Teaching and Learning

Towards Computational Thinking Across the Curriculum

Computational Thinking – Public domain image

Computational thinking is a framework that students can utilize to solve complex problems and apply across disciplines and in many types of settings, even ones far removed from computer science. Computational thinking is a paradigm where students learn how to break down a problem into smaller parts, identify patterns, and abstract those patterns to develop innovative solutions, skills which are applicable inside and outside of the classroom. For example, a student may implement computational thinking to refine their argument in a paper, self-reflexively examining their thesis broken down into specific cases, and basing their argument on the patterns that emerge across them. The same student may apply computational thinking in their strategy to juggle their life outside school, examining potential barriers to making it to class, splitting overwhelming challenges into “overcomable” ones, and devising step- by-step solutions to mitigating them. In careers, the ability to break up complex problems is necessary. A student taking on professional roles in the workforce will rely on the skills woven into computational thinking such as the ability to deconstruct a problem and analyze it carefully for a new solution. Being able to do so will enhance their competitiveness in the job market. Computational thinking can be a key underlying methodology for approaching education, work, and even everyday  problem-solving that can transform student outcomes. These skills are invaluable across disciplines. Iterating new approaches to social concerns or developing reproducible and scalable solutions with efficient data analytics pipelines in a sociology course, deconstructing an analysis of  themes in a literature course and building a new argument based on observable patterns, identifying patterns in experiments and analyzing new results in a chemistry class–these are all examples of how the rigor of computational thinking can improve student work in class. Computational thinking is a framework applicable to any complex problem.

The BMCC Technology Learning Community, a research project funded by the National Science Foundation (NSF), has been committed to infusing and evaluating computational thinking and project-based collaborative learning across the curriculum since Fall 2021. Eight faculty members in the Computer Science, Sociology, and Business departments have participated. The project has impacted over 600 students across a variety of majors. Recently our Spring 2022 Ideathon culminated in 220 students across disciplines developing prototypes for projects which used computational thinking and technological innovation to address real world problems they face on and off campus, such as housing and sustainability. In evaluating our research project, we have conducted ongoing interviews and focus groups, as well as survey forms, and are engaging in qualitative and quantitative analysis of this data. BMCC Students who participated in our project articulated the applicability of computational thinking to multiple areas of problem-solving as well as an overall increase in feelings of confidence and competence.

Embedding these skills more comprehensively across education on campus is one way to support this student success. The project is expanding to include more faculty, and to share the lessons we have learned along the way. Fall 2022 BMCC Faculty Development Day presented an opportunity to share techniques for infusing computational thinking across the curriculum. Now we are preparing a longer form paid summer/fall professional development opportunity to help faculty implement computational thinking collaboratively in their classrooms. The informational session for the upcoming paid “Computational Across the Curriculum Fellowship” is Thursday, April 27th 1pm-2:30pm (on Zoom), the registration link is here: RSVP.

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