Learning With Dogs: An Exploration of Student Outcomes from a Canine-Assisted Experiential Learning Intervention
Bibliographic record
Abstract
Pedagogical researchers are increasingly investigating the impact of experiential learning models, which include hands-on or immersive engagement with content, on student outcomes, as compared to traditional transmission-based learning models. Many previous studies focused on academic competencies and learning outcomes, but the present research investigated non-academic positive outcomes such as self-awareness and wellbeing. Self-awareness and meta-learning can be explained by neural reward pathways that govern motivation; active engagement with activities leads to deeper cognitive processing and higher motivation. Thus, students that are actively engaged and interested in their own learning processes tend to have better learning outcomes. Wellbeing is studied less in this context but is preserved and enhanced through this model of learning. This may be in part because experiential activities often allow for increased peer relationships, which are known to act as a buffer against stress. Combining the literature on experiential learning with the literature on canine-assisted learning provides a particularly interesting area of investigation, as several researchers have attempted to integrate the power of experiential learning with the well-known health and wellbeing benefits derived from interacting with dogs. Students reported increased confidence, wellbeing, and literacy skills after engaging with canine-assisted learning programs. Data from a novel case study conducted at Bader College in 2022-23 supported the link between experiential learning and wellbeing. In this case study, ten students took part in a dog training program over the course of the academic year. The students submitted weekly learning reflections, which highlighted the benefits they derived from the experience in relation to wellbeing, deeper cognitive processing, and meta-learning. The results of this case study suggest the need to take a more holistic approach to student outcomes when contrasting experiential learning models with traditional learning models. The study also raised awareness of the benefits of learning activities involving dogs.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".