Frequency Analysis of Terminology on Engineering Examinations
Notice bibliographique
Résumé
Abstract Frequency Analysis of Terminology on Engineering ExaminationsThere have always been differences between instructor expectations of what students “shouldknow” and the actual background experience that students have entering an engineeringprogram. The divergence between this assumed knowledge and the actual knowledge base maybe increasing as the student population diversifies. Previous work has noted the impact ofdiversity in this regard. The issue is not just wide differences in preparation in basic math, orscience, or communication ability, but diversity in the cultural background of students. Whilewe frequently laud diversity we have not always followed this up by supporting inclusivity in ourclassrooms and finding ways to bridge cultural differences that may exist. Specifically, when wecontextualize technical material to situate an engineering problem in a real-world scenario,students are subject to a test of their background experience – so, instead of clarifying a technicalconcept, the context may make the concept more inaccessible. This may also compromise theinclusivity of the learning environment, causing students to doubt their suitability for studyingengineering.Current engineering students bring with them a wealth of knowledge that is as diverse as thebackgrounds and cultures they represent. However, students may be unfairly disadvantagedduring examinations, for example, if they are expected to understand terminology that assumes aspecific set of a priori experience; instead of assessing whether the student understands thetechnical material, assessments may inadvertently test vocabulary. This is especially importantfor examinations because the closely-supervised setting prohibits assistance. Yet, pedagogicallywe would prefer to assess understanding of concepts in authentic situations, not in the abstract.And a number of effective methods, such as model-eliciting activities (MEA’s), are based onauthentic contextualization.This represents an instance where learner characteristics are misaligned with the expectations ofthe learning environment, and there has been little research in this particular area of engineeringeducation. The goal of the current study is to evaluate the frequency of this type ofmisalignment. As raw data we are using an exam bank that contains final examinations collectedover a number of years for all engineering courses at a large engineering school. A frequencyanalysis of the words and terms used on the exams has been carried out, excluding coursespecific technical terminology. At this point in the study we are assuming that infrequently usedwords and terms are typically less familiar to students. This is an assumption that will be testedin a subsequent phase of the study.The results of the frequency analysis are analyzed with respect to: 1. The types of words and terms that are used on exams. 2. The types of courses where contextualization appears to be used most often. 3. The types of contexts that appear most frequently.The results will be discussed within the theoretical framework of learner characteristics andinteraction with the learning environment. In particular, we will examine these results withreference to the literature on Universal Instructional Design (UID), and current work on learnerdiversity.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».