Rafting Together with Lipids ‐ Team Teaching & Active Learning
Notice bibliographique
Résumé
Effective oral and written communication skills are key assets for success in and outside of academia. However, students completing undergraduate degrees are often not equipped with these skills. Among the challenges that contribute to this problem is the lack of interest by teachers to conduct in‐class activities that provide students the opportunities to nurture these skills. It can be very time consuming for a single instructor to construct and implement multiple active learning activities, designed to encourage and give students the chance to develop these important communication skills. Additionally, the traditional lecture format hinders development of higher Bloom’s Taxonomy skills and limits opportunities for engagement. In order to overcome this problem, we joined forces with Biochemistry professor, Vanina Zaremberg at the University of Calgary to execute active learning techniques in a fourth year Lipid Biochemistry course. Having multiple instructors allowed us to implement three in‐class activities: construction of concept maps, debate, and guided writing assignment , thoughtfully designed to give students the opportunity to learn and utilize communication and writing skills. Anonymous surveys were conducted following completion of these activities, giving us valuable feedback. The first activity was centered around building concept maps and was designed to tackle the challenge of the varying background knowledge of the students at the beginning of the semester. In short, students drew a concept map on the very first and last day of classes, which not only allowed the instructors to see what information they were bringing into the course, but also allowed the students to connect and organize their knowledge in a thoughtful manner. Rich discussion among instructors on these maps helped to develop lesson plans and outline lesson outcomes. The concept maps conceived on the last day of classes gave an explicit visual of what the students learned throughout the semester. The second activity, the in‐class debate on lipid rafts, allowed students to openly discuss and learn about a key controversial topic in Lipid Biochemistry in a unique way and without the pressure of a typical quiz or assignment. Surprisingly for the instructors, the debate took up the entire 75‐minute lecture. One hundred percent of the responses answered that the debate was an effective way to learn about the topic and students enjoyed learning from their peers. Finally, a guided writing assignment was executed, as it has been reported that students lack experience critiquing published work or a peer’s writing. The writing assignment gave students the opportunity to exercise and develop communication and writing skills while participating in a guided peer‐review process. 79% of the class reported that this was the first time they did a peer‐review exercise, even though most were in their final semester of their undergraduate degree. Overall, the feedback gathered from the survey indicated that students were involved, more confident in the topics, and enjoyed these activities more than typical lectures, suggesting that team teaching enhances student experience and engagement.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,017 |
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 ».