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Record W1561466974

Analysing the Effectiveness of Learning Objects for Secondary School Science Classrooms

2009· article· en· W1561466974 on OpenAlexaff
Robin Kay, Liesel Knaack

Bibliographic record

VenueJournal of educational multimedia and hypermedia · 2009
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMathematics educationClass (philosophy)Science learningQuality (philosophy)PsychologyScience classTeaching methodValue (mathematics)PedagogyScience educationComputer scienceArtificial intelligencePhysics
DOInot available

Abstract

fetched live from OpenAlex

The current study offers a comprehensive, systematic analy sis of learning objects used in secondary school science class rooms. Five reliable and valid measures were used to exam ine the effectiveness of learning objects for 503 students and 15 teachers in 27 science classrooms. The results suggest that teachers typically spend 1-2 hours finding learning objects and preparing lessons that often focus on the review of pre vious material. both teachers and students are positive about the learning benefits, quality, and engagement value of learn ing objects, although teachers are more positive than students. student performance increased significantly, almost 40%, when learning objects were used in conjunction with a vari ety of teaching strategies including brief introductions, letting students work on their own, and providing guiding handouts. it is reasonable to conclude that science-based learning ob jects are effective teaching tools in the secondary school en vironment.

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)0.005
metaresearch head score (Gemma)0.029
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.010
GPT teacher head0.292
Teacher spread0.282 · 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

Citations23
Published2009
Admission routes1
Has abstractyes

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