Computational Thinking Theme Sessions

While there has been a recent push to increase computational thinking for STEM students, many open questions exist about how to promote and integrate this type of thinking into the undergraduate curriculum. We are interested in the different ways that computational approaches have been integrated into the curriculum of different disciplines, and, conversely, in how application area from other STEM disciplines have been integrated into computer science. The focus of this strand of the conference will be on understanding the role computing and coding plays in the STEM classroom across disciplines. Mainly, the focus will be on answering the following questions:

  • How can DBER and SoTL investigate and inform best practices for the integration of computing in STEM classrooms?
  • What new connections between the disciplines and teaching in the different STEM fields are emerging and can be gainfully used based on computing, coding, and data?
  • What types of activities and materials are available across disciplines that foster computational thinking?

Kam Dahlquist, Dept. of Biology at Loyola Marymount University; Ran Libeskind-Hadas, Dept. of Computer Science at Harvey Mudd College; and Andrew Schaffner, Dept. of Statistics at Cal Poly, have organized the various sessions related to problem solving in STEM education. Information about these leaders can be found on the Computational Thinking Theme Organizers page.

Morning Workshop- Computing Across Domains
Leo Porter, Computer Science and Engineering Dept. at UC San Diego

Computing has become nearly ubiquitous in our personal and professional lives. This has the potential to be a unifying factor across fields as a common knowledge we all share. In fact, many of us envision computing as an essential component throughout K-12 and higher education.

However, the realities are more complex. Although this workshop focuses on higher education, debate abounds about the curriculum in K-12 education and that curriculum heavily impacts what we can assume of our incoming students. Even within higher education, because each field often perceives different requirements from computing, we find a computing curriculum which is typically fractured across campus. For example, at some institutions, there are different introductory computing courses hosted by biology, chemistry, cognitive science, data science, engineering, and computer science departments.

This workshop has two goals. 1. Are our demands from computing truly so unique? Must the computing curriculum be fractured? To help inform these questions, we'll work in small groups based on discipline to determine what we want our students to be capable of doing with computing at both a program and course level. This discussion should help us better understand what we all have in common; and what we do not. 2. What can we learn from existing work in computing education research about how to teach computing? Specifically, we'll discuss how learning computing may be different than other fields, common challenges for students learning computing, and established best practices in teaching computing.

Contributed Paper Session

Different Flavors of Computing - Providing Context to a First Course in Computing
Chris Clark, Engineering, Harvey Mudd College

In 2010, the Computer Science Department at Cal Poly SLO augmented the traditional three course computer programming sequence by adding a fourth course to the beginning of the sequence. This course is not just taken by CS students, but by computer engineering students as well. The goal of this additional course is to attract and retain undergraduate students that have no prior experience in computing by providing problems that have relevance in society. Several versions of the new course were offered in parallel, and each version differed by application area. Specifically, there are 5 versions of the course taught: gaming, mobile apps, robotics, music, and computational art. Students were provided with the opportunity to choose which course they took so that pre-existing interests could be leveraged. This presentation will provide details of the curriculum and course topics. Results regarding graduation rates, GPA, and attitude towards computing will also be presented. Notably, computer engineering four year graduation rates rose by 14.6%.

A Framework for Models and Modeling to Unify Mathematicians and Biologists and Improve Student Learning
Kam Dahlquist, Biology, Loyola Marymount University

Professional societies and collaboratives have called for the incorporation of more quantitative skills, such as mathematical modeling, securely into the foundation and throughout undergraduate biology programs. However, the integration of modeling both within and between mathematics and biology curricula remains limited. I belong to a collaborative working group sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS) that has engaged in cross-disciplinary discussions to address this and has produced a framework for models and modeling that facilitates communication between faculty practitioners and improves student learning. I will present the framework and suggest ways that it can be extended to all STEM disciplines.  I will also discuss ways that a similar cross-disciplinary working group could address computation across STEM curricula.

Where do Mathematics and Statistics fit in to a World of Data Science?
Jo Hardin, Mathematics, Pomona College

As a statistician, I have embraced the data science revolution and my role in training new data scientists. Beyond encouraging students to interpret statistical results, I have enhanced my courses to include computing as a way to develop students' ability to think algorithmically. I give examples of ways that traditional statistics courses can be strengthened in a world of increasing data and computation. As a member of a mathematics department, I have thought deeply about the role of mathematics in data science. There is an ongoing challenge for mathematicians to respond to the theoretical needs of a new generation of scientists. I provide some suggestions for modifications to a mathematics curricula which will create a theoretical framework on which a strong data science foundation can be built.

CS 1 Green:  An Introductory CS course with a Biology Theme
Ran Libeskind-Hadas, Computer Science, Harvey Mudd College

I will describe a first-year undergraduate course developed at Harvey Mudd that teaches CS 1 concepts and skills using problems from biology to motivate and exercise each topic.  The course covers the same computing content as the College's "standard" CS 1 course while demonstrating connections and applications to biology.  This course has been taught at Harvey Mudd for nearly a decade and has been successfully taught at other schools as well.  The talk will describe the course philosophy, structure, and assessment results.

Teaching Computational Thinking in Data Science with Open-Source Tools
Brian Granger, Physics, Cal Poly San Louis Obispo

Cal Poly San Luis Obispo has created an interdisciplinary data science minor that allows students from the computer science (CS) and statistics (STATS) departments to complete a program that 1) provides them with core courses in both CS and STATS and 2) uses that as a foundation for teaching a dedicated data science curriculum. In 2017, a second cohort of students has begun this curriculum as the first cohort is completing it. In this talk, I will reflect on my experience of developing and teaching DATA 301, "Introduction to Data Science in Python," which students take after introductory courses in CS/STATS. In particular, I will describe my approach to teaching this course, which emphasizes computational thinking and open-source software. This approach has emerged from my research program which is focused on building open-source software for scientific computing and data science (The Jupyter Notebook and the Altair Statistical Visualization library). 

Afternoon Working Group

During the Working Group session, after a short recap of the day's highlights, affinity groups of three to five faculty each will discuss conference themes and outline future work in a collaborative online space. Themes will be suggested by the conference organizers and by the participants themselves throughout the day. Participants will then choose the affinity group with which he or she wants to work. It is our vision that these affinity groups will exist beyond the end of the conference to develop white papers for submission to the conference proceedings, building community amongst STEM faculty beyond the home institution. Possible affinity groups include: Carrying out reproducible research, Making programatic institutional change, and the Degree to which computing could be infused in discipline-based courses.

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