Assessing Student Performance Between Face-to-Face and Online Course Formats in a College-Level Communications Course
Bibliographic record
Abstract
This observational study adds to a small number of college-specific studies comparing student performance in online and face-to-face versions of the same course. It also complements more large-scale college-based studies that compare the delivery formats across courses, disciplines, and institutions. Using descriptive statistics and the chi-square and ANOVA methods, the author examined comparative educational outcomes by measuring student performance and key factors of student performance in the same mandatory professional communications course taught simultaneously in an online and face-to-face format over a 5-semester time frame. The findings are consistent with other comparative studies that have established that in comparison to face-to-face students, online students are generally more academically prepared; more mature; and more commonly full-time employed, fluent in the English language, and female. Similar to other studies, the factors of gender, age, education, and writing proficiency are significant indicators of student achievement; the factors of employment hours, native language, and direct/indirect entry are not, which shows some discrepancy with other studies. In terms of overall student performance, online and face-to-face-component students earned similar grades and had similar completion and retention rates. This finding does not concur with a number of studies that show that online students are significantly less likely to successfully complete courses than their face-to-face counterparts. Course type (mandatory, elective, remedial, regular), advancement in a course of study (lower year, upper year), and delivery mode choice (fully online vs. mix of online and face-to-face) are probed as explanatory variables for differences in findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".