Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory
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.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".