Using classroom/learning assessment techniques (CATs) in biology labs and classes
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".