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Current Trends in Smart Classrooms and Sustainable Internet of Things

2024· book-chapter· en· W4402774265 on OpenAlexaff
Eriona Çela, Mathias Fonkam, Philip Eappen, Narasimha Rao Vajjhala

Bibliographic record

VenueAdvances in business information systems and analytics book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsCape Breton University
Fundersnot available
KeywordsInternet of ThingsInternet privacyComputer sciencePsychology

Abstract

fetched live from OpenAlex

This chapter explores the evolving landscape of smart classrooms, focusing on the integration of sustainable internet of things (IoT) technologies. This chapter systematically reviews current trends, highlighting how IoT innovations are reshaping educational environments to enhance learning experiences and improve resource efficiency. The chapter examines the impact of smart technologies on classroom design, teaching methodologies, and sustainability practices, offering insights into the benefits and challenges of adopting IoT in education. This chapter underscores the potential of sustainable IoT solutions to create more adaptive, efficient, and environmentally conscious educational spaces, setting the stage for the future of education.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0280.009

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 head0.012
GPT teacher head0.291
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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