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Record W4381850574 · doi:10.1299/jsmetsd.2022.123

Usage Pattern of On-demand Type Content Prepared in the Face-to-face Classes

2022· article· en· W4381850574 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Proceedings of the Tecnology and Society Conference · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFace-to-faceClass (philosophy)Face (sociological concept)Quarter (Canadian coin)Perspective (graphical)Computer scienceMultimediaPsychologyMathematics educationArtificial intelligenceLinguisticsHistory

Abstract

fetched live from OpenAlex

Usage pattern of on-demand type learning content as alternative materials to the face-to-face classes in engineering subjects for undergraduates was reported. The learning content was simply made by recording screen images of the video projector and the explanative voices of the teacher in the classroom during the lesson in video files. The recorded files were provided on the learning management system as the streaming content to the students who had been absent from the face-to-face class. Approximately a quarter of registered students took on-demand lessons once or twice throughout one semester. The most common reason for absence from the face-to-face class was bad health, whereas direct reasons due to COVID-19, such as side effects of the vaccine or being in close contact with an infectious disease patient, were small. These facts suggest that the necessity of providing on-demand type content should be considered from the perspective of supplementing the absence of face-to-face classes rather than correspondence to the COVID-19 situation.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.105
GPT teacher head0.350
Teacher spread0.245 · 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