The Outcomes of Analytical Thinking and Curiosity from the Virtual Smart Classroom using the Cooperative Problem-Based Learning
Bibliographic record
Abstract
This research is intended to study the outcomes of analytical thinking and curiosity derived from the virtual smart classroom using the cooperative problem-based learning; thereby the said virtual smart classroom is a research tool initiated from the integration of the problem-based learning and the cooperative learning coupled with the aid of artificial intelligence and virtual technologies to facilitate learning activities. The development of the said research tool was conducted with the Spatial.io platform and the application ClickUp, which is widely employed to promote learning and organize the works with high quality and attractiveness. This research relies on the pre-experimental research method. The data in this study were collected from 30 research participants, who are vocational certificate students (dual vocational education) of Year 1 and Year 2 in Semester 2 of Academic Year 2024 at Nakornluang Polytechnic College. These participants were derived by means of cluster sampling and well protected with the policies of confidentiality and anonymity in accordance with educational ethics. The research instruments used for data collection include the evaluation form on analytical thinking, and the evaluation form on curiosity. The results of this study correspond to the three research hypotheses and this insists that: (1) the outcomes of analytical thinking after learning with the platform are at a very good level (mean=7.23, SD=1.41); and (2) the outcomes of curiosity after learning with the platform are at a very good level (mean=10.43, SD=1.65). It can be clearly seen from the aforementioned results that the virtual smart classroom using the cooperative problem-based learning is so efficient that it can be applied in practical use so as to promote learners' analytical thinking and curiosity. This is because the said platform provides learners with hands-on self-learning experiences coupled with active learning and collaborative learning, which is said to enhance their learning achievement as well.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".