A systemic transformation of an arts and sciences curriculum to nurture inclusive excellence of all students through course-based research experiences
Bibliographic record
Abstract
Introduction We describe herein a large-scale, multidisciplinary course-based undergraduate research experience program (CRE) developed at Lawrence Technological University (LTU). In our program, all students enrolled in CRE classes participate in authentic research experiences within the framework of the curriculum, eliminating self-selection processes and other barriers to traditional extracurricular research experiences. Methods Since 2014, we have designed and implemented more than 40 CRE courses in our College of Arts and Sciences involving more than 30 instructors from computer science, mathematics, physics, biology, chemistry, English composition, literature, philosophy, media communication, nursing, and psychology. Results Assessment survey data indicates that students who participate in CRE courses have an enhanced attitude towards research and discovery, as well as increased self-efficacy. This intervention is particularly relevant for non-traditional students, such as students who commute and/or have significant work or childcare commitments, who often experience limited access to research activities. Discussion Herein we highlight the importance of a systemic institutional change that has made this intervention sustainable and likely to outlast the external funding phase. Systemic change can emerge from a combination of conditions, including: (1) developing a critical mass of CRE courses by providing instructors with both incentives and training; (2) developing general principles on which instructors can base their CRE activities; (3) securing and maintaining institutional support to promote policy changes towards a more inclusive institution; and (4) diversifying the range of the intervention, both in terms of initiatives and disciplines involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".