Scholarlink

Using RStudio’s Bibliometrix software and the PRISMA methodology, a systematic literature review (SLR) and a scientific mapping of the scientific productions of the Scopus database on the prediction of academic success in Higher Education Institutions were developed. The search was narrowed down using the ERIC thesaurus or its approximations and Boolean operators. The main questions related to the meta-analysis (MA) of the data on the located scientific productions were set as follows: 1. In which years were the most publications on the topic published? 2. What are the journals to which the papers belong? 3. From which nations are the research carried out? 4. What is the type of paper being dealt with? 5. In what areas is the research being conducted? 6. What is the predominant language? 7. Key words that relate to research? 8. According to the number of citations, which papers are most relevant? The analysis found that scientific productions in English are the most common in the subject, and the highest number of publications (27.40%) was recorded in 2023, and 2024 promises to exceed this figure. SLR researched to offer a comprehensive and objective view of the available studies of Data Mining (DM) and its contribution of techniques to determine university academic performance like Educational Data Mining (EDM). Finally, Random Forest (RF), Nearest Neighbors (NN), Support Vector Machines (SVM), Decision Trees, Logistic Regression, Closed Recurrent Neural Network (GRU), Naive Bayes and XGBoost are the most important EDM techniques.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bibliometric Mapping of Scientific Literature Located in Scopus on Predicting Academic Success with Educational Data Mining

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • María Isabel Uvidia-Fassler,
  • Heidy Madeline Remache-Remache,
  • Pablo Martí Méndez-Naranjo,
  • Andrés Santiago Cisneros-Barahona,
  • Daniel Antonio Chuquin-Vasco

摘要

Using RStudio’s Bibliometrix software and the PRISMA methodology, a systematic literature review (SLR) and a scientific mapping of the scientific productions of the Scopus database on the prediction of academic success in Higher Education Institutions were developed. The search was narrowed down using the ERIC thesaurus or its approximations and Boolean operators. The main questions related to the meta-analysis (MA) of the data on the located scientific productions were set as follows: 1. In which years were the most publications on the topic published? 2. What are the journals to which the papers belong? 3. From which nations are the research carried out? 4. What is the type of paper being dealt with? 5. In what areas is the research being conducted? 6. What is the predominant language? 7. Key words that relate to research? 8. According to the number of citations, which papers are most relevant? The analysis found that scientific productions in English are the most common in the subject, and the highest number of publications (27.40%) was recorded in 2023, and 2024 promises to exceed this figure. SLR researched to offer a comprehensive and objective view of the available studies of Data Mining (DM) and its contribution of techniques to determine university academic performance like Educational Data Mining (EDM). Finally, Random Forest (RF), Nearest Neighbors (NN), Support Vector Machines (SVM), Decision Trees, Logistic Regression, Closed Recurrent Neural Network (GRU), Naive Bayes and XGBoost are the most important EDM techniques.