A Bibliometric Analysis on How Organic Chemistry Education Research Has Evolved Collaboratively Over Time
Bibliographic record
Abstract
Organic chemistry is widely regarded as a challenging topic; generally, students prefer to memorize rather than critically analyze concepts resulting in meaningful learning. In recent years, the curriculum of the organic chemistry subject has been reshaped and redefined to overcome the difficulties that students often experience while trying to understand the syllabus. The goal of this research is to illustrate the organic chemistry education’s current trends, which adopted the bibliometric analysis method. A holistic review was carried out on organic chemistry education articles obtained from the Scopus database between the year 2011 up to 2020. Based on the keywords of “organic chemistry” and “education”, the study has accumulated 1056 papers for further evaluation. Various tools have been implemented, for example, Microsoft Excel was used to conduct the frequency analysis, VOSviewer for data visualization, as well as Harzing’s Publish or Perish in regard to citation metrics and analysis. Bibliometric indicators were employed to report the findings in this study, for instance, language, subject area, research trends by year of publication, top countries, top influential institution, active source title, citation analysis, authorship analysis and keywords analysis. The results show an increasing growth rate of literature on organic chemistry education from 2011 until 2020. The United States was the top contributor to organic chemistry education research, followed by Canada. Healthy collaboration exists across researchers, countries, and institutions. This evolvement of organic chemistry education reflects a rising emphasis on Science, Technology, Engineering and Mathematics (STEM) discipline incorporated into the 21st-century curriculum to prepare the desired workforce.
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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 | 0.020 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.156 | 0.234 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".