Equilibrium or Studying Attractors to Upgrade Educational Suitability, Teaching Presence, Social Presence and Learning Results in MOOCs
Bibliographic record
Abstract
The purpose of this research is to find the equilibrium or studying attractors to upgrade educational suitability, instructional existing, social existing and learning results in Korean Massive Open Online Courses (K-MOOCs). To conduct this study, learners who had taken K-MOOC were selected for the study; data were collected from 369 K-MOOC learners. The data were studied using the SPSS 26.0 and AMOS 26.0 platforms. About the methodology of analysis, descriptive statistics analysis was carried out first, followed by an examination of the correlation. The measurement model was then confirmed using confirmatory factor analysis (CFA), and structural equation modeling (SEM) was used to validate the structural links and mediating effects between the variables. Further, to verify a mediating effect on educational performance, the numerical importance of the mediating result was verified using bootstrapping. Analysis outcomes are as the following: First, this study discovered that K-MOOC educational appropriateness positively impacted learning results. Second, this study discovered that K-teaching MOOC's presence meaningfully and statistically significantly moderated the association between educational appropriateness and learning outcomes. Lastly, this research discovered that K-social MOOC's presence had a statistically significant impact on moderating the link between educational appropriateness and learning results. The conclusions that can be derived from the analyses' findings are the following: To improve the learning performance of K-MOOC, which is operated through the national budget, education authorities must first deeply understand the learning needs of learners in accordance with social changes. It is also important to secure educational suitability by preparing and providing a variety of educational courses in consideration of learners' learning motives and learning goals. Lastly, it is important to increase the satisfaction and immersion in education by expanding the components of teaching existence and social existence to derive actual learning results. This study is meaningful in that it examines the direct and indirect effects of the state-led K-MOOC's educational suitability on learning outcomes, and in that it confirms the influence of teaching existence and social existence in that they enhance education outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".