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Students' Uses of a Private Margin on Public Online Discussions

2018· book-chapter· en· W2903975490 on OpenAlexaff
Carl James Forde, Kevin O’Neill

Bibliographic record

VenueAdvances in library and information science (ALIS) book series · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRealmMargin (machine learning)Space (punctuation)Private spaceReading (process)Mathematics educationPublic relationsPsychologyMedical educationPedagogyPolitical scienceComputer scienceMedicineLaw

Abstract

fetched live from OpenAlex

For centuries, marginal notes have been integral to the acts of reading and studying. In the print realm, margins provide a private space where readers can record their initial reactions to text. Today many postsecondary students use online discussion forums as a prescribed part of course activities; yet these forums typically provide no private space for students to record their initial reactions to one another's posts. The authors added a private margin to the online discussion environment used in two graduate courses and examined students' uses of it. Without any specific instruction or encouragement, students used this margin as an integral part of how they participated in the discussion forum over the entire semester. The most common uses of the Virtual Margin were to privately record opinions on other students' posts, to create summaries of others' posts for personal study, and to create private drafts of notes to post publicly later. Overall, the results suggest that a private margin has potential to assist students in their learning and in developing public forum contributions.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.277
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.357
Teacher spread0.329 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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