• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Continuous Assessment in Agile Learning using Visualizations and Clustering of Activity Data to Analyze Student Behavior.
【24h】

Continuous Assessment in Agile Learning using Visualizations and Clustering of Activity Data to Analyze Student Behavior.

机译：使用可视化和活动数据聚类分析学生行为的敏捷学习中的连续评估。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.;One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.;A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student's daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
机译：当今的软件工程教育是技术先进且发展迅速的学科。作为一门学科，学生不仅要设计而且还要学习新技术，因此以项目为基础的课程形式获得动手学习经验非常重要。为了获得最大的学习收益，学生必须以一致的节奏或节奏进行基于项目的学习活动。基于项目的课程加上频繁的形成性反馈系统可以帮助学生不断评估他们的进度，并使其脱离按时限驱动的学习方法。学生活动，作为提供频繁，形成性反馈的一种手段。本文测量了该工具对学生遵守学习过程的影响。向准本科生和软件工程专业的学生提供带有准实时视觉报告和通知的个性化仪表板。这些可视化报告对合规性的影响是通过在课程中多次使用仪表板活动的日志轨迹和调查工具进行测量的。这项研究的第二个方面是应用学习分析来了解学生合规性的模式。这项研究采用无监督的机器学习算法来识别在基于项目的课程中观察到的学生行为的独特模式。分析并标记这些独特的行为模式可以帮助教师理解典型的学生特征。此外，了解这些行为模式可以帮助教师进行及时，有针对性的干预。在这项研究中，利用由学生的日常活动和本科生软件工程课程的评分分数组成的数据集，用于识别学生行为的独特模式。

著录项

  • 作者

    Xavier, Suhas.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Computer science.;Educational technology.
  • 学位 M.S.
  • 年度 2016
  • 页码 85 p.
  • 总页数 85
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号