CPATH-1: An interdisciplinary, problem-based, collaborative course on computational thinking

该项目正在为贝勒大学的非计算机科学本科生开发和提供一门关于计算思维的课程。在这门课程中，计算思维被看作是算法，因此它帮助学生发展有效地解决算法问题所需的技能。它包括诸如问题抽象和分解、基本编程概念以及计算的实际和理论限制等主题。这门课程有几个独特的方面。首先，它面向所有学生，而不仅仅是科学相关学科的学生。第二，它是协作的。学生们以小组形式学习，每周更换一次。第三，以问题为基础。学生通过解决实际问题来学习计算思维。最后，课程中给出的问题来自计算机科学以外的学科。来自整个大学的教师们正在帮助开发一套将在课堂上使用的问题。由于这门课程的重点是计算思维如何在其他学科中应用，因此它比现有的计算机科学专业入门课程更能培养非计算机科学专业的学生。项目的第一年重点放在课程开发上，包括定义问题类型的规范列表，为课程收集问题，以及为学生实现选择合适的软件。该课程将在项目第二年开设两次，每学期一次。考虑到传统的计算机科学导论课程的招生情况，对这门课程的需求很高。这门非专业的入门课程为非计算机科学家提供了计算思维的广泛介绍。两位主要研究人员都有多年使用基于问题的协作学习向各级计算机科学专业的学生教授计算思维的经验。以问题为基础的课程，使用来自外部学科的问题，用于激发计算主题。这门课程也让学生了解计算的广度。S应用程序，并将其引入其他领域。这门课程有可能成为引入算法计算思维的典范，从而具有变革性。它汇集了来自多个学科的学生和问题，并将他们团结在一个共同的主题下。该课程在训练学生计算思维方面的效果将被严格评估，并与传统的计算机科学入门课程进行比较。更广泛的影响如果技术要继续对社会产生积极的影响，那么与技术互动的人必须了解如何有效地使用和创造技术。为此，建议开设的课程通过为学科以外的学生提供服务来扩大计算机的参与。它还通过将外部教师纳入课程开发，扩大了教师的参与。该课程通过提供课程、软件工具和一系列可公开使用的问题来增强教育基础设施。ppi们对这门课程有着广泛的支持，不仅来自贝勒大学计算机科学系内部，也来自贝勒大学许多其他院系的教员。

This project is developing and offering a course in computational thinking for non-computer science undergraduate students at Baylor University. In this course, computational thinking is viewed algorithmically, thus it helps students to develop the skills needed to effectively solve problems with algorithms. It includes such topics as problem abstraction and decomposition, fundamental programming concepts, and the practical and theoretical limits of computation.There are several distinctive aspects to this course. First, it is intended for all students rather than only those in science-related disciplines. Second, it is collaborative. Students work in small groups that change weekly. Third, it is problem-based. Students learn computational thinking by solving real problems. Finally, problems given in the course come from disciplines outside of computer science. Faculty members from across the university are helping to develop the set of problems to be used in the class. Because the course focuses on how computational thinking may be used in other disciplines, it will better train non-computer science majors than the existing introductory course for computer science majors.The first year of the project is focused upon curriculum development, including defining a canonical list of problem types, gathering problems for the course, and selecting appropriate software for student implementation. The course will be offered twice in the second year of the project, once each semester. Given the enrollment in traditional introduction to computer science courses, demand for the course is high.Intellectual MeritThis introductory course for non-majors gives a broad introduction to computational thinking for non-computer scientists. The principal investigators each have years of experience using problem-based, collaborative learning to teach computational thinking to computer science majors at all levels. A problem-based curriculum, using problems from external disciplines, serves to motivate the computational topics. The course also educates students about the breadth of computation?s application, and introduces them to other fields.This course has the potential to be transformative by becoming a model for the introduction to algorithmic computational thinking. It brings together students and problems from multiple disciplines, and unites them under a common theme. The efficacy of the course in training students in computational thinking will be rigorously evaluated and compared with a traditional introductory computer science course.Broader ImpactsIf technology is to continue to have a positive impact on society, it is essential for the people who interact with technology to understand how to effectively use and create with it. To this end, the proposed course broadens participation in computing by serving students outside the discipline.It also broadens the participation of faculty members by including external faculty in course development. The course enhances education infrastructure by providing a curriculum, software tools, and a body of problems to be made publicly available. The PIs have broad support for this course, both from inside the computer science department and across the faculty in many other departments at Baylor.