STEAM Learning Environment on Gamification System to Promote Innovators: A Bibliometric Analysis and Systematic Review
Bibliographic record
Abstract
The STEAM learning environment with a gamification system has been found to promote innovators by enhancing creative thinking and innovation skills in students. It can engage and motivate students, making the course content come alive. The objective of this study is to provide an overview of the research conducted in the field of STEAM education with a focus on gamification, specifically examining studies published within the last six years. A bibliometric analysis and systematic reviews were performed to examine the trends in published literature on steam learning environment on gamification systems to promote innovators between the years 2018 and 2023. The retrieval of pertinent documents was conducted by employing keywords associated with “TITLE-ABS-KEY (“steam” AND “gamification” AND “innovation” AND “skill” AND “innovator” AND “learning environment” AND “ecosystem”)” in the title, abstract, and keywords of the documents. Consequently, a total of 5 documents were obtained from the Scopus database for the purpose of conducting bibliometric analysis and systematic review. The review examines the pattern of publication growth, identifies the papers with the highest citation counts, determines the primary sources of these articles, assesses the productivity of writers, analyzes the leading countries contributing to the field, and identifies the prominent subject areas within the research domain. Based on the results of our investigation, it can be concluded that Thailand exhibits the highest level of productivity in terms of publications and citations. Education and Information Technologies is widely recognized as the primary scholarly resource in its field. Through the co-occurrence of keywords analysis, we determined that the most significant keywords associated with steam learning environment on gamification systems to promote innovators are gamification, creative thinking, steam education, design thinking and digital learning ecosystem and so on. The computer science and social science domains have the highest number of published documents.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.025 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.139 | 0.122 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".