As learning and instruction become increasingly diverse, educational researchers are able to collect numerous types of learning-related data from different sources, mainly from online/digital learning environments (e.g., online college courses, Massive Open Online Courses, learning management systems). Unfortunately, traditional analytic approaches do not easily handle large amounts of text and other forms of digital metric data. Digital metric data in the education field can be thought of as data of ever-changing learner performance and behaviors, learning paths, and learner-created artifacts, which were intentionally or unintentionally collected in digital learning environments. However, the proliferation of computer technologies and the development of computational analysis algorithms have led researchers actively to utilize new methods to investigate digital metrics in diverse learning environments. The methods described in this chapter are collectively called Learning Analytics (LA), which embrace the use of computational techniques that account for the conditions and factors of the learning experience. Although there is a growing body of literature that focuses on the use of LA, there is a concern that these approaches might not fully support the researcher faced with the task of comprehensive analysis of qualitative data. Still, there are a few studies that demonstrate how to converge LA with qualitative analyses as a form of mixed methods research. This chapter provides an overview of these studies to demonstrate how LA can be integrated into mixed methods research.