Scientometric Analysis of Personalized Learning Research
Bibliographic record
Abstract
This study presents a scientometric analysis of personalized learning research from 2020 to 2024. Using the Lens database, 4,463 scholarly articles were analyzed to identify key trends and patterns in this rapidly evolving field. The analysis revealed consistent publication growth over the 5 years, with journal articles dominating as the primary publication type. Hessian Normal University emerged as the most productive institution, while Jackson Steinher was identified as the most prolific author. The United States and China were the leading countries in terms of research output. Education and Information Technologies was the top journal publishing personalized learning research. Co-authorship network analysis highlighted collaborative patterns among researchers, while keyword co-occurrence networks revealed the centrality of artificial intelligence and related concepts in the field. Citation analysis identified influential documents and sources shaping the discourse. The findings suggest an increasing focus on integrating AI and machine learning into personalized learning systems and a growing emphasis on interdisciplinary approaches. This scientometric overview provides valuable insights into the current state and emerging trends in personalized learning research, which may inform future studies and applications in this domain.
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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.140 | 0.188 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".