This is why we do it: Using a Design Based Approach to Optimize Student Learning in an Online Discussion Based Course
Bibliographic record
Abstract
Resume: La necessite d'offrir de bons cours en ligne s'est intensifiee en raison de la pandemie de COVID-19, avec la montee en fleche de l'education en ligne. L'enseignement en ligne est une experience differente de celle de l'enseignement en face a face. Dans un cours en ligne, une planification prealable et une conception de cours minutieuses sont essentielles a la reussite des etudiants et un cours en ligne bien concu est essentiel pour soutenir les experiences d'apprentissage des etudiants. Une facon de concevoir un cours efficace et entierement en ligne est de penser au cycle d'apprentissage des eleves (Lawson, 2001;Kolb, 1984;Carver et al., 2007;Murphrey, 2010;Bassanjav, 2013) bien avant le debut du cours, ce qui donne le temps aux instructeurs d'interagir avec leurs etudiants pendant le cours . Pour mieux comprendre comment la conception des cours et l'activite d'un instructeur affectent le cycle d'apprentissage des etudiants, nous avons explore les cycles d'apprentissage des etudiants dans trois instances d'un cours en ligne de niveau superieur offert dans un etablissement d'enseignement de premier plan au Canada. Dans cet article, l'utilisation par un instructeur d'un cycle d'apprentissage a travers les trois cours est etudiee, en mappant l'activite de l'instructeur sur l'activite des eleves. En utilisant les donnees du LMS PeppeR, nous nous sommes concentres sur la cartographie du cycle d'apprentissage prevu a travers les trois offres. Les resultats ont revele que les etudiants trouvaient leur rythme en ligne qui etait relativement constant tout au long du cours, jusqu'a la semaine 7 oh ils se sont concentres davantage sur le projet final. De plus, nous avons constate que la mise en place d'un cycle d'apprentissage bien concu donne a l'instructeur un cadre clair dans lequel il lui permet d'individualiser davantage l'instruction pendant le cours. Cette etude de cas sera utilisee comme base pour mener une analyse plus large des cours en ligne qui utilisent les cycles d'apprentissage comme modele pour faciliter les presences cognitives, sociales et pedagogiques.Alternate abstract:The need to deliver good online courses has intensified due to the COVID-19 pandemic, with the surge in online education. Teaching online is a different experience from that of teaching in a face-to-face setting. In an online course, careful prior planning and course design is crucial to student success and a well-designed online course is essential to support students' learning experiences. One way to design an effective, fully online course is to think about the student learning cycle (Lawson, 2001;Kolb, 1984;Carver et al., 2007;Murphrey, 2010;Bassanjav, 2013) well before the course begins, which gives instructors time during the course to interact with their students. To better understand how course design and an instructor's activity affects the students' learning cycle, we explored students' learning cycles across three instances of a graduate-level online course offered at a leading education institution in Canada. In this paper, one instructor's use of a learning cycle across the three courses is studied, by mapping the instructor's activity to student activity. Using data from the LMS PeppeR1, we focused on mapping the planned learning cycle across the three offerings. The results revealed that students found their rhythm online and it was was relatively consistent throughout the course, until week 7 when they became more focused on the final project. Also, we found that having a well1 designed learning cycle in place gives the instructor a clear framework in which to further individuate the instruction during the course. This case study will be used as the foundation for conducting a larger analysis of online courses that employ learning cycles as a model for facilitating cognitive, social and teaching presences.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".