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Record W4239355011 · doi:10.24124/2019/59065

Listening to learn: a quantitative study of listening comprehension in the elementary classroom

2019· dissertation· en· W4239355011 on OpenAlexaff
Christy McKenna

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsActive listeningClass (philosophy)Listening comprehensionMathematics educationPsychologyComprehensionReading (process)Test (biology)Statistical analysisTask (project management)Reading comprehensionIntervention (counseling)PedagogyComputer scienceEngineeringLinguisticsMathematicsCommunicationArtificial intelligence

Abstract

fetched live from OpenAlex

The goal of this research study was to investigate listening comprehension and demonstrate how a listening comprehension intervention could lead to improvement. This study intended to contribute to the knowledge base of research with elementary-aged students while providing educators with guidance in teaching listening. Two Grade 5 classes comprised of the participant groups; one class served as the intervention group, while the other class served as the control group. This study followed a quantitative research methodology using a quasi-experimental design that included pretesting and posttesting in listening and reading. Statistical analysis using t-tests compared the groups. The findings of this study did not achieve statistical significance but resulted in several educational significances. The implications of this study indicate that the task of designing an age-appropriate course and measuring improvement is challenging. Subsequent research in the area of listening comprehension course development and test development for this age group is recommended.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.378
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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