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Enregistrement W2619369406

Proceedings of the 1st International Conference on Learning Analytics and Knowledge

2011· article· en· W2619369406 sur OpenAlexaffabout
Shane Dawson, Caroline Haythornthwaite, Simon Buckingham Shum, Dragan Gašević, Rebecca Ferguson

Notice bibliographique

RevueLearning Analytics and Knowledge · 2011
Typearticle
Langueen
DomaineComputer Science
ThématiqueOnline Learning and Analytics
Établissements canadiensAthabasca UniversityUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésDowntownAnalyticsHospitalityGeographyMedia studiesLibrary scienceHistoryArchaeologySociologyTourismComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

We welcome you to the 2012 Learning Analytics and Knowledge conference, being held in the beautiful city of Vancouver, Canada. Before you explore the city and the University of British Columbia, please join us in acknowledging that Vancouver and UBC are located on the traditional, ancestral, and unceded territory of the Canadian First Nations Musqueam people, and to thank the Musqueam people for their hospitality. Vancouver is a city rich in cultures, including people of First Nations, Asian, British, and many other origins and areas from the original Gastown to Granville Island, and from the high rise towers of downtown to the beaches of Kitsilano. Residents are an active group, with trails facilitating bicycling in the city, mountains close at hand for skiing and hiking, and the sea for boating of all kinds. At very close hand to the conference is the famed Stanley Park with its 22 kilometer (13.7 miles) seawall for walking, jogging or bicycling. For the more hardy, at little further off is Grouse Grind where you can test yourself against the mountains with 2,830 steps, and an 853 meter (2,800 feet) vertical ascent to a magnificent view of the Vancouver area. Or, stroll the streets of Vancouver, visit the UBC campus and perhaps you'll see some familiar spaces and places from the many films made here We hope you'll be able to make the most of your visit to Vancouver and of your time at the LAK12 conference. At time of writing, a number of weeks before the conference, we are sold out! This signals to us the importance of this emerging area of learning analytics and of the conference. We are pleased to be involved and helping to promote this new and exciting area of research and practice. We also want to thank those involved in helping make the conference such a success. Establishing a new research conference with proceedings published in the ACM Digital Library demands an extremely competent Program Committee, and we are indebted to our colleagues for their commitment to LAK12. A rigorous review process ensured that each paper was evaluated by at least three program committee members, and in many cases by four. Each paper was discussed in the online forum, with the authors having the option to reply to comments as distilled by the Program Chairs before a final decision was reached. Over our three days, we'll hear from three keynote speakers and an international set of authors in the field of learning analytics. The adjudicated papers include 14 Full Papers accepted from 36 submissions (39%). Of these a further six were accepted in briefer form as Short Papers and two as Design Briefings. We also invited authors to submit Short Papers that share preliminary conceptual, technical and empirical contributions: of the 26 submissions, 15 were accepted (58%). The program also includes three panels aimed to provide more discursive forums. As well as papers, the program includes two full-day and two half-day workshops taking place on April 29th on the UBC campus. In one year we have doubled the conference size. At LAK11, held in Banff in 2011, there were 17 adjudicated papers in a single track. LAK12 more than doubles in size to 40 in two parallel tracks, plus the pre-conference workshops. We have every confidence that this year's LAK will be a great success and will grow in size and reputation as at the end of LAK12 we will pass the torch on to the LAK13 organizers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,137
Score d'incertitude au seuil0,460

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,011
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,002
Communication savante0,0130,011
Science ouverte0,0030,006
Intégrité de la recherche0,0020,005
Charge utile insuffisante (le modèle a refusé de juger)0,1370,070

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,278
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations63
Publié2011
Routes d'admission2
Résumé présentoui

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