Comparison of course completion and student performance through online and traditional courses
Bibliographic record
Abstract
<p class="MsoNormal" style="margin: 0in 0in 0pt; line-height: 200%;"><span style="font-size: small;"><span style="font-family: Times New Roman;">Enrollment in online courses has outpaced overall university enrollment for the past several years. The growth of online courses does not appear to be slowing. The purpose of this study was to compare course completion and student academic performance between online and traditional courses. Archival data from the host university student records system was collected using the Structured Query Language. Descriptive statistics were used to analyze student characteristics. Chi-square analysis was used to determine if statistically significant differences existed between students enrolled in online and traditional courses when comparing course completion and academic performance. Analysis found statistically significant differences existed in both course completion and academic performance for students enrolled in online versus traditional courses. Additional analysis indicated statistically significant differences existed in course completion by course discipline. </span></span></p>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".