The demand for education in STEM fields is exploding, and universities and colleges are straining to satisfy this demand. In the case of Computer Science, for example, the number of US students enrolled in introductory courses has grown three-fold in the past decade. Recently massive open online courses (MOOCs) have been promoted as a way to ease this strain, but scaling traditional models of teaching to MOOCs poses many of the same challenges observed in the overflowing classrooms, namely, assessment of students’ knowledge and providing meaningful feedback to individual students. This motivates a new agenda for computing research: formalize tasks such as assessment and feedback as computational problems, develop algorithmic tools to solve resulting problems at scale, and incorporate these tools effectively in learning environments.

Automated tutoring has been studied at different times in different communities. Top-down approaches based on formal logical reasoning are rooted in research on formal verification and synthesis. The resulting tools for tasks such as automatic generation of problems, automatic grading, and automatic generation of hints have been developed for problems arising in a number of CS courses such as algorithms, automata theory, compilers, databases, and programming. At the opposite end of the spectrum of automated tutoring technology, bottom-up approaches based on machine learning mine data from learning experiences of students. Emerging applications of this approach range from learning analytics tools for students and instructors to track learning progress to personalized feedback tools that recommend the next best learning activity to a student based on past history.

The goal of this workshop was to bring together researchers developing educational tools based on technologies such as logical reasoning and machine learning with researchers in education, human-computer interaction, and psychology to articulate a long-term research agenda. The focus was on college-level courses in computer science, mathematics, and physics. The workshop was expected to foster new collaborations among participants from diverse disciplines, suggest new research directions in computer-aided education, inspire other researchers to work on these problems, and ultimately result in technology for effective and personalized learning.