High Structure Active Learning Pedagogy for the Teaching of Organic Chemistry: Assessing the Impact on Academic Outcomes
Abstract
Organic Chemistry is a required course for programs in chemistry, biology, and many health science careers. It has historically been considered a highly challenging course with significant failure rates. As with many science disciplines, the teaching of Organic Chemistry has traditionally focused on unstructured exposition-centered delivery of course material. This report details efforts to transform the teaching of a large section of Organic Chemistry to a more student-centered approach through the use of a highly structured, active learning format. In this study, the authors examine student performance data on homogeneous examinations and course grades for two groups of students at a large public university for comparison between those that received highly structured active learning pedagogy and those that received traditional, unstructured, lecture pedagogy in Organic Chemistry. Data consist of repeated cross sections over 4 different years, with a total sample size n = 766. Regression and propensity score matching (PSM) are used to analyze student academic outcomes. Results suggest that students exposed to a highly structured, active learning pedagogy in Organic Chemistry scored statistically significantly higher on total points earned and final exam scores, and had a higher probability of an increase in their final course grade. Results further suggest that while increased structure in conjunction with active learning improved outcomes generally for all students who received it, those students at the lowest academic achievement levels experienced the most gains. This study is significant in its causal analysis of the impact of highly structured active learning on academic outcomes.
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Students experienced greater academic improvement and a positive impact on their learning correlated with active learning pedagogy.
“[120] showed that the students experienced greater academic improvement and a positive impact on their learning correlated with active learning pedagogy”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [120]
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Green chemistry based: Development of substitution reactions experiments
Mulyanti, S., Kadarohman, A., & Sardjono, R. E. (2022) · AIP conference proceedings · doi:10.1063/5.0112195 · Cited by 2 · see citing works
CASH: Collaborative Activity on the Sash of the Hood
Kennedy, S. M., & Dreon, O. (2019) · Journal of Chemical Education · doi:10.1021/acs.jchemed.8b00423 · Cited by 2 · see citing works
Chemical Structure Data Retrieval Algorithm for Chemistry Online Teaching
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Verma, A. (2022) · Journal of Chemical Education · doi:10.1021/acs.jchemed.1c01271 · Cited by 2 · see citing works
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- Authors
- Crimmins, M. T., & Midkiff, B.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Crimmins, Michael T.
- Venue
- Journal of Chemical Education
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-02-08
- Volume
- 94
- Issue
- 4
- Pages
- 429-438
- ISSN
- 0021-9584 (ISSN-L)
- 1938-1328
- OpenAlex
- W2587462225
- Cited by
- 101
- References
- 42
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 8 · 2023: 8 · 2022: 15 · 2021: 16 · 2020: 15 · 2019: 20 · 2018: 4
- FWCI
- 38.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Education (publishedVersion)
- Funders
- Leona M. and Harry B. Helmsley Charitable Trust
- Topics
- Social Sciences › Education › Innovative Teaching Methods
- Social Sciences › Education › Chemistry Education and Research
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Various Chemistry Research Topics
- Keywords
- organic chemistry education, active learning, student-centered learning, lecture method, educational outcomes, course grades, final exam performance, low-achieving students, course failure rates, teaching reform
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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