Factors Affecting Learning Gains among Students in Microbiology Class: A Preliminary Study Between a U.S. Community College and a Canadian Comprehensive University
Bibliographic record
Abstract
Though in the past, serious concerns have been raised about students’ interest and learning gains in STEM courses, not much research has been done to examine the differences in learning science at community colleges and universities. The purpose of this paper is to close this gap. This paper analyzes the influence of students’ demographics, preparedness, major, and attitudes on their learning gains in an introductory microbiology class at a community college vs. a university. Student demographics, information about their preparedness level, major, and attitudes were collected in a questionnaire and students’ learning gains were assessed by comparing student performance on a pre- and post-test on four different topics in microbiology. Our results indicate that students’ majors and attitudes such as their willingness to actively participate in the classroom discussions and spend time outside the classroom to learn are major factors that enhance their learning. Age and marital status positively impact learning gains while gender, employment status, and citizenship status show no impact on learning gains in students. Our results also indicate that students at the community college who had less exposure to science classes in high school or biology classes in college achieved statistically higher learning gains despite having overall lower scores on two of the four post-tests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".