Student Success in Co-operative Education: An Analysis of Job Postings and Performance Evaluations
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Date
2023-05-26
Authors
Salm, Veronica
Journal Title
Journal ISSN
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Publisher
University of Waterloo
Abstract
Co-operative education (co-op) programs combine coursework and work internships and have become popular worldwide. In this analysis, we use two separate co-op datasets to understand employer expectations and factors that contribute to student success.
First, we analyze over 13000 unique filled job postings from work terms in 2021. We group skills using k-means analysis and frequency counting to characterize the types of co-op jobs available to students, finding that co-op students are frequently required to possess both technical skills (such as knowledge of specific tools) and soft skills (such as communication). Next, we construct two separate weighted bipartite graphs linking the groups of academic programs advertised to by employers to either the required skills or titles of each job. By using community detection to co-cluster the nodes in each graph, we determine the types of skills and roles expected by employers for students in different programs. We find significant differences in the expectations of employers for students in each program, including the importance of soft skills for arts students and the prevalence of data science and artificial intelligence skills in many academic programs.
Second, using over 45000 performance evaluations collected separately for in-person (2019) and remote (2021) internship positions, we uncover the characteristics of successful co-op students. Each evaluation includes an overall performance rating and written comments and recommendations provided by the supervisor. By using logistic regression and word frequency counting to analyze supervisors’ general and recommendation comments, we find the most successful students to be excellent leaders and innovators, with remote students also being praised for their independence. Supervisors encourage remote students to be innovative and learn technological skills, while the supervisors of in-person students recommend improving oral communication and presentation abilities.
By identifying the job roles and required skills expected by employers for students in different academic programs, institutions can better prepare students for appropriate jobs. By understanding the skills that contribute to student success in remote and in-person contexts, students can focus on developing the most important skills for their intended work environment. Together, these findings highlight important skills that students should acquire in their early careers.
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Keywords
student success, co-operative education, machine learning, logistic regression, bipartite graphs, community detection, clustering, text preprocessing, k-means, job descriptions, performance evaluations