Engaging Psychology Students at a Distance: Reflections on Australian and Canadian Experiences
Bibliographic record
Abstract
Engagement enhances learning, particularly for abstract and theoretical concepts. This article is an instructor reflection on student engagement with a case example of mobile learning for two differing senior undergraduate psychology courses, Theories of Counselling and Psychotherapy, and Ethics and Current Issues in Psychology. The instructor was experienced and the students were Canadian or Australian, respectively. The courses compared were delivered through an asynchronous online-enhanced distance model for a Canadian university and through a blended learning model for an Australian university. Issues with student engagement are explored through a review of informal and formal student feedback and instructor reflection. Although motivational instruction was a consistent factor in the course and instructional evaluations, this case example highlights the elusive nature of student engagement given the multiple factors involved in student expectations and needs and differing models of delivery for these undergraduate psychology courses. The author is left acknowledging only that different learning opportunities benefit the range of psychology students who may engage in them.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.038 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".