Abstract
Computing educators have become increasingly interested in learning analytics, which involves collecting and analyzing data on students' learning processes and outcomes for the purpose of improving learning and instructional practices. A variety of computer programming environments enable the automated collection of log data on students' programming processes. In addition, log data on students' online social behavior can be easily collected. All of these data can be analyzed alongside data on students' learning outcomes in order to identify correlations between learning processes and outcomes, and ultimately to better tailor instruction to students' needs. This BOF will provide a platform for discussing the emerging field of learning analytics within the context of computing education. The following questions will serve as a starting point for our discussions: (1) What types of data should we be collecting on computing students' (2) How can we best analyze these data in order to gain meaningful insights into students' learning processes? (3) How can we design effective instructional interventions based on the data we collect and analyze?
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