Uncovering Relationships between Task Understanding and Monitoring Proficiencies in Postsecondary Learners: Comparing Work Task and Learner as Statistical Units of Analyses
Bibliographic record
Abstract
Educational psychologists have researched the generality and specificity of metacognitive monitoring in the context of college-level multiple-choice tests, but fairly little is known as to how learners monitor their performance on more complex academic tasks. Even lesser is known about how monitoring proficiencies such as discrimination and bias might be related to key self-regulatory processes associated with task understanding. This quantitative study explores the relationship between monitoring proficiencies and task understanding in 39 adult learners tackling ill-structured writing tasks for a graduate “theories of e-learning” course. Using learner as unit of analysis, the generality of monitoring is confirmed through intra-measure correlation analyses while facets of its specificity stand out due to the absence of inter-measure correlations. Unsurprisingly, learner-based correlational and repeated measures analyses did not reveal how monitoring proficiencies and task understanding might be related. However, using essay as unit of analysis, ordinal and multinomial regressions reveal how monitoring influences different levels of task understanding. Results are interpreted not only in light of novel procedures undertaken in calculating performance prediction capability but also in the application of essay-based, intra-sample statistical analysis that reveal heretofore unseen relationships between academic self-regulatory constructs.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".