assessment. To identify the key competency areas, skills and behaviours presented within current literature were analyzed. Secondly, gaps were identified and levels of competence were determined within each key competency area. The resulting analysis produced the Online Teaching Competency (OTC) Matrix including five competency areas: Community & Netiquette, Active Teaching/Facilitating, Instructional Design, Tools & Technology, and Leadership & Instruction. This leveled competency matrix can be used to inform professional development in the online teaching environment and is also a useful guide in the areas of self-assessment, portfolio design, and the development and evaluation of professional development opportunities. Cette étude a comme objectifs de cerner les principaux domaines de compétences qui mènent à la réussite de l’instruction en ligne et de développer un cadre qui soutient le développement professionnel et l’autoévaluation. Afin de cerner les principaux domaines de compétences, une analyse des aptitudes et comportements présentés dans les études actuelles a été réalisée. Deuxièmement, les écueils ont été cernés et les niveaux de compétence ont été déterminés au sein de chaque domaine de compétences. L’analyse qui en a résulté a généré la matrice des compétences de l’enseignement en ligne, comprenant cinq domaines de compétences : collectivité et nétiquette, enseignement actif/animation active, conception de l’instruction, outils et technologie, ainsi que leadership et instruction. Cette matrice graduée peut servir à façonner le développement professionnel dans un environnement d’enseignement en ligne. Elle sert aussi de guide pratique pour les domaines de l’auto-évaluation, de la conception de portfolio et du développement et de l’évaluation des occasions de développement professionnel.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it