Computational Literacy for Non-traditional Students

Contributed by Maria Hwang, maria_hwang@fitnyc.edu

Institutional and departmental context

• Institution Name: Fashion Institute of Technology (FIT)
• Location: New York City, NY
• Student body size: 8555 undergraduate students; 212 graduate students: 8767 total students (7,406 Full-Time Students: 1,361 Part-Time Students)
• Degree(s) offered: A.A.S., B.F.A., or B.S. degrees (The School of Graduate Studies offers seven programs leading to the Master of Arts, Master of Fine Arts or Master of Professional Studies degree. In addition to the degree programs, FIT offers a wide selection of non-credit courses through the Center for Professional Studies.)
• Department/major name: Science and Math department (no majors are offered, only a minor in math is offered since 2009); We are currently working on building a computer science minor.
• Number of contributing faculty: Four faculty – Maria Hwang; Sandra Markus; Jennifer Shloming; and Calvin Williamson
• Number of majors annually: More than 40 degree programs. Majors span a wide range of fields, from photography and toy design to international trade and cosmetics and fragrance marketing.

Facilitation

We are willing to facilitate this discussion.

Potential facilitators include the collaborators in this application – Sandra Markus; Jennifer Shloming; and Calvin Williamson.

Description

As a trade-school and community college, graduating mostly non-computationally focused students, the Fashion Institute of Technology (FIT) has been working to increase the student body’s computational literacy. However, there has been limited resources dedicated to creating and supporting this environment.

Undergraduate students at FIT apply directly to their majors, either in the School of Business and Technology, or the School of Art and Design. The Science and Math department has no majors since the courses serve to fulfill general education requirements. Only a math minor has been offered since 2009 and we are now working on creating a computer science minor due to popular demand. However, reflecting the nation-wide decline in student population, FIT has been faced with numerous financial challenges, contributing to the difficulty of providing tailored, and computationally integrated and oriented courses.

In response to these challenges, for example, one of the computer science courses provided at FIT (a permutation of a statistics course called ‘Statistics, Machine Learning, and Data Mining’) has evolved throughout the last four years and continues to do so. Our latest cloud based approach has sparked our interest in sharing this with the computer science higher education community. Because no software installation or server infrastructure is needed beyond a Google login and computation is all done on Google Colab servers, the course does not rely on support from IT, or any additional costly resources, which typically make widespread technology-integrated curriculum adoption difficult. Therefore, the environment scales to large groups of students or multiple sections easily. Besides providing students a familiar document environment for their work, notebook style lectures and assignments encourage students to practice and utilize literate programming in the form of active note-taking and course documentation creation.

We share our resources with the CSE community—as others have done and will continue to report on our trials and errors with the serverless, cloud-based, machine learning course at FIT serving non-traditional students with little to no technical backgrounds. We hope to lead a discussion surrounding such curriculum adoption and the challenges with developing institutional support for the infrastructure required for computation literacy.