Using the Critical Incident Questionnaire as a Formative Evaluation Tool to Inform Online Course Design: A Qualitative Study
Bibliographic record
Abstract
The online instructor plays a prominent role in influencing how students respond to an online course, from designing the course structure, course activities, and assignments to encouraging interaction. Therefore, to develop effective online courses, instructors need robust feedback on their design strategies. Student evaluation of teaching (SET) functions as a summative evaluation of the course design and delivery. Yet, the feedback from SETs can only be integrated into the next iteration of the course, thereby failing to benefit the students who provide the feedback. One suggestion is to use midsemester formative evaluation to inform course design in real time. A qualitative research study was conducted to explore whether the Critical Incident Questionnaire (CIQ) could be an effective formative evaluative tool to inform real-time online course design and delivery. Thematic analysis was conducted on the midcourse evaluations obtained from 70 students in six fully online master’s level courses. There are three key findings from this study. First, CIQ use can provide opportunities for real-time adjustments to online course design and inform future redesign of online courses. Second, responses received via the CIQ prioritize the student voice and experience by focusing on factors that are critical to them. Finally, this deep-dive analysis reinforces the enduring factors that contribute to effective online course design and delivery. A recommendation for practice is to use the CIQ as an effective tool to gather formative feedback from students. This feedback can then be used to adjust course design as needed.
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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 | 0.083 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".