Reflecting on Learner Assessments and Their Validity in the Presence of Emerging Evidence from Neuroscience
Bibliographic record
Abstract
We can now get purposefully directed in the way we assess our learners in light of the emergence of evidence from the field of neuroscience. Why higher-order learning or abstract concepts need to be the focus in assessment is elaborated using the knowledge of semantic and episodic memories. With most of our learning identified to be implicit, why we should make use of the constructivist theory in assessing learners becomes quite evident. Why we need to deviate from setting assessment on the basis of veridical decision making and the need incline towards adaptive decision making become evident when we understand that most of our life decisions are adaptive in nature and human beings naturally possess creative instincts. When assessments are used to direct learners to use the frontal lobes, the organ of civilisation, more, the requirement of more carefully designing the timing component of assessment arises. After all, it is important to understand that enhancing learner consciousness and wisdom is key when we understand the prime goal of education is to enhance human development of learners so as to enable them to be better problem solvers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".