Creation of Educational Innovations through Cloud-based Constructivism and Connectivism Learning for Undergraduates
Bibliographic record
Abstract
This research examined the process of creating educational innovations through cloud-based constructivism and connectivism learning for undergraduates. The objectives were as follows: (1) To examine the cloud-based constructivism and connectivism learning model’s roles in helping undergraduates create educational innovations; and (2) To evaluate the learning and innovation skills of students participating in cloud-based constructivism. The population in the study consisted of 60 undergraduates of Phetchaburi Rajabhat University, acquired using simple random sampling. The undergraduates were enrolled in a course for designing and developing games for education and computer-assisted mathematics instruction. The independent variable was the format of the constructivism and connectivism learning model for undergraduates involving cloud technology to promote innovative education. The dependent variable was the result of innovative education. The research findings were as follows: (1) Participants’ overall satisfaction with the model was at the highest level (x = 4.53, S.D. = 0.60). (2) Out of the 60 students, 21 created innovative educational workpieces. Four workpieces were accepted for academic publication, showing that this model allowed learners to analyze media, promote innovative education, and apply educational innovations.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".