Identifying and Aiding At-Risk Students in Computing

识别和帮助计算机领域的高危学生

• 批准号: 1712508
• 负责人: Leo Porter
• 金额: $ 29.93万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712508&HistoricalAwards=false
• 关键词: Identifying; Aiding; Risk; Students; Computing

This project will address the problem of meeting computer science students' learning needs by identifying and supporting at-risk students. Computer science (CS) education has been pushed to the foreground as the importance of computing in society continues to increase. Initiatives like the Hour of Code and CS10K have been successful in attracting students to computing courses, but this in turn has also strained instructor resources at many institutions. Over the past 40 years of computer science education, a concerning theme has emerged in terms of student success. Students fail at elevated rates compared to other STEM disciplinary courses and often leave the field altogether after poor early experiences. Many learn less than instructors expect. The CS education community has done well to document these struggles and hypothesize on their antecedents, but has done less well in terms of intervening to help these students. Leveraging a source of student process and learning data not available to earlier generations of researchers such as in-class clicker responses and fine-grained programming activity data allowed for identification of struggling students in introductory computing courses using machine learning techniques. The core intellectual merit of the project is the creation of practical and sharable methods and tools for instructors to identify struggling CS students early, an improved understanding of the factors that cause students to struggle, and written reports on the value of one-on-one or small group interventions for helping CS students improve. Specifically, the project aims to advance the team's preliminary work in this area by (1) broadening the applicability of the technique to more computing courses under differing circumstances, (2) interviewing students at-risk early in the term with the goal of identifying reasons for their struggles, and (3) piloting interventions with follow-up student interviews to better understand the effect of the interventions. The broader impacts of this work will be the increased learning, success, and retention of computer science undergraduate students. Instructors armed with the tools of early and accurate identification of struggling students will be able to intervene before the students have fallen too far behind. Knowing why the students are struggling, and what actions might help, better equips instructors to intervene and help students. This offers the potential to grow the supply of capable computer scientists, which, despite increased national enrollments, will still fall short of industry demand. It also promises to help underrepresented groups, who are most apt to be at risk, thus improving gender, racial, ethnic, and socioeconomic equality in CS.

该项目将通过识别和支持有风险的学生来解决满足计算机科学学生学习需求的问题。 随着计算机在社会中的重要性不断增加，计算机科学（CS）教育已被推到前台。像编程时间和CS10K这样的计划成功地吸引了学生参加计算课程，但这反过来也使许多机构的教师资源紧张。在过去40年的计算机科学教育中，一个关于学生成功的主题已经出现。与其他STEM学科课程相比，学生的失败率较高，并且经常在早期经历不佳后完全离开该领域。 很多人学到的东西比老师期望的要少。CS教育界已经很好地记录了这些斗争，并对他们的前身进行了假设，但在干预帮助这些学生方面做得不太好。 利用前几代研究人员无法获得的学生过程和学习数据的来源，例如课堂上的点击器响应和细粒度的编程活动数据，可以使用机器学习技术来识别入门计算课程中的学生。该项目的核心智力价值是为教师创造实用和可共享的方法和工具，以早期识别挣扎的CS学生，提高对导致学生挣扎的因素的理解，并就一对一或小组干预的价值撰写报告，以帮助CS学生提高。 具体而言，该项目旨在通过以下方式推进团队在这一领域的初步工作：（1）将该技术的适用性扩大到不同情况下的更多计算课程，（2）在学期早期采访有风险的学生，以确定他们挣扎的原因，以及（3）通过后续学生访谈进行干预，以更好地了解干预措施的效果。 这项工作的更广泛的影响将是计算机科学本科生的学习，成功和保留的增加。教师如果能够及早准确地识别出有困难的学生，就能够在学生落后太远之前进行干预。知道学生为什么挣扎，什么行动可能会有所帮助，更好地装备教师干预和帮助学生。这为增加有能力的计算机科学家的供应提供了潜力，尽管全国招生人数增加，但仍将满足不了行业需求。它还承诺帮助代表性不足的群体，他们最容易处于危险之中，从而改善CS中的性别，种族，民族和社会经济平等。

项目成果

Achievement Goals in CS1: Replication and Extension

• DOI: 10.1145/3159450.3159452
• 发表时间: 2018-02
• 期刊: Proceedings of the 49th ACM Technical Symposium on Computer Science Education
• 作者: Daniel Zingaro;Michelle Craig;Leo Porter;Brett A. Becker;Yingjun Cao;Phill Conrad;D. Cukierman;Arto Hellas;Dastyni Loksa;Neena Thota

Understanding Sources of Student Struggle in Early Computer Science Courses

• DOI: 10.1145/3446871.3469755
• 发表时间: 2021-08
• 期刊: Proceedings of the 17th ACM Conference on International Computing Education Research
• 作者: Adrian Salguero;W. Griswold;Christine Alvarado;Leo Porter

A Quantitative Analysis of Study Habits Among Lower- and Higher-Performing Students in CS1

CS1 成绩较差和成绩较高的学生学习习惯的定量分析

• DOI: 10.1145/3430665.3456350
• 发表时间: 2021
• 期刊: 26th ACM Conference on Innovation and Technology in Computer Science Education
• 作者: Liao, Soohyun Nam;Shah, Kartik;Griswold, William G.;Porter, Leo
• 通讯作者: Porter, Leo

Behaviors of Higher and Lower Performing Students in CS1

CS1 中表现较高和较低的学生的行为

• DOI: 10.1145/3304221.3319740
• 发表时间: 2019
• 期刊: 2019 ACM Conference on Innovation and Technology in Computer Science Education
• 作者: Liao, Soohyun Nam;Valstar, Sander;Thai, Kevin;Alvarado, Christine;Zingaro, Daniel;Griswold, William G.;Porter, Leo
• 通讯作者: Porter, Leo

Exploring Student Experiences in Early Computing Courses during Emergency Remote Teaching

• DOI: 10.1145/3430665.3456315
• 发表时间: 2021-06
• 期刊: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1
• 作者: M. Lewis;Zhanchong Deng;Sophia Krause-Levy;Adrian Salguero;W. Griswold;Leo Porter;Christine Alvarado