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Record W4406810123 · doi:10.47756/aihc.y9i1.143

Technology Acceptance of Tinkercad for 3D Object Design Using Block-based Programming

2024· article· en· W4406810123 on OpenAlexaff
Felix R. Garnica-Arciga, Brenda Cerrato-Abdala, Laura S. Gaytán‐Lugo, Pedro C. Santana‐Mancilla, Miguel Á. García-Ruiz

Bibliographic record

VenueAvances en Interacción Humano-Computadora · 2024
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsAlgoma University
Fundersnot available
KeywordsBlock (permutation group theory)Computer scienceObject (grammar)Artificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Block-based programming, with its logical and structured sequence of instructions through graphical connections, is a powerful tool for developing programming skills. Tinkercad, a platform known for its versatility, uses block programming for a variety of tasks, including 3D modeling. This paper presents the results of a study that evaluated the acceptance of Tinkercad in educational activities focused on designing 3D models for practicing block coding among Mechatronics Engineering students. To achieve this, a three-day online workshop was conducted with 20 participants. Upon completion of the workshop, an adaptation of the Technology Acceptance Model instrument was applied to evaluate the acceptance of Tinkercad as a tool to facilitate block coding topics. The results were positive, demonstrating the ease of use and usefulness of Tinkercad for these students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0020.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.334
Teacher spread0.298 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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