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Record W2887360732 · doi:10.20961/ijpte.v2i1.19568

An Intervention-Based Active-Learning Strategy To Enhance Student Performance in Mathematics

2018· article· en· W2887360732 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

VenueIJPTE International Journal of Pedagogy and Teacher Education · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMathematics educationActive learning (machine learning)Intervention (counseling)Section (typography)Course (navigation)Computer sciencePsychologyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Experiments were performed to study the effect of integrating an intervention strategy on student learning in an active learning environment in three different undergraduate mathematics courses. In these pedagogical experiments, the learning was measured via several subjective tests and the overall final grade for each course. For each course the comparison was made between two sections, one receiving the material via traditional instruction (control section) and the second receiving the material via instruction based on the active learning strategy (experimental section). It was found that students taught using the latter approach performed significantly better in the tests and exams, reflecting a good understanding of the material.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.512
Teacher spread0.473 · 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