Facilitated Study Groups for Undergraduate Organic Chemistry: Experience from a Large Public Canadian University
Bibliographic record
Abstract
Undergraduate organic chemistry courses have a reputation for being difficult among students in biological and physical sciences programs. Due to the extensive problem-solving, visualization, and depiction of chemical structures/reactions required, students may perceive learning such content as similar to learning a new language. Several interventions such as course-integrated tutorials or discussion sessions have aimed to assist students. Another effective approach that chemistry educators might consider is Supplemental Instruction (SI), a well-established program that emphasizes student-driven learning whereby student SI leaders facilitate discussions to help students arrive at solutions while also developing effective communication and study skills. A type of SI, Facilitated Study Groups (FSG), established by the Centre for Teaching and Learning at the University of Toronto Scarborough (UTSC) in 2009, were introduced regularly into organic chemistry courses in 2012. This program provides semester-long optional small-group peer learning sessions, each of which corresponds to a course lecture. The aim of this paper is to provide comprehensive coverage detailing the structure of the organic chemistry FSG program, peer facilitation strategies employed, quantitative/qualitative synthesis of student outcomes indicating program uptake. We consistently find significantly higher grades and significantly lower attrition rates for students who regularly attend FSG sessions in comparison to those who do not (n=16 semesters). Given the growing diversity of undergraduate classes in terms of approaches to learning, language, and cultural barriers (international students, English second-language learners, learning and psychosocial disabilities), our FSG sessions seek to foster inclusion amongst our heterogeneous pool of attendees. Here, we describe strategies that tailored FSG sessions to a diverse group of undergraduate students as suggested by a sizable percentage of the class availing themselves of this resource and by a narrative synthesis of end-of-term surveys. Together, we demonstrate successful adoption of an SI-based model for organic chemistry and present a practical framework that includes pedagogically informed session strategies and cost estimates to guide design of similar programs for post-secondary students at other institutions.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".