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Enregistrement W6989896346

CLIL Funerary Archaeology courses for first-cycle and second-cycle degree students

2014· article· en· W6989896346 sur OpenAlexaboutno aff

Notice bibliographique

RevueCINECA IRIS Institutial research information system (University of Pisa) · 2014
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueSecond Language Learning and Teaching
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSubject (documents)Reading (process)PotteryActive listeningForeign languageIndigenousClassical archaeologyEnglish language
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This paper reports on the differences between two specialized funerary archaeology courses conducted by a native language teacher from the Institute for Computational Linguistics of the National Research Council in Pisa and a subject specialist in paleopathology and funerary archaeology from the Division of Palaeopathology, Department of Translational Research on New Technologies in Medicine and Surgery of Pisa University. Lessons addressed to first cycle three-year Bachelor's degree undergraduates who were studying archaeology, art history, natural and environmental sciences took place in the second semester of the year 2012-2013. Classes in the same discipline and addressed to students from the same faculties had been held a year earlier for a second cycle twoyear Master's degree course. The classes were delivered in English using CLIL (exploitation of a
\nvehicular foreign language to teach a special subject) associated with blended learning methodology
\n(combination of face-to-face instructor-led training with web-based technology). Appropriate teaching materials selected by the two teachers covered a wide range of topics, from the study of death to ancient burials, rites, and dynamics of human settlements, as well as evidence of past human societies recovered by excavations. In particular, ancient Roman funerary customs (inhumation,
\ncremation) and Medieval mortuary practices and burials were studied, alongside artifacts such as weapons, jewellery, and pottery vessels recovered from archaeological sites both in Italy and in Britain. Collaboration between language teacher and subject specialist was crucial for the selection of the reading and listening materials, for the correction of the oral and written work assigned to the students, and for the intervention on the part of the subject teacher to clarify points that had been raised, to assist the students during the individual presentations, pairwork or group discussions, and to encourage their work. Two researchers collaborating with the subject specialist also contributed to the lessons by presenting studies they had performed in their area of expertise and by assisting the students during the discussions. These student-centred tasks were aimed at accomplishing important educational goals such as student motivation, improved cognitive and academic performance,
\nenhanced access to online learning resources, peer learning and collaboration. The 2012-2013 course
\nproved to be much more interactive and challenging than the previous one, owing to the major emphasis given to the more practical aspects, in preparation for the fieldwork in archaeology and bioarchaeology, which was carried out in the summer of 2013, working with their peers from Ohio
\nState University and other Universities in the USA, Canada and Australia. Particular attention was devoted to the language of funerary archaeology, and the trainees extracted definitions from the texts they were using to enrich an ongoing English-Italian glossary of funerary archaeology terms. The most important items and sentence structures of the English language were studied and revised, and an English grammar containing contextualized examples drawn from specialized works in that domain was enriched with new material. Student exchanges under different European and international
\nprogrammes have emphasized on the need for specialist knowledge in specific thematic areas, alongside an oral and written command of a foreign language.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,809
Score d'incertitude au seuil0,929

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,067
Tête enseignante GPT0,291
Écart entre enseignants0,223 · 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 tête enseignante, 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
GenreEmpirique

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

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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