With online education comes large amounts of data that can reveal in ways able to be examined quantitatively only sparingly in the past, the physics problem-solving profiles of students. This project uses fundamental research to analyze physics problem-solving behavior of students enrolled in the Massive Open Online Course 8.MReVx offered on the MITx platform. Cluster analysis algorithms were employed to group students in natural clusters according to seven measures of students' performance. Educational Data Mining formalisms were employed to analyze: (1) what resources different categories of self-learners access while solving the tasks, (2) how they use the resources, (3) how they perform on various types of tasks that target specific cognitive levels and declarative and procedural knowledge, (4) what tasks they choose to solve, (5) how and when they solve them, and (6) what is the impact of different contexts on students' performance. Additionally, we studied students' self-regulation skills through the course and their demographic characteristics. In the end, we built and documented multi-dimensional comprehensive profiles of four categories of students. With the unprecedented increasing focus on online education in general and free online education in particular, our work has the potential to elucidate basic questions related to the educational effectiveness of Physics MOOCs, provide MOOCs creators with valuable findings that can inform their future course developments, and provide researchers on student learning with basic measures to evaluate the design of open online large enrollment courses. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]