Data visualizations to foster self-regulated learning with intelligent programming tutors: Towards an instructional design framework

Investigating visualization techniques to support student learning is a continuing concern within the computer science education community. Although visual representations designed in accordance with how students process information stands to facilitate program comprehension during learning of complex topics, few studies have systematically investigated different modalities of representation grounded in theories of learning. This chapter attempts to outline theoretical constructs from the perspective of self-regulated learning theory to derive instructional design principles. These principles can be listed as follows: (1) signaling visual information with embedded cues; (2) mapping verbal information to visual representations; and (3) translating visual information from one representation to another. Each design principle is exemplified in the context of student learning with an intelligent programming tutor designed to facilitate distinct phases of programming learning and task performance. Taken together, visual representations designed in accordance with these guidelines stands to promote self-regulated learning in the domain of programming. Copyright © 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG.