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"VIRTUALLY" A MAKER: MAKING IN AN ONLINE GRADUATE COURSE

2020· article· en· W3118166030 on OpenAlexaff
Janette Hughes, Laura Morrison, Jennifer Robb

Bibliographic record

VenueInternational journal on innovations in online education · 2020
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAffordanceAsynchronous communicationContext (archaeology)Collaborative learningComputer scienceSynchronous learningIntersection (aeronautics)Meaning (existential)Learning communityCourse (navigation)Online learningMathematics educationCooperative learningTeaching methodMultimediaPsychologyKnowledge managementEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Makerspaces have become established as communal and collaborative environments for making meaning, solving problems, and developing essential global skills and competencies. However, these spaces are commonly utilized by makers who are geographically co-located. In this qualitative study, we explored the intersection of a maker approach to learning and the affordances of online education—specifically, learning in one's own community by accessing the online course remotely as opposed to traveling to a physical classroom, and connecting the learning/making activities directly to one's home context—in a graduate-level course focused on critical making. We also discuss the theory and methods that informed the development and implementation of this course, given a recent shift from the use of traditional teacher-centered pedagogies in online learning. Our results suggest that the online course acted as a supportive community of inquiry that both scaffolded students' independent exploration of making in their local communities and facilitated continuous, collaborative learning through an effective blend of synchronous and asynchronous technologies. The need for a platform that enabled ongoing, organic sharing and connection between students was also highlighted.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.121
GPT teacher head0.416
Teacher spread0.295 · 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 designOther design
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

Citations9
Published2020
Admission routes1
Has abstractyes

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