STUDENT SUBMISSION PATTERNS IN ONLINE HOMEWORK AND RELATIONSHIPS TO LEARNING OUTCOMES: A PILOT STUDY
Bibliographic record
Abstract
Online homework systems are being increasingly used for auto-graded, instant feedback homework and practice for students in math, science and engineering. Students may use these systems, which often allow multiple or unlimited tries, in ways that are different from completing traditional paper-based homework, however research relating online homework system patterns of usage and learning outcomes is limited. This study explores online homework submission patterns and their links to student learning outcomes (weighted individual grades) by analyzing the submission patterns of two second-year engineering courses (~130 students each) from our institution over the 2017-2018 academic year using WeBWorK, an open online homework platform. 
 Students in each of the two courses were clustered into three groups using a K-means algorithm based on when during the homework period they tended to submit attempts. Clusters were used to approximately represent a submission pattern, meaning groups of students that submit attempts mostly early, mostly late, or more evenly over the period. Conducting one-way ANOVAs for each course, we found that there is a significant difference between clusters (submission patterns) in terms of mean individual weighted grades on tests and exams (p < 1.07e-08, p < 2.68e-5). Post-hoc analyses revealed that the best performing cluster (students who submit attempts mostly early) had a mean tests/exams grades that were about 10% higher than worst performing cluster (students who submit attempts mostly late) (p < 2.6e-06, p < 9.9e-05).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".