Explaining the Scientific Method to First Year Biology Students in an Accessible Manner
Bibliographic record
Abstract
The scientific method paired with the practice of statistics is widely implemented to analyze, interpret and derive conclusions regarding a dataset.This laboratory exercise introduced first year biology students to the scientific method, applying it to evaluate how brown planaria (Dugesia tigrina) responds to light.In the laboratory setting, students were required to formulate a research question and hypothesis, design a study, record observations, gather data and perform statistical calculations (mean, median, standard deviation, ttests), receiving feedback from their peers and teaching assistants.When surveyed, less than half of the students felt they had a strong background in mathematics or were comfortable in the subject.To resolve this, we produced multiple videos enabling students to review the content to help recall the material.These resources introduced students to terminology, provided them opportunities to practice usage of these terms, as well as instructions on how to perform statistical calculations.We employed an analysis of covariance (ANCOVA) to determine if the students who viewed the videos performed better on the statistical analysis and interpretation portion.Based on ANCOVA, the wet laboratory experiments in conjunction with video resources, resulted in increased student performance in the laboratory component compared to previous iterations without video tools.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.029 |
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".