由基于web的可视化分析工具支持的学术表现分析

2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) Pub Date : 2019-07-01 DOI:10.1109/ICALT.2019.00059

Ronak Etemadpour, Yongcheng Zhu, Qizhi Zhao, Yilun Hu, Bohan Chen, Mohammed Asif Sharier, Shirong Zheng, J. G. Paiva

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引用次数: 3

摘要

了解高校学生的学习成绩是教育研究领域的一个重要课题。教育工作者、项目协调员和教授感兴趣的是了解学生如何学习特定主题，特定主题如何影响其他主题的学习，学生在每门课程中的成绩/出勤率如何成为衡量其表现的重要指标，以及其他任务。在本文中，我们提出了一种可视化分析工具，该工具结合了数据可视化和机器学习技术，对来自程序课程的学生数据进行一些可视化分析。使用这个工具，我们直观地分析了学生在一些计算机科学项目课程中的表现，并证明了这种分析的结果将有助于教育专家了解课程结构的不足。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Academic Performance Analysis Supported by a Web-Based Visual Analytics Tool

Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students' grades/attendances in each course may represent important indicators to measure their performance, among other tasks. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students' data from program courses. Using this tool, we visually analyzed the students' performance in some Computer Science program courses, and demonstrated that the results of such analysis will help the education experts to understand deficiencies on course structures.

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2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)

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