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Record W2339137114 · doi:10.21432/t20s4f

Introducing Backchannel Technology into a Large Undergraduate Course | Introduction d’une technologie d’arrière-plan dans un vaste cours de premier cycle

2016· article· en· W2339137114 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsHumanitiesLibrary sciencePsychologyArtComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.286
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it