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Record W2124501599 · doi:10.19173/irrodl.v13i1.1141

An open education resource supports a diversity of inquiry-based learning

2012· article· en· W2124501599 on OpenAlexvenueno aff
Catherine Schmidt-Jones

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2012
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCasualOpen educationDiversity (politics)Resource (disambiguation)Mathematics educationThe InternetSet (abstract data type)Function (biology)Open educational resourcesComputer sciencePsychologyPedagogyWorld Wide WebSociologyPolitical science

Abstract

fetched live from OpenAlex

There have been numerous calls for research that demonstrates how open education resources (OERs) are actually being used. This case study sought to shed light on the users of a well-visited set of modular music-education materials published at Connexions. Respondents to a voluntary survey included teachers, students, self-directed learners, music ensemble participants, and casual learners. Most reported accessing individual modules on their own initiative, as part of a specific, immediate inquiry, rather than responding to institutional directives or following entire online courses. This was supported by computer-log records, which showed that most visitors to a module arrived from an Internet search for terms specific to that module. The study suggests that, for teachers and students as well as self-directed learners, one function of OERs is as a resource for just-in-time, inquiry-based 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0070.011
Open science0.0020.012
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.118
GPT teacher head0.460
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations32
Published2012
Admission routes1
Has abstractyes

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