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Record W4391775825 · doi:10.17239/jowr-2024.15.03.04

Which modality results in superior recall for students: Handwriting, typing, or drawing?

2023· article· en· W4391775825 on OpenAlex
Lindsay Richardson, Guy Lacroix

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

VenueJournal of Writing Research · 2023
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsCarleton University
Fundersnot available
KeywordsHandwritingModality (human–computer interaction)TypingRecallComputer sciencePsychologyNatural language processingArtificial intelligenceSpeech recognitionCognitive psychology

Abstract

fetched live from OpenAlex

One of the most common interests among cognitive psychologists is establishing ways to enhance human learning. An additional layer of complexity has been brought on by the rapid evolution of technology. Specifically, examining if the mechanisms involved in typing differ from those involved in handwriting. The literature concerning the implications of encoding modality on memory have been inconclusive. This present research examined whether encoding modality resulted in performance differences for word recall. Wammes et al.’s (2016) drawing versus handwriting methodology was utilized with the addition of a typing condition. The results replicated the drawing effect, whereby drawn words were better recalled than handwritten ones. Overall, the evidence did not suggest that the mechanisms involved in handwriting led to better free recall than those involved in typing. However, if the pen is indeed mightier than the keyboard (Mueller & Oppenheimer, 2014), then the effect is not explained by visual attention or sensorimotor action differences between modalities. Implications for education are discussed.

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)0.020
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.150
GPT teacher head0.522
Teacher spread0.373 · 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