The Trends of Differentiated Instruction Research: Bibliometric Analysis Spanning 1961–2023
Bibliographic record
Abstract
In educational research, differentiated instruction, or DI, is a popular subject. Staying up to date with its most recent advancements and frontiers open up new research avenues. This article analyzes developments and trends in differentiated learning using bibliometric analysis between 1961 and 2023. This research focuses on publications from 1961 to 2023, frequently cited keywords, authors who most frequently publish about DI, most frequently cited authors, journals that publish the most, countries that publish the most on DI topics. In the bibliometric analysis, a total of 842 articles were obtained, taken from the Scopus database. The findings indicated that : (1) 2021 marks the pinnacle of publication with 82 papers,(2) Differentiated Instruction, student, teacher, learning, e-learning have been the most popular search terms, (3) Davies et al. (2013), Valli and Buese (2007), Zhu Z (2016), Subban (2006), Reis et al (2011) these have been the papers that have been quoted the most, (4) Katrien Struyven, Marcela Pozas, Letzel, V author with the most number of publication, (5) International Journal of Inclusive Education, Teaching And Teacher Education, ASEE Annual Conference And Exposition Conference Proceedings are among the best journals, (6) Vrije Universiteit Brussel, Universiteit Gent, University of Virginia have been the leading universities, and (7) US, Belgium and Canada have been the leading nations in this sector. This paper is a valuable addition to the subject matter and gives a thorough summary, the scientific environment, and the subject’s future directions.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.082 | 0.163 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".