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AGILE TEACHING STRATEGY FOR ONLINE CLASSROOMS

2024· article· en· W4402744585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal on innovations in online education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Leadership and Innovation
Canadian institutionsDystonia Medical Research Foundation Canada
Fundersnot available
KeywordsAgile software developmentMathematics educationPedagogyPsychologyMedical educationComputer scienceMedicineSoftware engineering

Abstract

fetched live from OpenAlex

This paper is focused on student management constraints in the online classroom and proposes modifications to teaching strategies to address them. It explores the organizational aspects of the education process and how to prioritize learning while mitigating distractions inherent to digital device use, such as multitasking, tab switching, and smartphone doom surfing. The paper is based on 9 years of High School Big Data and AI Challenge Program carried out by STEM Fellowship. It seeks to contribute to the discourse around students who are used to any time/anywhere/any device access to information by proposing a transition from traditional classroom management replicated online to a novel teaching strategy, inspired by principles from Agile methodologies in various industries. Such a shift has the potential to advance educational practices, enabling educators to create learning environments that are better equipped to meet the diverse needs of Generation Z and Alpha students in online classes.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.095
GPT teacher head0.481
Teacher spread0.386 · 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