A context-aware knowledge map to support ubiquitous learning activities for a u-Botanical museum
Bibliographic record
Abstract
<p>Recent developments in mobile and wireless communication technologies have played a vital role in building the u-learning environment that now combines both real-world and digital learning resources. However, learners still require assistance to control real objects and manage the abundance of available materials; otherwise, their mental workload could become so high that learning becomes less effective. The learner is the priority in every learning situation and is, therefore, a crucial factor in executing u-learning. This study presents a u-learning system that integrates context awareness and ontological technology to design a context-aware knowledge map (CAKM) to improve learning efficiency. A case study of an Orchid Island botanical ecosystem course was conducted in classrooms and at the Botanical Garden of National Museum of Natural Science in Taiwan. Participants were university teachers and students. A questionnaire based on the technology acceptance model (TAM) theory was designed and used to measure the willingness for adoption or usage of the proposed system. The results demonstrate that this innovative approach can enhance learning intention. The results also indicate that this CAKM not only substantially improves the effectiveness of subject learning but also enhances the usability of u-learning systems in the museum environment.</p><p> </p>
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".