• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Proceedings of the Ninth annual international conference on international computing education research >A Fast Measure for Identifying At-Risk Students in Computer Science
【24h】

A Fast Measure for Identifying At-Risk Students in Computer Science

机译：识别计算机科学高风险学生的快速措施

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

How do we identify students who are at risk of failing our courses? Waiting to accumulate sufficient assessed work incurs a substantial lag in identifying students who need assistance. We want to provide students with support and guidance as soon as possible to reduce the risk of failure or disengagement. In small classes we can monitor students more directly and mark graded assessments to provide feedback in a relatively short time but large class sizes, where it is most easy for students to disappear and ultimately drop out, pose a much greater challenge. We need reliable and scalable mechanisms for identifying at-risk students as quickly as possible, before they disengage, drop out or fail. The volumes of student information retained in data warehouse and business intelligence systems are often not available to lecturing staff, who can only observe the course-level marks for previous study and participation behaviour in the current course, based on attendance and assignment submission. We have identified a measure of "at-risk" behaviour that depends upon the timeliness of initial submissions of any marked activity. By analysing four years of electronic submissions over our school's student body we have extracted over 220,000 individual records, spanning over 1900 students, to establish that; early electronic submission behaviour provides can provide a reliable indicator of future behaviour. By measuring the impact on a student's Grade Point Average (GPA) we can show that knowledge of assignment submission and current course level provides a reliable guide to student performance.
机译：我们如何确定有可能无法通过课程的学生？等待积累足够的评估工作会导致确定需要帮助的学生的时间大大滞后。我们希望为学生提供支持和指导，以减少失败或脱离的风险。在小班教学中，我们可以更直接地监控学生并标记评分评估，以在相对较短的时间内提供反馈，但是在大班教学中，学生最容易消失并最终辍学的情况构成了更大的挑战。我们需要可靠且可扩展的机制，以便在脱离接触，辍学或失败之前尽快识别高风险学生。讲授人员通常无法获得保留在数据仓库和商业智能系统中的大量学生信息，讲授人员只能根据出勤和作业提交来观察以前学习的课程级别标记和当前课程中的参与行为。我们已经确定了一种“风险”行为的度量，该度量取决于任何标记活动的首次提交的及时性。通过对我们学校学生团体四年来的电子提交书进行分析，我们提取了22万多条个人记录，涵盖了1900多名学生，以建立该记录；早期的电子提交行为可以提供未来行为的可靠指标。通过测量对学生的平均绩点（GPA）的影响，我们可以证明作业提交和当前课程水平的知识为学生的表现提供了可靠的指南。

著录项

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