通过机器学习和空间分析识别的大学生和毕业生的研究素养及其预测因素
Mohammed A Mamun1,2,3, Md Abu Huraira4,5, Momotaj Begum4,6
1CHINTA Research Bangladesh, Savar, 1342, Dhaka, Bangladesh. mamun@thechinta.org.
Scientific reports
|October 13, 2025
概括
超过一半的研究人员表现出不良的研究素养,影响学术出版. 提高研究培训和意识对于学术诚信和卓越至关重要.
科学领域:
- 学术出版学术出版公司
- 研究诚信研究诚信
- 学术传播学术交流
背景情况:
- "出版或灭亡"的文化驱使研究人员转向掠夺性的期刊.
- 研究了对掠夺性期刊的认识,但整体研究素养未得到充分探索.
- 这项研究通过全面评估研究素养来弥补这一差距.
研究的目的:
- 通过使用先进的分析技术,全面评估研究素养.
- 确定影响研究人员研究素养的因素.
- 探索研究素养的区域差异.
主要方法:
- 采用详细问卷进行的横截面调查.
- 传统的统计分析 (SPSS),机器学习 (Python,Google Colab) 和GIS映射 (R).
- 监督的分类算法 (随机森林,CatBoost) 和空间分析.
主要成果:
- 超过50%的参与者表现出不良的研究素养.
- 高度素养的关键预测因素包括对大学研究课程的满意度,外部研究培训和专业参与.
- 机器学习确定外部研究课程是最具影响力的因素;随机森林和CatBoost模型显示高精度 (73.04%和71.57%).
- 地理信息系统分析表明,在研究素养方面存在区域差异.
结论:
- 很大一部分研究人员缺乏足够的研究素养.
- 需要干预计划来改善研究实践和意识.
- 促进学术卓越需要有针对性的教育和专业发展举措.
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