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IoTalho: IoT Advancing Learning from High-tech Objects

2020· article· en· W3045708608 on OpenAlexaff
Péricles de Lima Sobreira, Jauberth Weyll Abijaude, Hellan Dellamycow Gomes Viana, Levy Marlon Souza Santiago, Karim El Guemhioui, Omar Abdul Wahab, Fabíola Greve

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsComputer scienceInterfacingMiddleware (distributed applications)MechatronicsArchitectureMultimediaThe InternetResource (disambiguation)World Wide WebHuman–computer interactionSoftware engineeringOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

This paper aims to present a platform to support the learning of high school students (without previous programming knowledge) for the development of technological solutions supported by the Internet of Things concept. This platform is designed from the integration of the following modules: A user-friendly visual programming environment for a specific microcontroller architecture; Web resource sharing tools assisting students in the tasks of communication and socialization of their projects (e.g., Wikis, Forums, Social Networks); A middleware, responsible for the communication between users (interfacing remotely with a teacher from a Web page) and sensors/actuators, present in an architecture remotely monitored by a teacher. With this proposal, it is expected that learners involved can be motivated and attracted by subjects related to the fields of Computer Science and (Electrical, Electronics, Mechatronics) Engineering. A scenario-based evaluation was performed to validate this platform.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations4
Published2020
Admission routes1
Has abstractyes

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