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Record W2057223161 · doi:10.2190/m770-j104-v701-8n45

A Systematic Evaluation of Learning Objects for Secondary School Students

2007· article· en· W2057223161 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 Technology Systems · 2007
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsQuality (philosophy)Learning objectMathematics educationObject (grammar)PsychologyMetric (unit)Theme (computing)Empirical researchComputer sciencePedagogyArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

Empirical research evaluating the effectiveness of learning objects is noticeably absent. No formal research has been done on the use of learning objects in secondary schools. The purpose of this study was to evaluate the use of learning objects by high school students. The evaluation metric used to assess benefits and quality of learning objects was theoretically sound, reliable, and partially validated. Overall, two-thirds of the students stated they benefitted from using learning objects. Students benefitted more if they were comfortable with computers, the learning object had a well organized layout, the instructions were clear, and the theme was fun or motivating. Students appreciated the motivational, interactive, visual qualities of the learning objects most. Computer comfort was significantly correlated with learning object quality and benefit. Younger students appeared to have less positive experiences than their older counterparts. There were no gender differences in perceived benefit or quality of learning objects, with one exception. Females emphasized the quality of help features significantly more than males.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
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 science0.0010.000
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 head0.024
GPT teacher head0.359
Teacher spread0.335 · 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