When words matter: evaluating the quality of open educational resources through lexicon.
Notice bibliographique
Résumé
There is a general agreement on the advantages of open education in e-learning in terms of inclusiveness and equity (Wiley, 2010). However, there is a relatively less developed debate about the quality evaluation of open educational resources (OERs), accessible for free and without spatial or time limits. Usually the OERs’ quality is assessed on structural features, i.e. accessibility, usability, learning goals, possibility for the learner to assess his/her progress during learning (McGill et al. 2013; Ehlers & Joosten, 2009). When it comes to the content of OERs, this is considered mainly at the macro-level (i.e. text cohesion and coherence, cultural differences among potential users, gender differences), and not at the micro-level of its lexical components. Conversely, one of the most debated issues within the quality of education, both traditional face-to-face education and distance technologyenhanced education, is on how the educational mediation takes place in order to develop transversal competences and basic skills linked to the use of language (Marconi, 1997). W. Nagy, expert in vocabulary development, asks, “Which words should a teacher teach?” (2011) and, more in general, then he considers how, irrespectively of the disciplinary contents, words that compose the educational \nmessage do have an intrinsic value and should descend from an intentional choice in the instructional design phase. \nMeasures related to text readability are generally based on the frequency of a word in a corpus of sufficiently wide dimensions. Texts with rare words are more difficult to understand than those that contain common words. However, the emphasis on the use of these tools to study the adequacy of textbooks and learning materials has been criticized (Davison - Green, 1988), as not always common words are easy to define (e.g. the article “the” it is very common but with difficult to define) whereas rare words in a written texts can be easier as, for instance, they are common in the spoken language (e.g. “t-shirt” or “fireman”). Building a structure of semantic relationships between words (similarities/oppositions, inclusion/exclusion, semantic fields) is instead one of the ways to help memorization, and it makes likely the passage from receptive to productive lexicon in learners. On these premises, it can be envisaged a set of criteria to evaluate the appropriateness of an OER text with respect to its learning goals. \nThe paper discusses the assumptions to evaluate the lexical aspects of OERs’ instructional messages, and presents the first results obtained from an exploratory study carried out on a set of OERs’ in Italian language on a variety of contents.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».