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Record W3037656622 · doi:10.18844/cerj.v10i2.4732

Analyzing the use of mathematics apps in elementary school classrooms

2020· article· en· W3037656622 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

VenueContemporary Educational Researches Journal · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMathematics educationComputer sciencePsychologyMathematics

Abstract

fetched live from OpenAlex

Limited research has been conducted on the use of mathematics apps in elementary school environments. The purpose of this study was to examine student (n=127) and teacher (n=6) attitudes toward the use of constructive-based, mathematics apps in grades 4 to 6 and to explore what factors influence learning performance. Students rated the design and engagement value of mathematics apps high, and the learning value moderately high. Teachers were neutral about app design but rated the engagement and learning value high. Student learning performance increased significantly after using mathematics apps for remembering, understanding, applications and analysis-based tasks. Student gender, ability, attitudes, and age had no significant impact on student learning performance. On the other hand, teacher gender and strategies had a significant impact on student learning performance. Students scored 13% higher with female teachers, 24% higher when students used apps in pairs, and 21% lower with a teacher-led strategy. Keywords: mobile apps, mathematics, elementary school, attitudes, learning performance

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.199
GPT teacher head0.362
Teacher spread0.163 · 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