Impact of Connectivism on Knowledge and Willingness of Students in Higher Education
Bibliographic record
Abstract
This study investigates the impact of connectivism on knowledge acquisition and the willingness of higher education students to apply that knowledge in practical settings. Using an experimental design, it investigates how connectivism manifests in learning processes, particularly focusing on a collaborative online international session (COIL) with 92 business management students from the UAE and South Korea. These students participated in a COIL session aimed at enhancing their understanding of diversity and inclusion management concepts. The study utilized an independent t-test to evaluate the effectiveness of COIL, comparing groups exposed to different modes of participation (connectivism mode and nonconnectivism mode). The results highlight connectivism’s role in increasing students’ willingness to utilize acquired knowledge. As a connectivism approach, COIL proves pivotal in applying learning practically. This research offers significant insights for curriculum designers, educators, and scholars, demonstrating the impact of social connectivism on learning enhancement. It provides valuable information for incorporating connectivism into traditional educational models, thereby enriching the theoretical and methodological understanding of the relationship between connectivism, COIL, knowledge acquisition, and application willingness. This study is particularly relevant for educators looking to integrate innovative methods in their teaching and expand the scope of knowledge and skill development for future work.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".