Differentiated instruction in science education: A thematic evolution through bibliometrics
Bibliographic record
Abstract
This study is a literature review that aims to identify thematic areas, key trends, key contributors, and contributions to the field of research by addressing the topic of differentiated instruction (DI) in science education. This literature review uses the Scopus database from 1964-2024 with bibliometric analysis on the biblioshiny application to obtain research results in the form of graphical and image data visualizations. Since 2010, this topic has increased according to the visualization results in term of the number of publications and continued to be of interest and spotlight in the 21st century. The central theme of the thematic evolution analysis shows the integration of DI with STEM, inquiry-based learning, and teacher professional development. Inclusive learning, active learning, and equity, become supporting themes in this study. These findings show the correlation between DI and the 21st century education. Leading authors and institutions have made significant contributions to advanced research in this field. The metadata of this research shows there are three highest ranks of countries contributors to this DI in science education; US, Indonesia, and Canada. Blonder and Decoito have been key contributors in this study. The conclusion of this study is that the trend of differentiated instruction (DI) in science education can provide space for creating active and diverse learning according to individual needs, thus supporting inclusive learning. The integration of DI with innovative learnings such as inquiry and STEM are recommended for further research. This study can be used as a basis for further research on the effectiveness of the globally and holistically approaching differentiation, especially within low resource contexs.
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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.195 | 0.223 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".