Bibliometric Analysis of Academic Studies on Student Support Systems in Open and Distance Learning
Bibliographic record
Abstract
The aim of this research is to conduct a bibliometric analysis of educational studies utilising Student Support Systems (SSS) in Open and Distance Learning, as indexed and scanned in the Web of Science (WoS) database under SCI-Expanded, SSCI, and A&HCI. The study employs descriptive and bibliometric analysis methods including journal articles exported as a .txt file from the WoS database. The data (6.366 open-access articles between 2013 and 2023) have been mapped using the VOSviewer. The research findings indicate that the majority of educational studies involving SSS in Open and Distance Learning are constituted by articles, with the number of publications beginning to rise from 2017 and the highest number of publications occurring in 2021. The authors receiving the most shared citations in this field have been identified as Dragan Gasevic, Sanna Jarvela, Abelardo Pardo and Jonna Malmberg respectively. The countries producing the most publications related to Student Support Systems in Open and Distance Learning are, in order, the USA, Australia, the People's Republic of China, Taiwan, Spain, Canada, and Turkey. From the perspective of country citation analysis, the USA has received the most citations and the institutions receiving the most citations are the University of Hong Kong in Hong Kong, Monash University in Australia, Beijing Normal University in the People's Republic of China, and the National University of Science and Technology in Taiwan. Finally, the most frequently used keywords in published studies have been found to be, in order, higher education, e-learning, online learning, and learning analytics.
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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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.178 | 0.237 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".