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Record W4294100247 · doi:10.31124/advance.20407233.v1

Asynchronous Google Classroom Mathematics and Synchronous L2 Oral Performance

2022· preprint· en· W4294100247 on OpenAlexaff
Stephanie Sadownik

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceReading (process)Reading comprehensionComprehensionAsynchronous communicationCognitionMathematics educationMultimediaPsychologyLinguisticsProgramming language

Abstract

fetched live from OpenAlex

In this meta-analysis, reading comprehension and mathematics are considered for elementary students between grade three and five, the age group where most reading comprehension strategies are introduced and practiced (Ness, 2011). Results indicate that (1) reading comprehension is linked to cognitive processing ability, the more students’ struggle with comprehending text, the less cognitive processing they have available to solve the mathematical problem; (2) mathematical language is precise and often above the grade level of the student who is solving the word problem. Asynchronous learning management systems or other online platform, such as Google Classroom, may help educators to meet federal obligations for supporting English Language Learners conceptual development in mathematics, developing language proficiency and providing equitable access for content area learning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.331
Teacher spread0.299 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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