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Record W2255704289

Issues in Project-based Distance Learning in Computer Science

2007· article· en· W2255704289 on OpenAlexvenueno aff
Patricia Fung

Bibliographic record

VenueInternational journal of e-learning & distance education · 2007
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationContext (archaeology)HumanitiesLibrary scienceThe artsSociologyComputer sciencePedagogyArtVisual artsGeography
DOInot available

Abstract

fetched live from OpenAlex

In the years since the inception of the Open University, UK (OU, UK), project-based work has been established as a component of many courses in the faculties of Arts, Social Sciences, Science, and Technology. Results from an early study of the desirability and feasibility of using project-based learning (Macmillan, 1975) indicated that this teaching methodology has a useful role to play in distance learning. There has, however, been no comparable use of it within the OU, UK, Mathematics Faculty. In its discussion of the first evaluation of a newly introduced project-based computing course within the Mathematics Faculty this paper considers how valid this methodology has proved to be within the different context of computer science. This discussion concludes that for a number of reasons it has proved more difficult to implement project-based work in the domain of computer science. The paper ends by considering the likely advantages and disadvantages associated with introducing electronic tutoring and conferencing to deliver this distance course and overcome the difficulties discussed. Depuis la création de la Open University du Royaume-Uni, de nombreux cours offerts dans les facultés des arts, des sciences sociales, des sciences de même que de la technologie, comprennent des travaux axés autour d'un projet. Les résultats issus d'une étude antérieure portant sur le bien-fondé et la faisabilité de l'apprentissage par projet (Macmillan, 1975) indiquent que cette méthode d'enseignement peut jouer un rôle utile dans l'apprentissage à distance. Toutefois, il n'y a eu jusqu'ici aucune utilisation comparable de la sorte au sein de la Faculté des mathématiques de la Open University. En plus de présenter une première évaluation d'un nouveau cours d'informatique développé autour de la réalisation d'un projet au sein de la Faculté des mathématiques, cette étude examine dans quelle mesure cette méthodologie s'est révélée valable dans le contexte spécifique de l'informatique. On en conclut que, pour plusieurs raisons, il s'est avéré plus problématique d'implanter une approche de travail par projet dans le domaine de l'informatique. L'article termine en considérant les avantages et les désavantages les plus susceptibles d'être associés à l'introduction d'un tutorat et de conférences électroniques dans la livraison de ce cours à distance et dans l'aplanissement des difficultés énumérées.

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)0.151
metaresearch head score (Gemma)0.240
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1510.240
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0110.023
Scholarly communication0.0280.022
Open science0.0060.017
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0090.003

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 head0.006
GPT teacher head0.297
Teacher spread0.291 · 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 designQualitative
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

Citations4
Published2007
Admission routes1
Has abstractyes

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