Assessing experiences with online educational videos: Converting multiple constructed responses to quantifiable data
Bibliographic record
Abstract
Online educational videos disseminated content for a university pre-service teacher internship program. Placed within an online course management system, the videos were accessed by 202 interns located in several Western U.S. states. To ascertain the interns’ subjective experiences of the online course and videos to assist in the evaluation of the course, the researcher analyzed qualitative survey data in the form of constructed response items using a new qualitative-to-quantitative protocol. Based on phenomenological and grounded theory methods, this protocol was designed to handle the large amount of subjective constructed responses, allowing the inductive understanding of the overall experiences of a common phenomenon. The responses provided critical information that is useful for instructional designers, online educators, and educational video producers. The data suggest: 1) Universities must carefully consider video hosting options to ensure access. 2) Online videos should be carefully planned to create high quality, concise videos of less than ten minutes in length, yet contain enough educational content to reduce the overall number of required videos. 3) Students appreciate the flexibility online course delivery offered in terms of scheduling and eliminating the need to come to campus to attend seminars. 4) Self-paced online courses require progress indicators to alleviate confusion.
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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 | 0.056 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".