1、数据名称:学生学业表现(Student Academics Performance Data Set) 2、数据来源:Dr Sadiq Hussain, Dibrugarh University, Dibrugarh, Assam, India 3、时间跨度:截至2018-09-16 4、区域范围: 5、数据大小:16KB 6、数据格式:arff 7、数据简介:该数据集试图找到基于不同社会、经济和学术属性的学期末百分比预测。学...
YuliaInternational Conference on Computer, Communication and Computational SciencesL.W. Santoso and Yulia, "The analysis of student performance using data mining," In the Proceedings of 3rd International Conference on Computer, Communication and Computational Sciences (IC4S), 2018....
Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated ...
Student performance, student progress and student potential are critical for measuring learning results, selecting learning materials and learning activities. However, existing work doesn't provide enough analysis tools to analyze how students performed, which factors would affect their performance, in which...
Summarization: refers to the process of summarizing the incoming stream of data or further analysis by transforming raw data into information. For example, we are typically more concerned with the mean and standard deviation, the minimum and maximum, etc. of a set of student’s metrics rather ...
In this paper, we present a new approach for training set selection in large size data sets. The algorithm consists on the combination of stratification an... JR Cano,F Herrera,M Lozano - Springer Berlin Heidelberg 被引量: 26发表: 2005年 Performance analysis in soccer: a Cartesian coordinates...
The main goal of educational data mining is to improve the student performance. The usage of data mining techniques which achieves the goal in an efficient... KS Priya,AVS Kumar - 《International Journal of Advanced Networking & Applications》 被引量: 24发表: 2013年 A comparative analysis of ...
The thesis comes to the same conclusion as the earlier studies: The results show that it is possible to predict student performance successfully by using machine learning. The best algorithm was naïve Bayes classification for the first data set, with 98 percent accuracy, and decision trees for...
In this study, students in the IG were asked to evaluate their own performance using the APP before their mid-term assessment meeting. This may have helped them identify their strengths and weaknesses and to set goals for improvement. They then met with their CE to discuss their self-...
Overall, the student performance on the AR activity presented in this work shows promising potential to replicate some of the learning gains that occur in physical hands-on activities. 6. Conclusions From analysis of each of the two interventions explored, the emergent themes illustrated aspects of...