A context-aware ubiquitous learning approach to conducting scientific inquiry activities in a science park
Bibliographic record
Abstract
Fostering students' scientific inquiry competence has been recognised as being an important and challenging objective of science education. To strengthen the understanding of science theories or notations, researchers have suggested conducting some learning activities in the field via operating relevant devices. In a traditional in-field scientific inquiry activity, the teacher usually lets the students operate the devices on their own after demonstrating the operational procedure. With such an approach, the students are likely to suspend the practice when they encounter problems; moreover, it is difficult for the students to connect what they have learned from the textbooks with the field practice. To deal with this problem, this study presents a context-aware ubiquitous learning system with sensing technology to detect and examine the real-world learning behaviours of students, such that personalised learning guidance and feedback can be provided; moreover, the students' experiences of operating those scientific devices, such as solar power equipment or the constellation simulators, can be conjunct to the knowledge learned from the textbooks. The experimental results from a science course of an elementary school show that this innovative approach is able to improve the learning achievements of students as well as enhance their learning motivation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".