Exploring the Impact of Jargon on Student Learning in Biology: Student Understanding, and Self‐Perception of Understanding, of Technical Vocabulary
Notice bibliographique
Résumé
The ability to communicate and collaborate is a core concept in biology education1, and includes mastery of both conceptual ideas as well as technical vocabulary. The “jargon load” is a particularly prominent hurdle in introductory biology courses, which are notorious for the vast quantity of new terms, often more than in a high school language course2. Previous research has shown that the jargon load can negatively impact learning3, 4. However, little work has been done to widely characterize student understanding of biology‐specific jargon, and to distinguish between types of jargon that may differently impede student learning. The purpose of this study was to assess students’ actual and self‐perceived understanding of various biological terms presented in first‐ and second‐year undergraduate biology courses in order to determine 1) the types of terms that students struggle with most, and 2) identify common errors in their understanding. In three large undergraduate biology classes, students were given an online survey about the specific terms they had seen in the course; the survey prompted them to assess their recognition and understanding of each term, as well as give definitions in their own words. In total, there were 93 vocabulary terms with over 2,400 student responses. The terms were grouped into thematic categories to facilitate analysis. Our results indicate that students struggle the most with Molecular terms (names of molecular structures). Interestingly, there was a significant difference between student's self‐perceived understanding and their actual understanding, and these differences vary between types of jargon. The least accurate self‐assessment was found for the following categories: Information (describing information transfer processes), and Incompatible Ambiguity (terms with precise scientific meanings that are used less precisely in everyday language). Analysis revealed that students often showed an overestimation of understanding: a significantly large proportion of incorrect definitions were submitted despite a high proportion of students self‐reporting that they understood the terms. For example, for terms in the Incompatible Ambiguity category, 83% of students claimed they understood the terms, but only 26% of the definitions submitted were correct. The findings of this research provide insights about which technical vocabulary may indeed be jargon, and possibly create a barrier to developing deeper conceptual understanding. Additionally, our results shed light on the variation in types of jargon, and highlight a need to consider student understanding of different types of jargon to support learning and scientific literacy. Support or Funding Information This work was supported by a Teaching and Learning Enhancement Fund grant from the University of British Columbia.
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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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».