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Student Feedback
After my co-teaching stint in the CIRTL program, I sent a survey to gather feedback on the effectiveness of active learning strategies, real-world connections, and the final term project. Five students responded, providing both quantitative ratings and qualitative observations. Please go through the slides on the right for a summary of the results.
Key findings show strong student engagement with the pedagogical approach. 80% of students rated the active learning strategy as "much more" or "somewhat more" effective than traditional lecture formats. Real-world connections to aerosol phenomena scored highest (80% at top rating), and students consistently cited the balance between lecturing and questioning as a factor that supported their learning. Humor also emerged as a retention tool in an 8 am course section.
The goal of the term project was to help students integrate concepts across the course, and 60% gave it a top rating. However, feedback highlighted the scale and repetitiveness as areas for refinement in future iterations. Slide density was flagged as a minor issue as well, tightening information to prioritize examples over detail remains a target for improvement.
This feedback reinforces my commitment to fact-first inquiry paired with contemporary applications, while revealing concrete opportunities to reduce cognitive load and streamline assessments.
Some insights from the CIRTL materials
As part of the CIRTL program, I had partaken in two core courses,
The CIRTL MOOC "An Introduction to Evidence-Based Undergraduate STEM Teaching" (compulsory),
The CIRTL MOOC "Advancing Learning Through Evidence-Based STEM Teaching".
Here I have written down some pointers and thoughts from the entire material.
Students filter new information through ingrained, often flawed, mental models.
The Curse of Knowledge makes experts less effective at explaining basic concepts than peers.
Presenting a challenge before an explanation creates a time for telling.
The affective domain (a student's emotions and sense of belonging) is as important as their cognitive domain.
Capable students leave STEM primarily due to poor instruction, not a lack of innate ability.
Extrinsic motivators like grades can actively destroy intrinsic motivation of curiosity (shifting from strategic to deep learning).
Problem-Based Learning or PBL builds adaptive expertise through authentic contexts as in real-world applications.
Teaching-as-Research or TAR transforms instruction from guesswork into an iterative practice.
Some ideas I would like to explore further:
How does punitive vs. reward-based language in course syllabi affect STEM students' willingness to seek help and stay in their majors?
We learnt from the training about the cognitive benefits of peer instruction. How does social belonging dynamics specifically play out during peer-to-peer interactions when the instructor is not directly moderating the conversation.
While instructors track immediate course metrics (like withdrawal rates), there seems to be a gap in tracking the long-term career impacts on the graduate students and postdocs who execute TAR-like projects, specifically regarding their future faculty success and whether they maintain these practices years later.
There seems to be recent critiques of Bloom's Taxonomy, with the premise that cognitive skills are fluid, not a rigid hierarchy. However, real-world implementation of alternative frameworks remains underexplored in STEM.
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