Reflect, Review, and Revise: Using Checklists to Improve Students’ Lab Reports
Bibliographic record
Abstract
We present preliminary findings from our study on how to help 2<sup>nd</sup> year Electrical and Computer Engineering (ECE) students enrolled in a design course improve the written quality of their reports. ECE students focus on the design aspects of their lab projects but tend to neglect the textual aspects. Hence, our motivating inquiry aimed to find a convenient, low-investment way of getting ECE students to improve the formatting, graphics, and style of their reports without the professor or TAs having to give up valuable lab or lecture time to writing instruction. The proposed solution is uniquely simple and manageable: a self-review checklist. To determine whether using a self-review checklist would prompt students to improve their writing, we compared and contrasted 27 reports produced by groups of students before they had access to a checklist with reports produced after they had access to a checklist. Although improvements in writing style appear to be minimal, early analysis shows that robust gains were made in document formatting and integration of graphics.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one teacher head, 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".