An Empirical Investigation of the Impacts of Web-Based Distance Education: Evidence for Justice Studies
Bibliographic record
Abstract
During the past decade, web-based distance education has created a profound impact on education and learning. It has increased access and expanded educational opportunities of many students. The National Center for Educational Statistics reported that over 50% of post-secondary institutions now offer a number of web-based courses. Peterson’s Guide to Distance Learning programs reported that over 75 programs in criminal justice or criminology. Although increasing number of courses are being offered through the web-based distance modality, it is however important to determine the perceptions of students to its use. The study therefore attempted to determine the perceptions on web-based distance education by students in the justice studies department at a Historically Black University. The Chi Square and correlational analysis revealed that age-group, gender, year in school and study time were statistically significant. A binomial regression with student’s major as the outcome was most robust with an R-Square of 0.522. Gender, study time and year in university were statistically significant at the .05 level and having done a web-based distance course. There was therefore a statistically significant variation in the perceptions of the students in justice studies towards web-based distance education.
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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 | 0.015 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".