Introducing Backchannel Technology into a Large Undergraduate Course | Introduction d’une technologie d’arrière-plan dans un vaste cours de premier cycle
Bibliographic record
Abstract
Backchannel technology can be used to allow students in large lecture courses to communicate with each other and the instructor during the delivery of lecture content and class discussions. It can also be utilized by instructors to capture, summarize, and integrate student questions, ideas, and needs into course content both immediately and throughout the course. The authors integrated backchannel software in one of two sections of a course, leaving the other section as a control; combined, the two sections contained a total number of 871 students. Data was gathered comparing both groups using online surveys and semester grades; results showed that the section using backchannel software had higher class satisfaction and perception of engagement, used their mobile devices more for accessing class content, felt more comfortable participating in class discussions, and had a higher grade average than the section that did not. The authors also explore their own experiences of finding, integrating, and maintaining backchannel technology. La technologie d’arrière-plan peut permettre aux étudiants de grands cours magistraux de communiquer les uns avec les autres et avec l’instructeur durant le cours et les discussions en classe. Les instructeurs peuvent aussi l’utiliser pour saisir, résumer et intégrer les questions, idées et besoins des étudiants dans le contenu du cours, et ce, immédiatement et pendant toute la durée du cours. Les auteurs ont intégré un logiciel d’arrière-plan dans l’une des deux sections d’un cours, faisant de l’autre section son groupe témoin. Ensemble, les deux sections comprenaient 871 étudiants. Des données ont été recueillies pour comparer les deux groupes à l’aide de sondages en ligne et des notes du trimestre. Les résultats ont démontré que la section utilisant le logiciel d’arrière-plan avait une plus grande satisfaction et une meilleure perception de l’engagement, que ses étudiants se servaient de leurs appareils mobiles pour accéder à davantage de contenus, se sentaient plus à l’aise de prendre part aux discussions en classe et avaient une moyenne plus élevée que ceux du groupe qui n’avait pas accès au logiciel. Les auteurs explorent également leurs propres expériences pour trouver, intégrer et entretenir la technologie d’arrière-plan.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".