Does Optional Online Quizzing Improve Student Satisfaction and Preparedness in an Undergraduate Elective Advanced Human Anatomy Course?
Bibliographic record
Abstract
Many methods of online quizzing have been used in the past to assess understanding, improve accessibility, and help improve student success, however it does require additional time and preparation on the part of the student, compared to completing only midterm and final exams. We have previously shown that completing online quizzes throughout the semester improves student success on course exams, however we have only anecdotal feedback about the student experience. The present study will examine student satisfaction in two offerings of an upper‐level elective advanced regional human anatomy course. In both offerings, students completed two online quizzes, each worth 10% of the final grade, and based on material from both lecture and online “dissection” labs (using human anatomy software). In one course, it was mandatory to complete both quizzes, however in the other course the quizzes were optional (with the option of moving the 10% to the midterm and final exam respectively for the two quizzes). Data will be presented from a student satisfaction survey with Likert‐style questions, through which we will assess student satisfaction with the usefulness and accessibility of the online quizzes to help guide future course design. Data will be compared between the two courses, as well as between the students in the optional course who chose not to complete quizzes, and those who do. We hypothesize that students will feel better prepared for midterm and final exams if they completed the quizzes, and that they will prefer the flexibility and accessibility of the optional quizzes, compared to the non‐optional quizzes. Support or Funding Information York University Faculty of Health Dean's Catalyst eLearning Grant This abstract is from the Experimental Biology 2019 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".