Developing lab activities for an introductory anatomy course: Reflections and recommendations from a student-faculty partnership
Bibliographic record
Abstract
Engaging students as partners in the development of course curricula can provide a range of educational and professional benefits to both students and faculty.It allows students to participate in the creation of new educational material, which can impact the learning of future students (Matthews et al., 2018).It can also develop mutual trust, respect, and understanding between faculty and students, with all parties appreciating the value of each member's unique viewpoint (Matthews et al., 2018).These benefits are fostered in such relationships, in part because students and faculty share the responsibility of contributing to the learning experience and addressing challenges related to the advancement of teaching and learning (Cook-Sather et al., 2014;Bonney, 2018;Spencer et al., 2021).Students and faculty who are involved in developing course content together are encouraged to engage in selfreflection to further their academic development (Pedrosa-de-Jesus et al., 2017).Self-reflection can also allow all partners engaged in curriculum design and delivery to critically evaluate their efforts and heighten their academic skills.It was with these sentiments foremost in our minds that we embarked upon an exciting students-as-partners experience, the primary objective of which was to design graded lab activities for a large first-year human functional anatomy course in a kinesiology program.Our group was comprised of kinesiology members (five undergraduate students, one graduate student, and one faculty member).Our initial task was to share ideas about what types of lab activities students would enjoy and find meaningful, as well as which labs would contribute to their learning experience.Prior to the development of the new activities, labs for this course consisted of question-and-answer periods, interactions with lab materials (e.g., models, skeletons), and discussions about course content.The graded labs created by our team included a variety of individual and small-group activities that could be delivered in both inperson and online environments.Once we decided on the types of lab activities to offer, the course instructor divided the team into subgroups of two partners each.Each subgroup was responsible for developing several labs, working independently and meeting as needed.The entire group met biweekly to share progress, ask questions, and provide feedback to each other.After developing the activities, all group members reflected in writing on their experiences, guided by questions posed by the faculty partner.The students and faculty partner reflected on developing laboratory activities for incoming students, collaborating together,
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".