Simulation innovation in cyberspace: A collaborative approach to teaching and learning in child and youth care education
Bibliographic record
Abstract
<p>Leveraging digital technology for practice innovation is a compelling challenge. Limited education and training prevent human service practitioners from incorporating technology into practice. Progress in this area will be achieved when significant changes to pedagogy support technology integration with teaching/learning partnerships in higher education. With the recent attention to relational Child and Youth Care (CYC) practice in cyberspace (Martin & Stuart, 2011), this paper aims to highlight student/teacher explorations in this emerging area of clinical practice using student-driven simulated online counselling sessions supervised by the course instructor. Beyond critical learning within the roleplay activities, students engaged in solving disruptions to simulations, which can enhance their future agility in real practice situations (Rooney, Hopwood, Boud, & Kelly, 2015). Foundations in the Scholarship of Teaching and Learning (SoTL), experiential learning theory (ELT), and learner-led (LED) approaches guided student engagement with technology and reflexive practice in this graduate level classroom.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".