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Record W2112846495 · doi:10.3138/jvme.30.4.351

Developing Interactive Course Web Sites for Distance Education and Characteristics of Students Enrolled in Distance Learning Courses

2003· article· en· W2112846495 on OpenAlexvenueno aff
Vikas Diwakar, Peggy A. Ertmer, A. Y. M. Nour

Bibliographic record

VenueJournal of Veterinary Medical Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationThe InternetCourse (navigation)MultimediaComputer scienceTask (project management)Subject matterProcess (computing)Synchronous learningSubject (documents)World Wide WebTeaching methodMathematics educationPsychologyCooperative learningCurriculumPedagogyEngineering

Abstract

fetched live from OpenAlex

The ubiquity of the Internet has made disseminating information across geographical boundaries a relatively easy task. Apart from text-based materials, the Internet provides an easy means to transmit images, sound, video, and other multimedia content to a global audience, making it an ideal medium for establishing distance learning programs. Two Internet-based distance learning courses were developed to teach animal physiology to veterinary technicians in the School of Veterinary Medicine at Purdue University. These distance learning course sites are designed to take advantage of multimedia technology to enhance students' learning experiences. Multimedia has been used in education to make the learning process more engaging and interactive. The two course sites have a number of multimedia features that complement the textual subject matter. This article describes the features of the course Web sites and summarizes our experiences in designing and conducting Web-based physiology courses to distance learners. In addition, we describe the characteristics of our distance learning students.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.039
GPT teacher head0.443
Teacher spread0.404 · 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 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

Citations15
Published2003
Admission routes1
Has abstractyes

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