Bibliographic record
Abstract
To improve and broaden participation in science, technology, engineering and math (STEM) requires assessments of learning that are created within a compassionate learnercentered philosophy with heightened attention to ethics and bias.This poster proposes a path towards scalable, learner-mediated assessment instruments of higher-order cognitive abilities that would be convenient to implement at large scales or in any university class. MotivationIt is increasingly more valuable for students to learn higher-order skills such as creativity and critical thinking than to learn factual or procedural knowledge (OECD, 2018), especially as artificial intelligence gains sophistication.Yet many assessments of learning and teaching in STEM (science, technology, engineering, and math), especially at large scales, tend to emphasize and reward lower-level factual and procedural learning (e.g., Momsen et al., 2013).Furthermore, many historical and current ways of assessing knowledge and abilities disadvantage marginalized groups (e.g., Kincheloe et al., 1997;Clark, 2013).Learner-centered instructional approaches help students develop higher-order skills in STEM (e.g., Etkina & Planinsic, 2015;Handelsman et al., 2006) and especially help underrepresented students (e.g., Theobald et al., 2020).Yet faculty are slow to adopt learner-centered approaches (e.g., Henderson & Dancy, 2007).Most universities primarily assess teaching through student evaluations, which are biased against minoritized faculty and may incentivize authoritative approaches (e.g., Kierstead et al., 1998;Deslauriers et al., 2019).To transform educational institutions into equitable systems will require rethinking how we perform and use assessments, both for providing students with credentials and for judging the effectiveness of instruction.
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 | 0.048 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".