MétaCan
Menu
Retour à la cohorte
Enregistrement W7034393292

Teaching, learning, and assessment activities used in additional language courses offered in blended contexts to promote the development of learners’ language skills in higher education

2022· other· en· W7034393292 sur OpenAlexaboutno aff

Notice bibliographique

RevueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2022
Typeother
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGene expression and cancer classification
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBlended learningHigher educationLanguage acquisitionLanguage assessmentLanguage educationEducational technologyComprehension approach
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This master’s dissertation explores the teaching, learning, and assessment activities that higher education instructors use in additional language courses in a blended format from an instructional design perspective. The types of activities that instructors use in order to develop learners’ language skills and the design of blended language courses have been under-researched in the literature, even though blended learning approaches in language learning have been used approximately since 2000 (Grgurović, 2017). Given the fast technology development, particularly web 2.0 technologies and other digital tools, and the possibility of learning an additional language, it becomes highly important to understand how additional language teaching and learning take place in these contexts. Moreover, the COVID¬ 19 pandemic has accelerated how technology is used by higher education institutions (Skulmowski & Rey, 2020). Therefore, in this study, the focus is on the language activities performed in the different types of blended courses, such as blended courses, blended online courses, and blended synchronous courses, as well as the mode in which they are performed: asynchronous, synchronous and face to face. The general objective of this study is to describe the additional language teaching, learning, and assessment activities used by instructors in blended courses to promote additional language skills development in higher education. To attain this objective, I formulated two specific objectives: 1. Describe instructors’ choices of teaching, learning, and assessment activities, and digital tools used in additional language blended courses. 2. Describe the complementarity between asynchronous, synchronous, and face to face activities’ modes. This study uses a qualitative methodology that is aligned with the objectives of the dissertation. The selected sample consists of three additional language instructors who taught English, French, and Spanish in a blended format at a university in Quebec province. The data collection methods include a semi-structured interview and the use of course documents. The data analysis methods consist of a descriptive thematic analysis and a documentary analysis. Moreover, the results and discussion are presented in the form of an article that has been submitted for publication to the Japan Association for Language Teaching Computer Assisted Language Learning Special Interest Group Journal (JALT CALL journal). It should be highlighted that this study uses secondary data from a larger study called “Élaboration et validation d'un modèle explicatif de la persévérance et de la performance dans les cours hybrides en enseignement supérieur” (Lakhal et al., 2019) which was subsidized by the Social Sciences and Humanities Research Council (SSHRC). The results of this study show that the activities performed in the blended courses for additional language learning are varied and are influenced by the affordances of technology and the blended courses’ modes. Generally, in these courses different skills and areas are developed: speaking, listening, reading, writing, subskills (i.e., grammar, vocabulary, and pronunciation), digital competency, metacognition, and content transfer and consolidation. The participants used the asynchronous mode for doing content transfer activities, developing learners’ digital competency, developing their comprehension skills, and preparing them for synchronous and/or face-to face modes. When it comes to synchronous and face to faced modes, the participants selected them to promote interaction and collaboration among learners, thus it involved the development of productive skills such as writing and speaking. Nevertheless, the four skills are spread in the different course modes, so they are not specific to only one mode. Concerning the digital tools used in the courses, they were diverse and mainly linked to the licenses available to the university. However, the study specifies a range of digital tools and the activities they support, some are specific to language teaching and learning. Finally, these results are relevant to the field in that they contribute to filling a gap in the scientific literature concerning the intersection between additional language teaching and learning and blended learning, they add knowledge to the discussion available in the scarce scientific literature about this intersection, and finally, they provide insights to language instructors in higher education teaching in blended formats about the different activities available to be taught in blended courses and reasons to implement them.

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)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,009
Tête enseignante GPT0,256
Écart entre enseignants0,247 · 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'étudeObservationnel
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
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke)Même sujetGene expression and cancer classificationTravaux en français237 207