Using Assessment to Promote Deep and Active Learning in an Online Anatomy Course
Bibliographic record
Abstract
The assessment plan we have in place has a greater influence over student learning than any other aspect of the curriculum. Through assessment, we are communicating to students what knowledge, skills, and habits of mind are essential and enduring, and what they should be working towards in our course; therefore we have to think carefully about what it is that we value as instructors and make sure we communicate that through our assessment plan. Indeed, students view grading as a kind of “currency indicating what teachers value” (Boud, 1990, p. 103). Assessment influences how students approach learning, how they engage with us as instructors, how they engage with other students, and the material itself. Students will approach the material differently if, for example, the assessment is a multiple‐choice exam, an essay, or group presentation. In this session, we will share with the participants how we have revised our assessment plan to ensure that it directly supports the intended learning outcomes for this course, promotes active learning, and encourages a deep approach to learning anatomy. To ensure a more systematic approach to the curriculum, there has to be alignment among the design, delivery, and assessment of learning: “When there is alignment between what we want, how we teach and how we asses, teaching is likely to be much more effective” (Biggs 2003). Our goal in teaching is to get students excited about anatomy, intrigued, and to want to know more. When students are engaged and find the content relevant, they are more likely to put effort into it and take a deep approach to learning. We will discuss in this session how we have implemented Jigsaw, Jeopardy, Scavenger Hunt and other assessment strategies in a first‐year online anatomy course to provide students with opportunities to demonstrate their learning, develop transferrable skills, and grow as individuals. Even though we capture student feedback throughout the course, we also sent a course evaluation to the students et the end of the term to specifically ask about the active learning components of anatomy. The project on evaluating this course has received ethics clearance, which means will be able to share that feedback with other educators at this conference along with some of the lessons learned and underlying principles to encourage deep learning in anatomy.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".