Student Worksheet Based on AR Cells and Tissues to Train Spatial Thinking and Problem Solving
Bibliographic record
Abstract
In the contemporary educational landscape of the 21st century, students encounter numerous challenges in cultivating advanced competencies across varied skill sets, notably in spatial thinking ability and problem-solving skills. This study aims to develop an Augmented Reality (AR) application focusing on Cells and Tissues, designed to enhance spatial thinking ability and problem-solving skills among biology students and prospective biology educators during lectures on Plant Development Structures. The objective is to provide practical and effective learning experiences. A key methodological approach is the integration of an augmented reality application, termed "AR Cells and Tissues," which specifically addresses plant anatomy with an emphasis on cells, tissues, and plant organs. The practicality of the application is evaluated through user feedback, while its effectiveness is assessed based on indicators related to spatial thinking ability and problem-solving skills. User satisfaction resulted in a mean score of 3.55, categorizing the application as "Very Practical." Furthermore, the effectiveness metrics reveal that spatial thinking capacity averaged 84.88, categorized as "High," while problem-solving capacity achieved a mean score of 77.73, also indicating a "High" level. These findings suggest that the development and implementation of the AR Cells and Tissues application considerably enhance spatial thinking ability and problem-solving skills, particularly within the realm of biology education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".