Adopting the Pedagogy of Trust and its Impact on Learning
Bibliographic record
Abstract
In this work, we present the results of our attempts to transit into a trust-based assessment environment for adult learners. The impact of utilizing an honour system of assessment throughout a course on data structures and algorithm design has been evaluated. The performance of the students has been compared with the performance of students from two previous cohorts that appeared for the same assessments in an invigilated environment. We found that with adult learners, who are more focused on learning the concepts to hone specific skills for applicability at their workplace, the performance variation between the test cohort and the reference cohorts was not significant. With further evidence of this promising initial step, we could evolve into a larger portfolio-based education framework in which students can showcase their competence and skills through a collection of projects and assessments (formative as well as summative), to help with their career growth. Establishment of such pedagogies will help our students step out of their comfort zone, undertake exploratory studies, be willing to unearth their vulnerabilities, and work to improve their shortcomings to help them advance their careers.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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 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".