An Analysis of the Journey of Open and Distance Education: Major Concepts and Cutoff Points in Research Trends
Bibliographic record
Abstract
In an effort to understand trends in open and distance education more comprehensively, this study aims to identify the research trends, major concepts, and cutoff points in the articles published between 2009 and 2016. From five major peer-reviewed journals, a total of 989 articles were analyzed through a systematic literature review process using content analysis. The articles were coded based on the following three categories: level, topics, and sub-topics. The results indicated the followings: (1) emerged main themes in the articles were foundations of open and distance education, instructional process, and effects of applications; (2) there was an upward growth in the publishing of the articles on massive open online courses, open educational resources, and students’ perspectives; (3) new pedagogical approaches and online learning design played a triggering role in the research topics; and (4) technological and pedagogical developments between 2011 and 2012 had an influence on the tendency of the articles. In addition, we explored cutoff points so that they may provide insights and valuable hints for researchers to design new studies in open and distance education field. Discussions about the gaps in the state-of-the-art trends and directions about future research were also included.
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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 | 0.042 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.067 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".