Evaluation of Online Log Variables that Estimate Learners’ Time Management in a Korean Online Learning Context
Bibliographic record
Abstract
The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a Learning Management System (LMS). Test was attempted of a structural equation model representing the relationships among Time and Study Environment Management (TSEM), one of the sub-constructs of MSLQ, influencing a set of time-related online log variables: login frequency, login regularity, and total login time. Data were collected from 188 college students in a Korean university. Employing structural equation modeling, a hypothesized model was tested for measuring the model fit. The results presented a criterion validity of online log variables to estimate their time management. The structural model including TSEM, online variable, and final score with a moderate fit indicated that learners’ time related online behavior mediates their psychological functions and their learning outcome. Based on the results, the final discussion includes the recommendations for further study and the meaningfulness in regard to the expantion of Learning Analtyics for Performance and Action (LAPA) model.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".