MétaCan
Menu
Back to cohort

Examining the Use of Lecture Capture Technology: Implications for Teaching and Learning

2016· article· en· W2410778309 on OpenAlexaffvenue
Jovan Groen, Brenna Quigley, Yves Herry

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAttendanceClass (philosophy)Mathematics educationPsychologyQualitative propertyComputer science

Abstract

fetched live from OpenAlex

This study sought to provide a better understanding of how lecture capture technology is used by students and how its use is related to student satisfaction, attendance, and academic performance. Using a mixed method design with both quantitative and qualitative methods to collect data, instruments included a student questionnaire, interviews and focus groups, lecture capture usage statistics, and grades. Results showed that 63% of students were satisfied with lecture capture and 75% of students indicated that it had facilitated their learning. Students primarily used the recordings to learn what they had missed while in class (79%) or because they were absent (72%). 70% of students reported having watched at least 50% of the recorded material (27% watched all the material), and only 8% of students did not view any recordings. Student satisfaction had a significant positive relationship with the number of lectures viewed. In regards to attendance, the self-reported data from students indicated that 61% of students did not miss classes (none or just one class). Students with the greatest number of missed classes viewed the recordings more frequently. As for student achievement, 68% of students indicated that lecture capture helped them to achieve better grades. Students with lower course marks accessed the recordings more frequently than those with higher marks. Instructor perceptions were that lecture capture predominately helped average to below average students achieve slightly better marks, but had less of an impact on the grades for particularly low and high achieving students. Cette étude avait pour but de fournir une meilleure compréhension de la manière dont la capture de cours en vidéo est utilisée par les étudiants et comment son utilisation est reliée à la satisfaction des étudiants, à leur assiduité et à leurs résultats académiques. Grâce à un concept de méthodes mixtes comprenant des méthodes quantitatives et qualitatives pour recueillir les données, les instruments incluaient un questionnaire à remplir par les étudiants, des entrevues et des groupes de discussion, des statistiques sur l’emploi de la capture de cours, ainsi que des notes. Les résultats ont indiqué que 63 % des étudiants étaient satisfaits de la capture de cours et 75 % d’entre eux ont pensé que cette méthode avait favorisé leur apprentissage. Les étudiants ont principalement utilisé les enregistrements pour apprendre ce qu’ils avaient manqué quand ils étaient en classe (79 %) ou parce qu’ils étaient absents (72 %). Soixante-dix pour cent des étudiants ont indiqué qu’ils avaient regardé au moins 50 % des enregistrements (27 % avaient regardé toutes les vidéos) et seulement 8 % des étudiants n’avaient regardé aucune vidéo. La satisfaction des étudiants a eu une incidence positive importante en fonction du nombre de vidéos visionnées. En ce qui concerne l’assiduité, les données auto-déclarées par les étudiants ont indiqué que 61 % des étudiants n’avaient pas manqué de classes (aucune absence ou seulement une absence). Les étudiants qui avaient le plus grand nombre d’absences avaient regardé les vidéos plus fréquemment. En ce qui concerne les résultats des étudiants, 68 % d’entre eux ont indiqué que la capture de cours les avait aidés à obtenir de meilleures notes. Les étudiants qui avaient obtenu les notes de cours les plus basses avaient regardé les vidéos plus fréquemment que ceux qui avaient obtenu des notes plus élevées. Les instructeurs ont déclaré que la capture de cours en vidéo avait surtout aidé les étudiants de niveau moyen ou inférieur à la moyenne à obtenir des notes un peu meilleures, mais qu’elle avait eu un effet moins important sur les notes des étudiants très peu performants ou sur celles des étudiants très performants.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.391
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal for the Scholarship of Teaching and LearningSame topicInnovations in Educational MethodsFrench-language works237,207