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Record W2078634927 · doi:10.1021/ed300497g

Recording Tutorials To Increase Student Use and Incorporating Demonstrations To Engage Live Participants

2013· article· en· W2078634927 on OpenAlexaffabout
Reuben Hudson, Kylie L. Luska

Bibliographic record

VenueJournal of Chemical Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsUploadComputer scienceMultimediaEvent (particle physics)Student engagementOnline learningMathematics educationWorld Wide WebPsychology

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Over the course of three semesters, the tutorials for introductory organic chemistry at McGill University evolved significantly with the input from student surveys. The tutorials changed from “chalk talks” in the first semester to a lecture capture format in the second in which PowerPoint slides, ink annotations, and associated audio were recorded, and uploaded online to be viewable by any student at any time. As expected, the later format reached more students, though fewer came in person to the live tutorial. In an effort to continue to reach as many students as possible, while at the same time providing a more engaging environment for students at the live event, the format changed once more. Demonstrations, discussions, and other personalized interactions not accessible online were incorporated in the third semester to provide a more meaningful experience for students physically present, without compromising the online content. This third tutorial format in the final semester did indeed encourage more students to come in person. Herein, we follow the evolution of these tutorials, discuss the impetus for changing formats, document student use (both online and in person) and conclude that lecture capture technology is an effective means of delivering optional course content and it can be effectively supplemented by demonstrations and other personalized interactions to reach students with different learning styles.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.005

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 head0.092
GPT teacher head0.452
Teacher spread0.360 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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