Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as R, which are better suited to analyzing complex data sets. However, teaching biostatistics and programming simultaneously has the potential to overload the students and hinder their learning. We sought to mitigate this overload by using cognitive load theory (CLT) to develop assignments for two biostatistics courses. We evaluated the effectiveness of these assignments by comparing student cohorts who were taught R using these assignments ( n = 146) with those who were taught R through example scripts or were instructed on a point-and-click software program (control, n = 181). We surveyed all cohorts and analyzed statistical and programming ability through students’ lab reports or final exams. Students who learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their curricula vitae. We also found that the treatment students were more motivated, less frustrated, and less stressed when using R. These results suggest that we can use CLT to teach challenging material.
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.
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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it