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Record W3049715890 · doi:10.1080/07380569.2020.1793050

The Mediating Effect of Study Approaches between Perceptions of Mathematics and Experiences Using Digital Technologies

2020· article· en· W3049715890 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

VenueComputers in the Schools · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMathematics educationKey (lock)Structural equation modelingPerceptionProcess (computing)Computer scienceMathematicsPsychology

Abstract

fetched live from OpenAlex

The learning process of constructing concepts in mathematics can be supported by visualization in graphical, numerical, and symbolic representations. However, previous research suggests that students have not fully taken advantage of the opportunities provided by these technologies. Two key factors, including mathematics conception and mathematics study approaches differentially used such technologies and consequently promote the quality of mathematics education. We hypothesized that these key factors play a key role in this shortcoming. A survey of 442 high school students from Iran was conducted to examine the hypotheses through structural equation modeling. Results confirmed that mathematics conception and mathematics study approaches were associated with students’ experiences and evaluations of using supportive digital technologies in mathematics learning. Moreover, mathematics study approaches mediated the relationship between mathematics conception and students’ experiences and evaluation of using these digital technologies. These findings explain how mathematics conception contributes to learning experiences when students use such digital technologies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.174

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 communication0.0000.000
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 head0.078
GPT teacher head0.345
Teacher spread0.268 · 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