Exploring the divergence between self-assessment and self-monitoring
Bibliographic record
Abstract
Many models of professional self-regulation call upon individual practitioners to take responsibility both for identifying the limits of their own skills and for redressing their identified limits through continuing professional development activities. Despite these expectations, a considerable literature in the domain of self-assessment has questioned the ability of the self-regulating professional to enact this process effectively. In response, authors have recently suggested that the construction of self-assessment as represented in the self-regulation literature is, itself, problematic. In this paper we report a pair of studies that examine the relationship between self-assessment (a global judgment of one's ability in a particular domain) and self-monitoring (a moment-by-moment awareness of the likelihood that one maintains the skill/knowledge to act in a particular situation). These studies reveal that, despite poor correlations between performance and self-assessments (consistent with what is typically seen in the self-assessment literature), participant performance was strongly related to several measures of self-monitoring including: the decision to answer or defer responding to a question, the amount of time required to make that decision to answer or defer, and the confidence expressed in an answer when provided. This apparent divergence between poor overall self-assessment and effective self-monitoring is considered in terms of how the findings might inform our understanding of the cognitive mechanisms yielding both self-monitoring judgments and self-assessments and how that understanding might be used to better direct education and learning efforts.
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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 | 0.047 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".