Over the past several years, the Guided Pathways framework has led to the creation of career clusters, interest areas or meta-majors at numerous California community colleges. These groupings allow students to take courses that apply to multiple majors, such as STEM, business, health sciences or industrial and transportation technology, in an effort to simplify academic major selection. However, little data exist about students’ course-taking patterns post-completion.

Between 2014-2017, community college students earned more than 93,000 associate degrees for transfer (ADT), completing an average of 90 units. But only 60 units are required for a California State University transfer, indicating that some students are spending more time and money on coursework than required. To better understand student completion time, courses taken, units completed and other associated major and meta-major patterns, the Chancellor’s Office partnered with Amazon’s Artificial Intelligence/Machine Learning Team. 

The AI/Machine Learning engine seeks out students that took the shortest path to completion and analyzes their course taking patterns to understand which courses they took when, course sequence, and which courses they took at another college.

“As the system rethinks its success metrics and increases its data mining efforts, Amazon’s top AI and Machine Learning scientists are helping us identify and better understand where gaps and barriers exist along students’ pathways to achieving their educational goals,” said Dr. Omid Pourzanjani, Visiting Vice Chancellor, Digital Futures. “These findings will inform our conversations about pathway designs, scheduling practices and intra-college course-taking patterns.”

Meta-Major Patterns Discovered

Parsing through 2014-15, 2015-16, and 2016-17 data, the Amazon team has found copious patterns per college and per students’ major and meta-major course completion. Findings have been shared with more than 35 community colleges’ administrators and researchers, who alongside academic counselors, faculty, academic deans and Guided Pathways coordinators contribute to major and meta-major design.

“Having this data greatly enhances our ability to refine those designs informed by actual student journeys and through comparative analysis of program completion throughout the system,” said Robert Rundquist, Visiting Executive, Guided Pathways. 

Dr. Sonya Christian, president of Bakersfield College, echoed Rundquist’s sentiment. “Just as Edison, the greatest inventor in our history, made electric light accessible to the masses, it is our responsibility as educators to evolve and innovate toward efficiency, sustainability and scalability to meet the needs of our 21st century learners,” stated Christian. “The student journey project illuminates patterns in students’ navigation through our institutions, allowing us to carve the most efficient pathways to completion of degrees and certificates. Further, this partnership creates opportunities for colleges to identify and address institutional barriers throughout the student journey in order to design a comprehensive student success architecture using the Guided Pathways framework. Thanks to the partnership with Amazon’s AI/Machine Learning Team, California’s community colleges will be better equipped to provide customized, concierge support so that getting on the path and staying on the path becomes as simple as flipping on a light switch.”

Extending Analysis to A.A. and A.S. Degrees

The data intelligence partnership’s next phase will extend to pattern recognition among students’ associate of arts or associate of science degree completion. The Amazon team will examine students’ course taking sequences to determine the shortest paths to degree completion, providing colleges with even more actionable major and meta-major data.

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