The Immersive Connectivist Learning Environment to Enhance Creative Innovation Skills for Higher Education
Bibliographic record
Abstract
This research report titled creating an immersive connectivist environment to enhance creative innovation skills for higher education aims to 1. design an immersive virtual learning environment that connects knowledge to enhance creative innovation skills for higher education students. 2. evaluate the quality of the designed immersive connectivist virtual learning environment. 3. compare students’ academic achievement before and after using the immersive environment and 4. examine students’ satisfaction with the immersive learning environment. The sample group consisted of 30 experts and 33 undergraduate students from Udon Thani Rajabhat University. The findings revealed that 1. creative innovation skills in higher education can be categorized into seven main areas (analytical and problem solving skills, teamwork, storytelling and presentation, self-directed learning, creative risk-taking and experimentation, digital and technological literacy, and adaptability and flexibility). 2. The environment was rated highly appropriate by experts, with an average score of 4.43 and a standard deviation of .50. 3. A paired-sample t-test was conducted to examine the difference in students’ academic achievement before and after using the immersive learning environment. The results showed that the post-test scores (M = 41.61, SD = 2.68) were significantly higher than the pre-test scores (M = 30.85, SD = 2.48), with a t-value of 12.50 and p < .05. This statistically significant result indicates that the immersive environment had a positive effect on students’ academic performance and 4. students expressed high satisfaction with the immersive learning experience, with an average score of 4.38 and a standard deviation of .49.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".