The Effect of Teaching Methodology and Course Duration on Student Performance at Different Assessment Types across Different Cognitive Levels as defined by Bloom’s Taxonomy
Bibliographic record
Abstract
The University of Western Ontario currently offers a third year systemic human anatomy course (ANATCELL 3319) in face to face (F2F) and online sections during the fall/winter intake (F/W). While F/W sections share the same lecture and assessment materials, F2F students attend weekly one‐hour cadaveric labs while online students attend weekly interactive video conference labs. During the summer intake, the course is offered in the online section only, with labs given twice a week. The assessments consist of multichoice (MCQ) and short answer questions. Our previous publication showed that F/W F2F scored higher in their MCQ assessments than F/W online students at different Bloom’s Taxonomy levels. In addition, F/W online students scored higher than summer online students in the shared MCQ. In the current study, we further compared the performance of students in MCQ against their performance in short answer questions, in total and at different Bloom’s Taxonomy levels within/against the assessment type(s). For F/W sections (F2F students n=142; online students n=172), there were 300 MCQ (knowledge (n= 149), comprehension (n= 120), application (n= 21), and analysis (n= 10)) and 118 short answer questions (knowledge (n= 88), comprehension (n= 18.5), application (n= 10.5), and analysis (n= 1)). Both groups scored higher in their MCQ than short answer questions, in total and at most cognitive levels (p ≤ 0.05). F/W F2F students scored higher than F/W online in both assessment types and at most levels. The variance across levels differed based on the assessment type. For example, All F/W students scored the highest in comprehension MCQ but the lowest in short answer comprehension questions. This indicates that a change of assessment type influences the way in which students answer. With summer online students (n=44), we compared 169 MCQ (knowledge (n=79), comprehension (n=74), application (n=9) and analysis (n=7)) with 59 short answer questions (knowledge (n= 34), comprehension (n=12), application (10) and analysis (n=3)). Students scored higher (p ≤ 0.05) in their MCQ total, comprehension MCQ, and application MCQ than the adjacent levels of the short answers. Unlike their F/W counterparts, students scored lowest in their MCQ comprehension (p<0.001) in relation to the other levels. Our findings indicate that teaching F2F is likely to improve student performance in terms of both overall grades and cognitive ability. In addition, students are likely to score higher in MCQ than short answer questions which is indicative of a reliance on cues within MCQ (especially comprehension) to help them overcome their lack of information. Finally, the performance of summer students, particularly in relation to comprehension‐type questions which rely on memorisation, indicates that the spacing of course material may help in achieving higher grades. Support or Funding Information Dr. Kem Roger
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".