Foundation Skills for Scientists: An Evolving Program
Bibliographic record
Abstract
We have undertaken an integrated and collaborative approach to developing foundational skills of students in a first year, Introductory Biology course. The course is a large lecture and laboratory course with enrollments ranging from 800-1000 per year. Teaching and Learning experts were brought into the course as weekly ‘Foundation Skills for Scientists’ sessions were created. The initial challenges were to have effective knowledge exchange between collaborators and create an integrated course syllabus. Once effective sessions were created, the next challenge was to improve student valuation of them. High value was only achieved when the skill sessions were tightly linked to course assignments and activities and was delivered ‘just in time’. Even then, the challenge has been to motivate students to realize that the sessions are directly relevant to them. Overall, student performance has improved since the program was initiated as measured by rate of retention in the course, overall course marks and quality of writing. Nous avons utilisé une approche intégrée et collaborative pour approfondir les compétences de base des étudiants de première année qui suivent un cours d’introduction à la biologie. Il s’agit d’un cours magistral et en laboratoire, auquel s’inscrivent entre 800 et 1000 étudiants par an. Ce cours a bénéficié de l’apport d’experts en enseignement et en apprentissage afin d’appuyer le développement de séances hebdomadaires portant sur les compétences de base en sciences. Les difficultés initiales étaient de susciter un échange de connaissances efficace entre les collaborateurs et de créer un plan de cours intégré. Une fois les séances organisées, la difficulté suivante a été de faire en sorte que les étudiants les apprécient davantage. Ces derniers les ont jugées très utiles uniquement lorsqu’elles étaient étroitement liées aux tâches et aux activités et lorsqu’elles étaient offertes au moment opportun. Même alors, le défi a consisté à motiver les étudiants afin qu’ils se rendent compte que les séances leur sont directement pertinentes. Dans l’ensemble, la performance des étudiants s’est améliorée depuis le début du programme comme l’indiquent les mesures du taux de persévérance dans le cours, les notes générales et la qualité de la rédaction.
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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".