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Record W4321790527 · doi:10.5430/jct.v12n1p283

Embracing Digital Technologies into Mathematics Education

2023· article· en· W4321790527 on OpenAlexvenueno aff
Analyn M. Gamit

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGRASPMathematics educationArtifact (error)Plan (archaeology)Computer scienceEducational technologyValue (mathematics)PedagogyPsychology

Abstract

fetched live from OpenAlex

It might be challenging to find a way to incorporate digital technology into the classroom successfully. The purpose of this research was to document the implementation of a digital tool into three high school mathematics classrooms to enhance teacher and student learning. The researcher used the Learning Management System (LMS) as an educational online integrated software to looked at educators’ perspectives on its value and how they are putting their newfound knowledge to use in the classroom, as well as their reasons for using LMS resources and determine how teachers plan to use new technological tools in their classrooms. Using surveys and classroom observations, the researcher found that a poorly established social artifact was the most significant barrier to students' education. When teachers do not try to develop standard procedures for utilizing technology, students often struggle to use the instrument well. Teachers can only possibly assist their pupils in integrating teacher and agent instructions when they grasp how the tool works for themselves. Instead of having one cohesive learning experience, students are kept from their teachers and devices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.327
Teacher spread0.316 · 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 designOther design
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

Citations7
Published2023
Admission routes1
Has abstractyes

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