Academic achievement is a multifaceted outcome influenced by a multitude of factors spanning across educational, socioeconomic, and individual characteristics. Understanding the key determinants of students’ academic performance is paramount for educato
天池实验室 数据集 公共数据集 正文 students-academic-performance-dataset 拌面加辣2019-05-09115058CC-BY-SA-NC 4.0 新建Notebook 内容 Notebook 评论 描述 学生成绩预测 数据列表 数据名称上传日期大小下载 xAPI-Edu-Data.csv2019-05-0937.13KB 文档 目录...
Furthermore, the base competencies of first-year students have been a subject of investigation (cf. Krumrei-Mancuso et al.2013; Zehetmeier et al.2014), which provides valuable insights into the academic skills of young people that contemporaneously enter the university system. Whereas some studies...
High dropout rates and poor academic performance among students are examples of the most common issues that affect the reputation of an educational institution. Students’ academic records can be analyzed to explore the factors behind these phenomena. This paper discusses the building of a model to ...
We apply these methods to a panel dataset from South Korea, the Korea Education Longitudinal Study. The results show that the true effect of private tutoring remains, at most, modest. Instrumental variables (first-difference) estimates suggest that a 10-percent increase in expenditure raises a ...
A booster ensemble technique is applied to the transformed dataset to further fine-tune it. The predictive model is trained and tested by using k-fold cross validation on the training and testing data using the above five ML supervised algorithm iteratively. In the final step, performance metrics...
To make schoolwork better, it is thus crucial to gain insight from data to improve academic performance and help students who are deaf and hard of hearing move forward. A collected benchmark dataset in this study was analyzed, which contains information for more than 700k records of deaf and...
This study investigated the growth trajectory of academic achievement in Math and English among 519 students in a vocational senior high school in Taiwan. Covering the complete individual learning profile, our dataset included pre-enrollment variables, periodic test scores, and college entrance examination...
A booster ensemble technique is applied to the transformed dataset to further fine-tune it. The predictive model is trained and tested by using k-fold cross validation on the training and testing data using the above five ML supervised algorithm iteratively. In the final step, performance metrics...
(2004) to analyze select aspects of the AEQ test emotions scales, but made use of the entire dataset in analyzing all 24 scales of the instrument. The original version of the AEQ measuring students’ habitual achievement emotions experienced across academic achievement situations was used, implying...