Seeing is Knowing: Instructor Perceptions of Teaching an Online Undergraduate Anatomy Course with Laboratory
Bibliographic record
Abstract
Online versions of traditional face‐to‐face (F2F) courses at post‐secondary institutions have become prevalent due to increased enrollment in undergraduate programs. While many studies assess the impact of online delivery formats on student outcomes, fewer examine how teaching online affects instructors. An online section of a traditional F2F undergraduate anatomy course with a prosection laboratory commenced in 2012–13 at Western University Canada. Lectures for F2F students were broadcast in both live and archived format to online students using Blackboard Collaborate virtual classroom software (BBC). Laboratories were delivered online by a teaching assistant (TA) who manipulated 3‐dimensional (3D) anatomical computer models in BBC. The objectives of this study were to reveal instructor perceptions of teaching online and suggest modifications for future years. Online instructors (4 professors; 5 TAs) who taught during the first two course offerings participated in individual open‐ended interviews. Emergent themes were generated from a content analysis of interview transcripts (2 independent coders; Fleiss’ kappa = 0.82). The major emergent theme encompassed issues pertaining to teaching without seeing the learner. The instructors were unable to use non‐verbal student feedback (e.g body language) during a lesson to assess engagement (N = 8) and understanding (N = 4). The instructors identified additional methods of communication (email, instant messaging and drawing in BBC) to be sufficient when initiated by students (N = 6). The impact of BBC use on teaching practices was also discussed. Preparation time for teaching online was longer than for F2F, although the difference was not substantial (N=7). Though BBC was reliable and easy to use for lecture broadcasts, all professors noted that it impeded their mobility in the lecture hall, thus it was perceived that interaction with the F2F students decreased. In the online laboratory, live desktop sharing of 3D models in BBC was not possible due to the quality of students’ internet connection speeds; however, the majority of TAs (N = 3) found pedagogical value in the students drawing on screen captures of 3D models to communicate understanding. In the future, mandatory synchronous participation through instant messaging may improve feedback to instructors on student engagement and understanding, though it might not be feasible due to schedule conflicts. It is recommended that online instructors organize frequent formative student assessments to obtain feedback on engagement and understanding.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".