Identifying and Aiding At-Risk Students in Computing

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提高，计算机科学（CS）教育已经被推到了前台。像编程一小时和CS10K这样的项目已经成功地吸引了学生学习计算机课程，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，关于学生的成功出现了一个令人担忧的主题。与其他STEM学科课程相比，学生的失败率更高，并且经常在早期经历不佳后完全离开该领域。许多学生学得比老师期望的要少。计算机科学教育界很好地记录了这些挣扎，并对他们的背景进行了假设，但在干预帮助这些学生方面做得不太好。利用学生过程和学习数据的来源，如课堂上的点击反应和细粒度的编程活动数据，可以使用机器学习技术识别在入门计算课程中挣扎的学生。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，以便及早识别挣扎的计算机科学学生，提高对导致学生挣扎的因素的理解，以及关于一对一或小组干预对帮助计算机科学学生提高的价值的书面报告。具体来说，该项目旨在通过(1)扩大该技术在不同情况下对更多计算机课程的适用性，(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)通过后续学生访谈试点干预措施，以更好地了解干预措施的效果，来推进团队在这一领域的初步工作。这项工作的更广泛的影响将是增加计算机科学本科学生的学习，成功和保留。拥有早期和准确识别困难学生的工具的教师将能够在学生落后太多之前进行干预。知道了学生们为什么挣扎，以及采取什么行动可能会有所帮助，教师就能更好地干预和帮助学生。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，但仍将无法满足行业需求。它还承诺帮助代表性不足的群体，这些群体最容易面临风险，从而改善计算机科学领域的性别、种族、民族和社会经济平等。

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.