  There is an increased interest in the application of learning analytics (LA)
to promote self-regulated learning (SRL). A variety of LA dashboards and
indicators were proposed to support different crucial SRL processes, such as
planning, awareness, self-reflection, self-monitoring, and feedback. However,
the design of these dashboards and indicators is often without reference to
theories in learning science, human-computer interaction (HCI), and information
visualization (InfoVis). Moreover, there is a lack of theoretically sound
frameworks to guide the systematic design and development of LA dashboards and
indicators to scaffold SRL. This chapter seeks to explore theoretical
underpinnings of the design of LA-enhanced SRL applications, drawing from the
fields of learning science, HCI, and InfoVis. We first present the
Student-Centered Learning Analytics-enhanced Self-Regulated Learning (SCLA-SRL)
methodology for building theory-driven LA-enhanced SRL applications for and
with learners. We then put this methodology into practice by designing and
developing LA indicators to support novice programmers' SRL in a higher
education context.

Computers and Society;Human-Computer Interaction