The Relationship between Successful Completion and Sequential Movement in Self-Paced Distance Courses
Bibliographic record
Abstract
A course design question for self-paced courses includes whether or not technological measures should be used in course design to force students to follow the sequence intended by the course author. This study examined learner behavior to understand whether the sequence of student assignment submissions in a self-paced distance course is related to successful completion of the course. The study included 543 students in 89 different general education courses at a private university in the United States during a two year period. Results indicate that students who completed at least one assignment or exam out of the intended sequence of instruction were more likely to complete the course. Results were consistent when replicating the analysis with subsets of the data by course characteristics such delivery type, content, course format, and course level; and student demographics such as ethnicity, gender, GPA, and class standing. Learner control and self-direction within online self-paced courses should be included in course design to increase the likelihood of learner successful completion. Additional research could benefit course design and student support to promote higher rates of completion in self-paced courses.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".