A conceptual model for reflecting on expected learning vs. demonstrated student performance

Educators are faced with many challenging questions in designing an effective curriculum. What prerequisite knowledge do students have before commencing a new subject? At what level of mastery? What is the spread of capabilities between bare-passing students vs. the top-performing group? How does the intended learning speci cation compare to student performance at the end of a subject? In this paper we present a conceptual model that helps in answering some of these questions. It has the following main capabilities: capturing the learning speci cation in terms of syllabus topics and outcomes; capturing mastery levels to model progression; capturing the minimal vs. aspi- rational learning design; capturing con dence and reli- ability metrics for each of these mappings; and nally, comparing and re ecting on the learning speci cation against actual student performance. We present a web- based implementation of the model, and validate it by mapping the nal exams from four programming subjects against the ACM/IEEE CS2013 topics and outcomes, using Bloom's Taxonomy as the mastery scale. We then import the itemised exam grades from 632 students across the four subjects and compare the demonstrated student performance against the ex- pected learning for each of these. Key contributions of this work are the validated conceptual model for capturing and comparing expected learning vs. demon- strated performance, and a web-based implementation of this model, which is made freely available online as a community resource.