STEAM Education with Gamification: A Bibliometric Analysis
Bibliographic record
Abstract
STEAM education with gamification, a method that integrates game elements into the learning process, has proven to be a powerful tool. It not only encompasses various forms of gamified education but also has the potential to captivate and inspire students, breathing life into the course material. This study aims to present a comprehensive summary of the research carried out in the domain of STEAM education, with a particular emphasis on gamification. The focus will be on analyzing studies published in the past seven years. A meticulous bibliometric analysis was conducted to investigate the patterns in the published literature on STEAM education with gamification from 2017 to 2023. The relevant documents were retrieved by using keywords related to steam and gamification in the title, abstract, and keywords of the documents. Thus, 34 documents were acquired from the Scopus database for bibliometric analysis. The review analyzes the rate of publication growth, identifies the papers with the highest number of citations, determines the primary sources of these articles, evaluates the productivity of authors, examines the leading countries contributing to the field, and identifies the prominent subject areas within the research domain. Thailand has the highest output level in terms of publications and citations, as inferred from the results of our analysis. The Ceur Workshop Proceedings are widely acknowledged as the foremost scholarly resource in their subject. We have identified the most important keywords related to gamified STEAM education by conducting keyword analysis. Factorial Analysis provides a visual summary of the complex relationships between various concepts related to educational technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.030 | 0.107 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".