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Record W2059563989 · doi:10.1300/j025v23n01_03

Learning Objects, Type II Applications, and Embedded Pedagogical Models

2006· article· en· W2059563989 on OpenAlex
George Gadanidis, Karen Schindler

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

VenueComputers in the Schools · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsLearning objectContext (archaeology)Computer scienceObject (grammar)Educational technologyFocus (optics)Type (biology)Mathematics educationExperiential learningArtificial intelligenceHuman–computer interactionPsychology

Abstract

fetched live from OpenAlex

Abstract In this paper we consider the extent to which learning objects that focus on higher level thinking might be seen as Type II applications, as defined by Maddux, Johnson, and Willis (2001). We conclude that learning objects are at best hybrid applications, with some Type I and some Type II characteristics. We also consider whether the educational effect of a learning object is attributable to the technology or to the pedagogical model embedded in the learning object. We discuss this question in the context of a study of middle-school students' mathematical thinking while performing some investigation tasks using learning objects and some without. We suggest that a learning object may be seen as an instance of the pedagogical model embedded in its design, and that different instances, even ones where technology is not used, are educationally equivalent. That is, it is the pedagogical model and not the technology that provides the predominant educational effect.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.069
GPT teacher head0.374
Teacher spread0.304 · 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