Virtual Mystery Webtool: Collaborative Critical Thinking with Online Hybridised Problem-Based Learning
Bibliographic record
Abstract
Integration of digital technologies in post-secondary curricula has increased since its widespread implementation during the COVID-19 pandemic (Strielkowski, 2020; Sukula et al., 2020). The Virtual Mystery (VM) webtool is an online asynchronous hybridised problem-based learning webtool designed to provide cost-effective small group collaborations for large in-person courses. The practical and unique nature of each mystery promotes collaborative critical thinking and self-directed learning. The VM webtool facilitates experiential learning and has demonstrated success in large introductory Anthropology courses (Fukuzawa et al., 2021). In this study, we examine the effectiveness of the VM webtool in a variety of different course disciplines with smaller class sizes in both online and in-person course modalities. Quantitative data of student rankings on five-point Likert scale and qualitative responses from open-ended questions were collected in post-course surveys across four undergraduate courses which included Biological Anthropology, Archaeology, Psychology, and Forensic Toxicology. The results demonstrate positive responses for the content and problem-based learning application of the VM webtool across disciplines and course levels. Student responses highlighted the need for updated technological solutions to enhance student communication in the VM webtool, leading to a larger discussion on challenges of sustainability with online applications in the digital age.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".