The influence of e-learning on individual and collective empowerment in the public sector: An empirical study of Korean government employees
Bibliographic record
Abstract
Our study explores the influence of e-learning on individual and collective empowerment by using data collected from e-learning class participants of Korea’s Cyber-Education Center. For the survey, a questionnaire was sent to each of the 41 central ministries’ education and training officers (ETO) via email. The ETOs distributed the questionnaire to individuals in their ministries who have taken e-learning classes offered by the Cyber-Education Center during the first half of 2012. Out of more than 1,000 e-learning class attendees, 161 responded to the questionnaire survey. A set of multiple regression models was employed to explore significant predictors of government employees’ individual and collective empowerment in e-learning environments. Using existing literature on empowerment theories, a set of 16 questions was developed. A factor analysis was conducted to condense 16 individual variables into several large categories. Four factors including meaning, competence, self-determination, and collective empowerment were extracted from the 16 questions. The first three equations stood for individual empowerment and the last one for collective empowerment. Each of the four factors was utilized as a dependent variable in the multiple regression analysis. Each regression model uncovered its own set of variables that played a role in empowerment. The predictor variables of the meaning dimension were more widely split than those of the competence dimension or the self-determination dimension and collective empowerment. Only one independent variable—preference of e-learning class to offline class—was associated with all four dependent variables. However, modalities of e-learning activity, which were expected to be a significant predictor of empowerment, were not associated with any of the four dependent variables. In addition, lecture types of the e-learning class were also expected to be a significant predictor of empowerment but were only associated with the competence dimension.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".