When You Come to a Fork in the Road, Take It: Teaching Social Work Practice Using Blended Learning
Bibliographic record
Abstract
The debates surrounding the effectiveness of teaching social work online highlight the challenges of adequately preparing students for face-to-face practice by way of web-based technologies. The purpose of this paper is twofold. Firstly, to briefly describe how a particular School of Social Work when designing its part-time undergraduate degree program (BSW), arrived at a fork in the road and instead of choosing between the paths of in-class or online course delivery, the School decided to offer the entire degree using a blended learning platform. Secondly, to compare the development and implementation of three specific practice courses within the part-time degree program (interviewing and assessment, social work theory, and a practicum integration seminar) each of which was offered using blended learning. This paper contributes to the debate about the value of using web-based components when teaching social work practice and will be helpful to educators from within many disciplines, who are wishing to critique their own development processes when designing and teaching practice courses using blended learning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".