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Record W2942817286 · doi:10.1145/3290607.3312878

NoteStruct

2019· article· en· W2942817286 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Toronto
FundersOffice of Naval ResearchNational Taiwan UniversityMinistry of Science and Technology, TaiwanIntel Corporation
KeywordsComputer scienceMultimediaInterpretation (philosophy)Online videoNote-takingOnline learningElaborationSample (material)Human–computer interactionMathematics educationWorld Wide WebPsychologyKnowledge management

Abstract

fetched live from OpenAlex

Note-taking activities in physical classrooms are ubiquitous and have been emerging in online learning. To investigate how to better support online learners to take notes while learning with videos, we compared free-form note-taking with a prototype system, NoteStruct, which prompts learners to perform a series of note-taking activities. NoteStruct enables learners to insert annotations on transcripts of video lectures and then engages learners in reinterpreting and synthesizing their notes after watching a video. In a study with a sample of Mechanical Turk workers (N=80), learners took longer and more extensive notes with NoteStruct, although using NoteStruct versus free-form note-taking did not impact short-term learning outcome. These longer notes were also less likely to include verbatim copied video transcripts, but more likely to include elaboration and interpretation. We demonstrate how NoteStruct influences note-taking during online video learning.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.379
Threshold uncertainty score0.961

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)0.0500.040

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 head0.016
GPT teacher head0.340
Teacher spread0.324 · 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

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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