CRII: IIS: HCC: Interplay of Emotions and Self-Efficacy of Novice Students during Programming Tasks: A Multi-Modal Approach

计算机编程是计算和工程专业学生越来越需要的技能，通常在本科课程的早期就可以找到。 然而，学习编程是一项艰巨的任务，可能会引发各种各样的情绪，这些情绪可能会影响学生的学习和对学习编程能力的信念。 反过来，这可能会影响他们的持久性和最终保留在个人编程课程和计算相关的学位和职业生涯。 该项目旨在更好地了解学生个人的背景和对自己能力的信念如何塑造他们对编程挑战的情绪反应，以及这些反应如何反过来影响他们的信念。 这项工作将为人们如何学习编程提供更好的模型，并为本科生的编程教学方式提供信息，并且可能对那些经常经历更多负面情绪和自我效能感较低的群体的学生特别有益。 从长远来看，该项目将有助于劳动力发展和扩大对计算和STEM的参与。该项目旨在使用新颖的想法来促进对非认知因素（情感和自我效能）的理解，因为它们与本科生学习入门编程有关。 首要的研究问题是：学生的情绪和自我效能信念如何相互作用，在编程任务？ 这项工作还将梳理出这种相互作用如何在性别，种族和编程经验的差异中为学生带来变化，并利用在美国最大的公立大学之一进行这项工作所提供的多样化样本。 为此，项目小组将进行两轮数据收集和分析。 在第一轮中，将建立两个数据收集工具（编程成就情绪问卷和计算机编程自我效能量表）的效度和信度。在第二轮中，该团队将使用经过验证的量表收集数据，沿着实时生物特征数据和访谈。 三角分析这些数据将有助于建立理论和计算模型的情绪和自我效能感之间的相互作用的背景下学习编程在本科水平。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Computer programming is an increasingly necessary skill for computing and engineering students that is often found early in an undergraduate program. However, learning programming is a difficult undertaking that can induce a wide array of emotions that may impact students' learning and belief in their abilities to learn programming. This, in turn, may affect their persistence and eventual retention in both individual programming courses and computing-related degrees and careers. This project looks to better understand how individual students' backgrounds and beliefs about their abilities shape their emotional responses to programming challenges, and how those responses in turn affect their beliefs. The work will lead to better models of how people learn programming and inform the way programming is taught to undergraduate students, and may be especially beneficial to students from groups who often experience more negative emotions and lower self-efficacy than students overall, including women and minority students. In the longer term, the project will contribute towards workforce development and broadening participation in computing and STEM in general.This project aims to use novel ideas to advance the understanding of non-cognitive factors (emotions and self-efficacy) as they relate to learning introductory programming for undergraduate students. The overarching research question is: how do students' emotions and self-efficacy beliefs interact with each other during programming tasks? The work will also tease out how that interplay varies for students across differences in gender, ethnicity, and prior experience with programming, drawing on the diverse sample provided by doing the work at one of the largest public universities in the United States. To do this, the project team will conduct two rounds of data collection and analysis. During the first round, the validity and reliability of two data collection instruments (the Achievement Emotions Questionnaire for programming and Computer Programming Self-Efficacy Scale) will be established. In the second round the team will collect data using the validated scales, along with real-time biometric data and interviews. Triangulating these data will help build both theoretical and computational models of the interplay between emotions and self-efficacy in the context of learning programming at the undergraduate level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.