MétaCan
Menu
Back to cohort

Use of Lecture Capture in Undergraduate Biological Science Education

2013· article· en· W2133510719 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsUniversity of GuelphUniversity of Guelph-Humber
FundersUniversity of Guelph
KeywordsAttendancePsychologyClass (philosophy)Mathematics educationHumanitiesArtComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study examined the use of lecture capture in students in a large 3rd year undergraduate biological science course at the University of Guelph. Data regarding viewing behaviour, academic performance, and attendance were analyzed in relation to student learning approach (as assessed by the R-SPQ-2F), gender, and year of post-secondary education. It was found that relative to historic controls, students provided lecture capture videos increased their final exam grade by approximately 5%. It was also found that learning approach was significantly related to video viewing behaviour, final exam performance, and attendance, with a deep learning approach being associated with more video views, better performance, and a greater tendency to watch videos to master and review material. A surface approach showed contrasting associations. Moreover, a higher deep approach score was related to fewer absences, while a higher surface approach score was related to more absences and increased the likelihood of a student missing a class. Gender also influenced viewing behaviour, with females being more likely than males to watch videos to generate notes and to review material. This research demonstrates that learning approach and gender are significant predictors of lecture capture behaviour, performance, and/or attendance in biological science education, and provides support for the use of lecture capture as a tool to improve academic performance. Cette étude examine l’utilisation de la capture de cours dans une grande classe d’étudiants de premier cycle inscrits à un cours de sciences biologiques de troisième année. Les données relatives au comportement de visionnement des vidéos, aux résultats académiques et à l’assiduité ont été analysées en relation avec l’approche d’apprentissage des étudiants (telle que mesurée par le R-SPQ-2F), le sexe et l’année d’études post-secondaires. Cette étude a montré que, comparativement aux contrôles historiques, les notes obtenues aux examens finals par les étudiants exposés à des vidéos académiques étaient de 5 % supérieures. L’étude a également indiqué que l’approche d’apprentissage était liée de façon significative au comportement de visionnement, aux résultats obtenus aux examens finals et à l’assiduité, et que l’approche en profondeur était liée à un nombre supérieur de visionnements des vidéos, à de meilleurs résultats et à une tendance accrue à regarder les vidéos afin de maîtriser et de réviser la matière. L’approche en surface a indiqué des associations contrastées. De plus, un score supérieur d’approche en profondeur était lié à un nombre moins élevé d’absences alors que l’approche en surface était liée à davantage d’absences et qu’elle augmentait les possibilités que les étudiants soient absents en classe. Le sexe avait également une incidence sur le comportement de visionnement, les femmes ayant davantage tendance à regarder les vidéos afin de prendre des notes et de réviser la matière que les hommes. Cette recherche a montré que l’approche d’apprentissage et le sexe sont des indicateurs importants de comportement en ce qui concerne la capture de cours, la performance et/ou l’assiduité dans le domaine des sciences biologiques et qu’elle offre un soutien efficace pour l’utilisation de la capture de cours en tant qu’outil pour améliorer la performance académique.

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)0.013
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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 score0.377
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.102
GPT teacher head0.399
Teacher spread0.296 · 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