Using the Principles of SoTL to Redesign an Advanced Evolutionary Biology Course
Bibliographic record
Abstract
A primary goal of university instruction is the students’ demonstration of improved, highly developed critical thinking (CT) skills. However, how do faculty encourage CT and its potential concomitant increase in student workload without negatively impacting student perceptions of the course? In this investigation, an advanced biology course is evaluated after structural changes (implemented in 2010) met with a poor student evaluation of the course and the instructor. This analysis first examines the steps used to transform a course to encourage CT and then explains how it can be assessed. To accomplish these goals, the instructor collaborated with an educational developer to redesign the course using a philosophy informed by SoTL. This approach, as we see it, represents a set of principles that demand transparency in the development and application of strategies whose aim is to encourage student learning. However, the SoTL approach would be insufficient to simply promote a set of strategies without some mechanism for evaluating its efficacy. Therefore, we designed a “Graded Response” (GR) multiple-choice test to measure CT development and hence to properly evaluate whether the strategies embedded in our SoTL-informed course redesign have adequately met our goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".