Implementing EDIA: Building belonging into the laboratory learning environment
Bibliographic record
Abstract
Actively building a sense of belonging in students has been shown to increase engagement, retention, and enjoyment of students in courses, programs, and higher education in science.A recent meta-analysis of an early intervention tested in 45 American colleges and universities showed the power of creating a sense of normalcy that difficulties will arise when moving and making a new home to produce a growth mindset that these are common perceptions that can be resolved by persisting and trying again.Setting expectations of challenge and sharing the experiences of previous students helps to prepare a growth and resilience mindset in freshmen before they begin college or university (Walton et al. 2023).The journal Cell Biology Education has published a clear guide to evidence-based pedagogies that develop a sense of belonging in classes: https://lse.ascb.org/evidence-based-teaching-guides/inclusive-teaching/pedagogical-choices/#belonging.Groups of students with diverse perspectives have been shown to be better at problem solving than students who are only high AbstractLaboratories are natural active learning environments where students are immersed in learning the cognitive and physical skills to apply the conceptual knowledge of the course.To create an inclusive environment that makes every student feel welcome and respected, we can use many methods to build a sense of belonging to a supportive learning environment that fosters their development as scientists.Introductory surveys based on the student's values and barriers they face gives students the message that they are considered individuals and their opinions are encouraged.A classroom culture that encourages group work, generating big questions that they want answered by the course, flipped classroom activities that generate collaborative problem solving, and a lab culture that encourages peer teaching all contribute to a supportive learning experience.Organization of students in diverse assigned pods of four students, group in-lab assignments, peer review of draft student research papers before assessment by markers improve every student's understanding and achievement.Additional benefits are the development of the student's ability to critically evaluate their own work, respect viewpoints and abilities outside of their usual peer group and sometimes make new friends.Learning management systems designed with Universal Design for Learning principles provides choices for student's pre-lab preparation and automatic marking of pre-lab quizzes frees TA time for assignment feedback and focusing on struggling students during laboratory sessions.Transformation of courses by small changes each term is possible with a phased-in approach of inclusive initiatives.Discussion and suggestions from participants were encouraged during the conference workshop and are included here.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".