Automating an Engine to Extract Educational Priorities for Workforce City Innovation

• Main Author: Hobbs, Madison
• Format: Others
• Published: Scholarship @ Claremont 2019
• Subjects: Education; Work Based Learning; Natural Language Processing; Topic modeling; Data science; Curriculum and Instruction
• Online Access: https://scholarship.claremont.edu/scripps_theses/1388; https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=2402&context=scripps_theses

This thesis is grounded in my work done through the Harvey Mudd College Clinic Program as Project Manager of the PilotCity Clinic Team. PilotCity is a startup whose mission is to transform small to mid-sized cities into centers of innovation by introducing employer partnerships and work-based learning to high school classrooms. The team was tasked with developing software and algorithms to automate PilotCity's programming and to extract educational insights from unstructured data sources like websites, syllabi, resumes, and more. The team helped engineer a web application to expand and facilitate PilotCity's usership, designed a recommender system to automate the process of matching employers to high school classrooms, and packaged a topic modeling module to extract educational priorities from more complex data such as syllabi, course handbooks, or other educational text data. Finally, the team explored automatically generating supplementary course resources using insights from topic models. This thesis will detail the team's process from beginning to final deliverables including the methods, implementation, results, challenges, future directions, and impact of the project.