Exploration of Text Analytic Tooling on Classwork to Support Students' Learning in Information Technology

Information technology graduates reach industry and innovate for the future after completing demanding degrees. Upper division college courses require long hours of work on class projects and exams. Some students have hopes of completing their degrees, but are deterred due to many different issues. Instructors can monitor students' progress based on their assignments, projects, and exams. Judging students' understanding and potential for success becomes more difficult when handling large classes. In this paper we utilize IBM Text Analytics Web Tooling on large amounts of unstructured text data collected from past assignments, exams, and discussions to help professors make assessments faster for large classes. In particular, we focus on an Information Security course offered at San Jose State University and use its classroom-generated data to determine if the extracted information provides strong insights for professors to help struggling students. We examine these issues through exploratory analysis.