Joining students on their SLICCs journey
Bibliographic record
Abstract
At Waterloo Engineering, we have great student leaders who go far beyond the average of 120 hours needed for a course credit in leadership roles, but currently receive no academic credit for this work. The SLICC (Student-Led, Individually-Created Course) model, developed by professors at the University of Edinburgh, is a great way to help the student leaders reflect on their own leadership experiences in a personalized format, producing a product that is of value to them. That is the motivation for a new course, offered in the winter 2022 term for the first time, GENE 415: Practical Analysis of Student Leadership Experience. As instructors, we were completely new to the SLICC model. After some basic training in the mechanics of the SLICC process with folks at Waterloo who are implementing it in their courses and support from folks at the University of Edinburgh, we put ourselves through a SLICC project with our students. This was done with lots of support from a senior educational developer from the Centre for Teaching Excellence. This is the story of SLICCs being implemented by two seasoned instructors and their educational journey to guide ten senior engineering student leaders through a new course designed to acknowledge, through course credit, their substantial leadership experiences throughout their undergraduate studies in engineering. This SLICC experience was completed at the height of the Omicron wave of COVID-19 in Ontario, revealing both the benefits and challenges of this self-directed learning model being implemented in an online environment and then shifting to in-person.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.165 | 0.068 |
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".