Exploring Differentiated Learning: A Bibliometric Examination of Research Development and Directions
Bibliographic record
Abstract
Purpose: To chart the progress and trajectory of differentiated learning research in Scopus database-indexed publications from 1954 - 2023. The first publication was found in 1994. Methodology: Using bibliometric analysis method. A literature review was performed to ensure that relevant research on bibliometric topics was conducted. A Boolean search engine was utilized to search the Scopus database from 1954 to 2023. The analysis of citations, document content, and networks was carried out using R and Rstudio tools, VosViewer, and Microsoft Excel. Using the Boolean operator TITLE-ABS-KEY (differentiated and learning) to search Scopus and generate 7,012 documents. Filtering is done with the Boolean operator (LIMIT-TO (SUBJAREA, "soci")) AND (LIMIT TO (DOCTYPE, "ar")) AND (LIMIT-TO (SRCTYPE, "j")) AND (LIMIT TO (LANGUAGE, "English")) for social science field restrictions, source documents are only journals and articles in English, resulting in 1. 425. analysis using Scopus, R, and Rstudio to determine the number of articles per year, per journal, author, affiliation, country, and topic/sector. To visualize using VOSviewer and Microsoft Excel data processing. Results: 848 publications matched the function, subject, and criteria set. Annual growth rate of 7.33%, most publications in 2022 by US country with affiliation from University of Toronto US. Jordan J was the most prolific author. Applications/Originality/Value: This bibliometric analysis is limited to Scopus data. Other national and international databases should have been considered in this study. This research offers a concise summary of the available literature for researchers engaged in differentiated learning and offers suggestions for future research endeavors.
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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 | 0.029 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.129 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".