METACOGNITION AND IT: THE INFLUENCE OF\nSELF-EFFICACY AND SELF-AWARENESS
Bibliographic record
Abstract
Organizations are increasingly relying on employees to self-manage their learning needs.Therefore, it is important for individuals to accurately assess their IT knowledge because accurate self-assessment is critical to effective self-management.Metacognition represents individuals' self-monitoring and self-regulating abilities, and plays a key role in self-managed learning.This study examines two dimensions of metacognitionself-efficacy and self-awareness.We aim to understand how self-efficacy and self-awareness influence individuals' metacognitive process and contribute toward increased effectiveness in self-managed learning.We argue that greater confidence in ability will result in increased self-awareness and learning outcomes in IT.Study findings suggest that increased computer self-efficacy is related to increased self-awareness and over-estimation.Low confidence in abilities was found to be related to under-estimation and lower levels of self-awareness.Therefore, under-estimation was found to be detrimental to learning outcomes while overconfidence was found to be beneficial.Further research is required to understand the threshold between beneficial and detrimental miscalibrated self-awareness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".