Using classroom/learning assessment techniques (CATs) in biology labs and classes
Notice bibliographique
Résumé
Have you ever wondered how your students are doing in your lab or class, or where they are in their unique learning journey?How can you assess or check-in with your students before the end of the term?How can your students self-assess where they are in your lab or class relative to the course intended learning outcomes?Obtaining informal student feedback is where Classroom Assessment Techniques, or CATs, can support as an educational tool for instructors, Teaching Assistants (TAs), lab coordinators, and other educators in not only identifying where your students are, but also informing and supporting your own teaching.CATs have the additional role of supporting our students by serving as a selfassessment tool and as a frequent check-in throughout the term.Classroom Assessment Techniques are generally anonymous, non-graded, in-class or in-lab activities for obtaining feedback from students around their learning.They help students to selfassess their own progress in a course or lab.CATs may highlight areas of confusion or uncertainty for students, and they can signal to an instructor that perhaps additional support, readings, or a revisit the next class on a specific topic is needed.They can also serve as a quick check-in with students around a lab protocol before embarking on an experiment or assay.Angelo and Cross (1993) argued that CATs are an effective way to receive meaningful feedback from your students related to your teaching and can inform the learning taking place by the students.CATs can range from a short 2-minute exercise to a longer exercise and are highly adaptable and modifiable to fit your unique class or lab dynamic and your teaching needs.Commonly used examples include a minute paper, muddiest point, one-sentence summary, application card, or flash cards -but there are many more.You can review an abbreviated version of 50 different CATs here.What follows is one interpretation of how to use a few different CATs, but it is not the only way you can use the CAT.Depending on where you look or who you ask, there may be different ways that others share how they use CATs.You can adapt and modify any CAT to support the needs of your unique student group, your teaching interests and needs, and your lab or class environment.A minute paper is a CAT that you can use whenever you want to give students a pause in their learning (Bachhel and Thaman, 2014) to synthesize what they have learned in one minute (or so).You can give the students a prompt based on the learning that has occurred and ask them to write what they are taking away in one minute.Then, you can collect those papers (or if it is electronically completed you can review the submissions) and quickly glance to see what the key components your students are taking away.If what they have written does not match what you were hoping they would take away, you may need to either revisit your intended learning outcomes, provide supplemental materials, or revisit the topic next class.The one-sentence summary prompts students to synthesize their learning from a certain unit, topic, or module into a single sentence.Often, the prompt can be written in such a way that students Mini Workshop: Classroom Assessment Techniques (CATs) in Biology
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 | 0,037 | 0,095 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».