Curriculum analytics: Exploring assessment objectives, types, and grades in a study program

Abstract

Higher education institutions are increasingly seeking ways to leverage the available educational data to make program and course quality improvements. The development of automated curriculum analytics can play a substantial role in this effort by bringing novel and timely insights into course and program quality. However, the adoption of curriculum analytics for program quality assurance has been impeded by a lack of accessible and scalable data-informed methods that can be employed to evaluate assessment practices and ensure their alignment with the curriculum objectives. Presently, this work remains a manual and resource intensive endeavour. In response to this challenge, we present an exploratory curriculum analytics approach that allows for scalable, semi-automated examination of the alignment between assessments and learning objectives at the program level. The method employs a comprehensive representation of assessment objectives (i.e., learning objectives associated with assessments), to encode the domain specific and general knowledge, as well as the specific skills the implemented assessments are designed to measure. The proposed method uses this representation for clustering assessment objectives within a study program, and proceeds with an exploratory analysis of the resulting clusters of objectives in relation to the corresponding assessment types and student assessment grades. We demonstrate and discuss the capacity of the proposed method to offer an initial insight into alignment of assessment objectives and practice, using the assessment-related data from an undergraduate study program in information systems.