Notice bibliographique
Résumé
Student mobility is a priority in the European Union since it not only allows academic interchange but also fosters the awareness of being a European citizen amongst students. The Bologna Process aimed at homogenizing the structure of the European Universities to facilitate the recognition of academic titles as foreseen by the Lisbon Recognition Convention and student mobility during their matriculation. Over one and a half million students have already benefited from mobility programs such as the Erasmus programme. Students that participate in a mobility program must consider a destination, a selection of courses to follow abroad and how their home institution will recognize their foreign credits. Selecting the most appropriate courses is not a simple task since a course title doesn't always reflect its content. As a result, manual inspection of syllabi is necessary. This makes the task time-consuming since it might require manual inspection and comparison of many syllabi from different institutions. It would be nice to be able to at least partially automate the process -- i.e. given a set of syllabi from two different universities, to be able to automatically find the best match among courses in the two institutions. We started experimenting with this possibility, and although we do not yet have final results we will present the main idea of our project. Our plan is to try to apply similarity matching algorithms to available documents. Similarity matching is often based on co-occurrence of common words. However, a naïve application of such an algorithm would probably end up generating spurious similarities from the co-occurrence of general terms like "hour, exercise, exam...". Using a stop-word strategy in which these words are catalogued and ignored might seem a viable solution, but generally does not significantly improve the results: words that may be considered irrelevant in one context might be important in a different context. The path we are following is to assume the existence of a reference ontology, where all terms have a description, and then try to identify the occurrence of the concepts existing in the ontology within the examined documents. In this way we will be able to state that "syllabus x deals with topic y". The matching between different syllabi would then be calculated by matching the topics that were associated with the syllabi. We decided to focus on the Computer Science domain since the domain has already been classified into areas, units and topics present in CC2001[1] and this ontology has already been mapped into XML structures[2]. We then used a similarity matching algorithm that uses Wikipedia as a reference corpus[3]. Although preliminary results are not yet fully satisfactory, we believe that this might result from working at the word level rather than at a concept level; "software engineering" is not just the co-occurrence of "software" and "engineering" but a more complex concept. We are therefore currently exploring the possibility of identifying multi-words as concepts (still by using Wikipedia as a reference to decide if this is the case or not). If our attempts are successful, the next step will be to (semi-)automatically crawl academic sites to identify curricula and automatically match them by using our algorithm.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,013 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,035 |
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 source (Gemma direct ou Codex distillé), 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 ».