MétaCan
Menu
Back to cohort
Record W4392721146 · doi:10.22318/icls2023.844281

Identifying Pre-Service Teachers’ Cognitive Engagement Patterns Using Time Series Clustering in a Programming Activity

2023· article· en· W4392721146 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsStudent engagementCluster analysisCognitionKey (lock)PsychologyComputer scienceLearning analyticsMathematics educationData scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Learners' cognitive engagement has been identified as a key factor affecting learning outcomes.However, limited research has focused on how dynamic cognitive engagement patterns influence learning outcomes.In this study, ninety-three pre-service teachers participated in an online computational thinking (CT) activity.Four patterns were identified using time series clustering: (1) low-engagement; (2) high-engagement; (3) early-engagement; and (4) late-engagement.The findings revealed that high-and late-engagement learners displayed significantly higher CT skills than low-engagement learners.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.056
GPT teacher head0.329
Teacher spread0.273 · 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