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Record W6930087278 · doi:10.5061/dryad.g81h3

Data from: Memory for lectures: how lecture format impacts the learning experience

2015· dataset· en· W6930087278 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2015
Typedataset
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPresentation (obstetrics)Key (lock)Note-takingStyle (visual arts)Human memoryExperiential learning

Abstract

fetched live from OpenAlex

The present study investigated what impact the presentation style of a classroom lecture has on memory, mind wandering, and the subjective factors of interest and motivation. We examined if having a professor lecturing live versus on video alters the learning experience of the students in the classroom. During the lectures, students were asked to report mind wandering and later complete a memory test. The lecture format was manipulated such that all the students received two lectures, one live and one a pre-recorded video. Results indicate that lecture format affected memory performance but not mind wandering, with enhanced memory in the live lectures. Additionally, students reported greater interest and motivation in the live lectures. Given that a single change to the classroom environment, professor presence, impacted memory performance, as well as motivation and interest, the present results have several key implications for technology-based integrations into higher education classrooms.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.010

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 head0.103
GPT teacher head0.330
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2015
Admission routes1
Has abstractyes

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