How to Assess Student Learning in Information Science: Exploratory Evidence from Large College Courses

Abstract

ABSTRACTAssessments in higher education can help instructors understand their students' incoming knowledge and learning gains. Constructing and validating assessments is especially challenging in emergent, fast‐growing interdisciplinary STEM fields such as information science. Unlike more traditional STEM fields like physics and mathematics, information science builds on cross‐disciplinary connections with multiple pools of domain knowledge. This research investigates how to construct and use assessments to effectively capture knowledge and skills in information science. Our study was conducted in five large information science courses at a U.S. research university on data analytics, web development, visualization, technology design, and natural language processing. We worked with domain experts to develop assessment items at three levels of knowledge: declarative, applied, and transferred (Anderson et al., 2002). The assessments were administered in a pre‐post design over two semesters with 1,202 students, with an evidence‐based revision of the assessments between the semesters. Our initial findings suggest that some knowledge levels (applied and transferred) may be more suitable for assessing student learning in information science courses. The findings have implications for assessment in emergent interdisciplinary education and inform our plans to develop constructive assessment methods for information science education.