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Record W2131277788

Media Player Tool Use, Satisfaction with Online Lectures and Examination Performance

2008· article· en· W2131277788 on OpenAlexaff
John N. Bassili, Steve Joordens

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPsychologyArt
DOInot available

Abstract

fetched live from OpenAlex

Media players allow students to pause lectures and to replay portions at will, two capabilities with potentially important pedagogical value that are not available in face-to-face lectures. The first study showed that many students use and value these media player features when watching online introductory psychology lectures. The second study showed that use of the features was correlated with superior exam performance, a learning outcome that was at least partially mediated by increased satisfaction with the learning approach. Together, the findings demonstrate that students who used the features made available by the media-players were more satisfied with the course, and performed better in it. Resume Les lecteurs multimedias permettent aux etudiants d’interrompre les enregistrements de cours en salle et d’en rejouer des parties a volonte, deux fonctions ayant une valeur pedagogique potentiellement importante qui ne sont pas disponibles dans les cours en salle. La premiere etude montre que plusieurs etudiants d’un cours d’introduction a la psychologie utilisent et apprecient ces fonctions des lecteurs multimedias. La deuxieme etude montre qu’il existe une correlation entre l’utilisation de ces fonctions et une meilleure performance aux examens, un resultat de l’apprentissage qui serait partiellement explique par l’augmentation de la satisfaction par rapport a l’approche d’apprentissage. Ensemble, les resultats indiquent que les etudiants qui utilisent ces possibilites inherentes aux lecteurs multimedias sont plus satisfaits de leur cours et obtiennent de meilleures notes.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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 teacher head, 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

Citations43
Published2008
Admission routes1
Has abstractyes

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