Learning Anatomy: Using the Blooming Anatomy Tool to determine how course delivery and duration affect the performance of anatomy students
Bibliographic record
Abstract
During the fall/winter intake (FW), the University of Western Ontario offers a 3rd year systemic human anatomy course (ANATCELL 3319) in face to face (F2F) and online sections. F/W F2F students attend a weekly 1‐hour cadaveric lab where they view and manipulate prosections while FW online students attend a weekly interactive video conference lab session where their teaching assistant display PowerPoint slides, anatomy software and other materials. Both sections share the same multi‐choice questions (MCQ). During the summer intake, the course is only given in the online section with the labs given twice a week. Our previous study, relating to a different academic year, showed that FW F2F and FW online students performed equally well on their final grades. At that time, online students had access to Netter's 3D Interactive Anatomy to study laboratory materials. In this current study, students were not given access to anatomical software, however, their teaching assistant used 3D4MEDICAL for instruction purposes. This study compared the MCQ scores between the FW sections and the summer intake taking into account the cognitive level of the questions as determined by the Blooming Anatomy Tool (BAT) developed by Thompson & O'Loughlan (2015). The BAT rubric consists of knowledge, comprehension, application and analysis levels. During FW, F2F (n= 156) and online (n= 197) students had MCQ (n= 300) classified according to the BAT as follows: 149 knowledge, 120 comprehension, 21 application and 10 analysis questions. The scores in BAT levels were compared between the two sections. F2F students scored significantly higher than FW online student in knowledge, comprehension and application as well as the total MCQ (p≤ 0.001). The lack of difference in analysis could be due to the low MCQ number in that category. When comparing the scores across BAT levels within each section, application did not differ from comprehension in F2F but was lower for FW online students (p= 0.026) suggesting that F2F students are better at solving application MCQ. It should be noted that the performance of students in their prior years was the same for both the FW F2F and online sections which suggests that the improved performance by the F2F students is related to the manner in which the laboratory material is delivered. When the scores of F/W online (n= 197) and summer online (n=41) students in common MCQ (n=222 with 108 knowledge, 94 comprehension, 14 application and 6 analysis) were compared, FW students scored higher than summer students in knowledge (p< 0.001) and comprehension (p=0.001) as well as the total MCQ (p< 0.001). It should be noted that the incoming grades were higher for FW than summer online students (p< 0.05), which may account for the difference outlined above. In summary, the results suggest that the performance of FW online students declined year over year when they were no longer provided with personal versions of the software used online (Netters 3D Interactive Anatomy). In addition, we found that learning anatomy F2F using prosections may increase the ability to solve application questions when compared to FW online labs. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".