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Record W3015401774 · doi:10.19173/irrodl.v21i2.4607

Sprinting to the Finish Line: The Benefits and Challenges of Book Sprints in OER Faculty-Graduate Student Collaborations

2020· article· en· W3015401774 on OpenAlexvenueno aff
Gabriela C. Zapata

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSprintLiteracyPsychologyFinish lineMedical educationMathematics educationPedagogyComputer scienceSociology

Abstract

fetched live from OpenAlex

This article investigates the results of a book sprint experience whose main objective was the development of instructional modules for an open textbook for the teaching of Spanish as a second language. Six graduate students at a public American university participated in the project for a week, working in pairs in the creation of activities that required the incorporation of the tenets of the dual pedagogical frameworks of performance- and literacy-based instruction (as realized through learning by design). Data were collected through both an opinion survey and the assessment of samples of the participants’ products. The results of the survey showed that graduate students felt that being part of the book sprint had been beneficial both at the professional and personal levels, but they had also experienced difficulties similar to those reported in previous studies. The products analyzed pointed to a lack of connection between the required pedagogical tenets and the materials developed, which has also been reported in existing works on pre- and in-service teachers as materials developers. The article discusses how these results could have been a consequence of the structure of the book sprint, and it offers recommendations for future activities of this kind.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.780
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.275
GPT teacher head0.462
Teacher spread0.187 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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