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Record W2039050488 · doi:10.2190/npgc-dwtm-admf-pac7

An Examination of High School Students' Understanding of Learning in a Computer Applications Class

2002· article· en· W2039050488 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

VenueJournal of Educational Computing Research · 2002
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsVariety (cybernetics)Class (philosophy)Mathematics educationMetacognitionPerceptionComputer scienceActive learning (machine learning)Experiential learningCooperative learningTeaching methodEducational technologyQualitative researchComputer-Assisted InstructionPsychologyCognitionPedagogySociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Students' voices have not been extensively heard in research in computer education. In this study, a secondary school teacher of computer applications (the first author) explores with her students their ideas and perceptions about learning in her class, asking them about how they learn best and what approaches and teaching strategies they find most conducive to learning. Analysis of qualitative data collected through use of questionnaires, interviews, and a focus group suggests that, given the opportunity, students can articulate a variety of ideas about their learning, engage in metacognitive activity, identify their strengths and difficulties in learning, and make suggestions about how to improve learning. Issues of cognitive load and the purposes and goals of instruction in computer applications are discussed. The findings inform not only the teaching and learning of computer applications courses, but teaching and learning more generally.

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)0.013
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.210
GPT teacher head0.521
Teacher spread0.311 · 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