MétaCan
Menu
Back to cohort
Record W1678709211

Design and Delivery of Tele-educational Courses

2007· article· en· W1678709211 on OpenAlexvenueno aff
Vincent Wade, Mark Riordan, Conor Power

Bibliographic record

VenueInternational journal of e-learning & distance education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDuration (music)Service (business)Economic shortageInformation and Communications TechnologyPublic relationsEngineering managementBusinessPolitical scienceKnowledge managementEngineeringComputer scienceMarketingWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

At a time when governments are eager to increase the numbers of well-qualified graduates, educational organizations are under increasing financial pressure and are experiencing a shortage of facilities to locate these increasing student numbers. Significant advancements in the capability and accessibility of computer technology have been heralded as a means of alleviating some of these pressures. Consequently, there is a movement to apply advanced information and communication technology innovatively to enhance the pedagogical aspects of teaching and relieve administration and management resources. One of the crucial determinants of whether this approach can deliver on the promises offered lies in the design of complex software systems in which many technical, cultural, pedagogical, and social factors have to be considered. This paper examines student and user requirements for the design of tele-educational course delivery and describes the experiences of applying appropriate multimedia information and groupware communication technologies to support educational services. The outcome of our initial studies based on the execution of two trials involving over 200 university students is presented, and the implications these have for the design of future systems is discussed. The paper also examines and presents trial experience on how educational services could be expanded beyond the university setting so that small independent educational companies could service niche markets for specialized short-duration tele-educational services using similar technologies. A un moment où les gouvernements sont soucieux d'augmenter le nombre de diplômés qualifiés, les organismes d'éducation subissent une pression financière accrue ainsi qu'une carence en locaux d'accueil pour le nombre croissant d'étudiants. Les progrès significatifs dans les capacités et l'accessibilité des technologies de l'informatique ont étant soulignés comme autant de moyens de se détacher de ces pressions. Il en résulte une tendance au développement et à l'application des techniques d'information et de communication pour améliorer les aspects pédagogiques de l'enseignement et soulager l'administration et la gestion des ressources. Une des conditions déterminantes dans le succès d'une telle approche dépend de la conception de logiciels complexes tenant compte de nombreux facteurs techniques, pédagogiques et sociaux. Ce document examine les besoins des utilisateurs et des étudiants dans le but de concevoir des cours à distance et décrit l'utilisation de supports multimédias appropriés et de technologies de communication pour des services d'éducation. Les résultats de notre étude basée sur deux périodes de test impliquant plus de 200 étudiants de premier cycle universitaire, sont présentés et les implications de ceux-ci sur le développement de systèmes à venir sont discutées. Cet article rend compte également des résultats obtenus par la mise à l'essai et examine la question de l'expansion « extra muros » de services éducatifs universitaires de sorte que de petites entreprises d'éducation indépendantes puissent offrir des services spécialisés de courte durée en télé-éducation à des marchés pointus en faisant usage de technologies similaires.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.353
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueInternational journal of e-learning & distance educationSame topicOnline and Blended LearningFrench-language works237,207