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Record W1644681929 · doi:10.19173/irrodl.v3i2.105

Gagne's and Laurillard's Models of Instruction Applied to Distance Education: A theoretically driven evaluation of an online curriculum in public health

2002· article· en· W1644681929 on OpenAlexvenueno aff
Peggy A. Hannon, Karl Umble, Lorraine K. Alexander, Don Francisco, Allan Steckler, Gail Tudor, Vaugn Upshaw

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumDistance educationClass (philosophy)Mathematics educationPsychologyInstructional designPedagogyCourse evaluationTeaching methodPublic healthMedical educationHigher educationComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

<P class=abstract>This article presents an overview of the instructional models of Gagne, Briggs, and Wager (1992) and Laurillard (1993, 2002), followed by student evaluations from the first year of an online public health core curriculum. Both online courses and their evaluations were developed in accordance with the two models of instruction. The evaluations by students indicated that they perceived they had achieved the course objectives and were generally satisfied with the experience of taking the courses online. However, some students were dissatisfied with the feedback and learning guidance they received; these students’ comments supported Laurillard’s model of instruction. Discussion captured in this paper focuses on successes of the first year of the online curriculum, suggestions for solving problem areas, and the importance of the perceived relationship between teacher and student in the distance education environment.</P>

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 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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.170
GPT teacher head0.482
Teacher spread0.312 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2002
Admission routes1
Has abstractyes

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