Deconstructing the lab notebook and scaffolding assessments in a course-based undergraduateresearch experience (CURE)
Bibliographic record
Abstract
The pedagogical framework of a course-based undergraduate research experience (CURE) involves having students learn essential experimental techniques, design an experiment, carry out the experiment, interpret data and communicate results (McLaughlin & Coyle, 2016).Common assessments used in a CURE are a research proposal, lab notebook, and manuscript/poster/presentation.However, there are several challenges with the use of the lab notebook as an assessment tool.For example, grading of the lab notebook often occurs after an experiment is complete.Errors in understanding or calculations are not caught prior to the students performing the experiment which can lead to a waste of expensive reagents and potentially limit the ability to do CUREs with molecular techniques.Furthermore, finding and assessing information in a lab notebook is extremely time consuming and comments may not be read or understood.To address these issues, I deconstructed the lab notebook into experimental plans and data submissions.Using these assessments, my observations in the lab (echoed in student feedback) are that students come to the lab more prepared, understand the protocol, and feel confident in their ability to troubleshoot if something goes wrong.Importantly, the experimental plans can be peer graded in class while the instructor goes over the information.This provides important, timely, and relevant feedback to the students.Lastly, the use of experimental plans and data submissions allows the scaffolding of assessments for the scientific communication pieces (manuscript/poster/presentation).
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 | 0.033 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".