Research: A Mixed-Methods Approach to Characterizing Engineering Students' Computational Habits of Mind

在我们的社会中寻找复杂问题的解决方案需要工程专业的毕业生谁不仅拥有一个学科的技术知识和技能，而且还具有专业的心态。 “思维习惯”是成为有效的问题解决者所需的思维模式，能够将这些技能转移到新的环境中。这些技能构成了工程师专业思维的重要组成部分。本研究旨在探讨工科大学生与计算相关的思维习惯。所提出的方法的新奇在于，数据收集和分析方法将机器学习与传统上用于教育的方法相结合，以分析数据。该项目的指导性教育研究问题是：工程专业学生在其学术发展的不同阶段表现出什么样的计算思维习惯？这项工作的目标是更好地了解这些模式与学术成就的关系，这反过来又为工程计算能力的学习途径提供了信息。本研究采用序贯混合方法设计，将机器学习与混合方法相结合，研究工科学生的计算思维习惯。为每个学习者收集高维定量数据，并使用机器学习来找到与相似学习者组相对应的模式。通过将学习者划分为相似性组，这些模式提供了比全局分析方法更高的理解细节。将通过收集和分析定性数据来确定模式背后的原因。在机器学习结果的基础上，使用自动主题选择策略将研究定性部分的学习者数量减少到可管理的规模。这种学习者减少策略的目标是为每组受试者获得一个小但有统计意义的样本，以便将资源集中在研究中最昂贵和最耗时的部分。通过这样做，混合方法研究被证明是可扩展的大型和复杂的数据集。该项目的基本原理是，它的成功完成：a）解决了在专业和未来工程师中培养终身学习文化的挑战; B）提供了对思维习惯模式与学术成就如何相关的理解，这反过来又为工程计算能力的学习途径提供了信息;以及（c）演示了如何通过使用机器学习来扩大混合方法研究，以研究非常大和复杂的多维数据集。这一努力支持了基金会在工程专业学生教育中融入必要技能的需要，如创造力，独创性，专业精神，毅力和承担计算风险的意愿。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Finding solutions to complex problems in our society requires engineering graduates who not only possess the technical knowledge and skill set of a discipline, but also a professional mindset. "Habits of mind" are modes of thinking required to become effective problem solvers capable of transferring such skills to new contexts. Such skills form an essential part of engineer's professional mindset. This project studies undergraduate engineering students' habits of mind related to computing. The novelty of the proposed approach is that the data collection and analysis methods integrate machine learning with methods traditionally used in education to analyze data. The guiding education research question of the project is: What computational habits of mind do engineering students exhibit at different points of their academic development? The goal of the effort is to provide a better understanding of how these patterns relate to academic achievement, which in turn informs learning pathways toward computational proficiency in engineering. The project follows an Explanatory Sequential Mixed Method Design to study engineering students' computational habits of mind by integrating machine learning in a mixed method research. High-dimensional quantitative data is collected for each learner and machine learning is used to find patterns corresponding to groups of similar learners. By dividing the learners into similarity groups, these patterns provide a higher detail of understanding than global analysis methods. The reasons behind the patterns will be identified by collecting and analyzing qualitative data. Building on the machine learning results, an automatic subject selection strategy is used to reduce the number of learners considered to a manageable size for the qualitative part of the research. The goal of this learner reduction strategy is to obtain a small but statistically meaningful sampling for each group of subjects in order to focus resources for the most costly and time consuming part of the research. By doing so, mixed method research is shown to be scalable to large and complex datasets. The rationale for this project is that its successful completion: a) addresses challenges in cultivating a culture of lifelong learning among professional and future engineers; b) provides an understanding of how patterns of habits of mind relate to academic achievement, which in turn informs learning pathways toward computational proficiency in engineering; and (c) demonstrates how mixed method research can be scaled up to study very large and complex multidimensional datasets through the use of machine learning. This effort supports the need for the foundation in engineering students' education to incorporate necessary skills such as creativity, ingenuity, professionalism, persistence, and willingness to take calculated risks.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.