A review of trends in distance education scholarship at research universities in North America, 1998-2007
Bibliographic record
Abstract
This article explores and summarizes trends in research and scholarship over the last decade (i.e., 1998-2007) for students completing dissertations and theses in the area of distance education. The topics addressed, research designs utilized, and data collection and analysis methods used were compiled and analyzed. Results from this study indicate that most of the distance education research conducted by graduate students in this period of time has been descriptive, often addressing the perceptions, concerns, and satisfaction levels of various stakeholders with a particular distance education experience. Studies of this type typically used self-report surveys and analyzed the data using descriptive statistics. Validating the concern of many distance education scholars, there was a lack of graduate student research aimed at developing a theory base in distance education. On a positive note, projects directly comparing distance education with traditional face-to-face classrooms to determine the merit of specific programs declined significantly in 2007 as compared to 1998. This result might indicate that distance learning is becoming accepted as a viable and important educational experience in its own right. Another encouraging finding was the decreased emphasis on studies focused on technology issues, such as those analyzing the quality of distance education technology and questioning educators’ ability to provide an acceptable technology-enabled distance learning experience.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".