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Educators in many disciplines are too often forced to rely on intuition about how students learn and the effectiveness of teaching to guide changes and improvements to their curricula. In computer science, systems that perform automated collection and assessment of programming assignments are seeing increased adoption, and these systems generate a great deal of meaningful intermediate data and statistics during the grading process. Continuous collection of these data and long-term retention of collected data present educators with a new resource to assess both learning (how well students understand a topic or how they behave on assignments) and teaching (how effective a response, intervention, or assessment instrument was in evaluating knowledge or changing behavior), by basing their decisions on evidence rather than intuition. It is only possible to achieve these goals, however, if such data are easily accessible. I present an infrastructure that has been added to one such automated grading system, Web-CAT, in order to facilitate routine data collection and access while requiring very little added effort by instructors. Using this infrastructure, I present three case studies that serve as representative examples of educational questions that can be explored thoroughly using pre-existing data from required student work. The first case study examines student time management habits and finds that students perform better when they start earlier but that offering extra credit for finishing earlier did not encourage them to do so. The second case study evaluates a tool used to improve student understanding of manual memory management and finds that students made fewer errors when using the tool. The third case study evaluates the reference tests used to grade student code on a selected assignment and confirms that the tests are a suitable instrument for assessing student ability. In each case study, I use a data-driven, evidence-based approach spanning multiple semesters and students, allowing me to answer each question in greater detail than was possible using previous methods and giving me significantly increased confidence in my conclusions. Ph. D.

Automated grading systems have been researched at various universities for several years. Numerous systems have been developed that automate the process of grading by compiling, executing and testing the students submitted source code. However, such systems are mostly written as UNIX scripts and are restricted to performing one kind of activity. The instructors or teaching assistants have to resort to other methods in order to provide their feedback to the students. The core of this thesis is to research a TA feedback mechanism which will streamline the grading process for the professors and teaching assistants. A web-based grading tool has been developed that allows course staff to enter comments for students' programs directly through a web browser. The interface provides for full direct-manipulation editing of comments, which are then immediately viewable by students when they look up assignment results. Such an interface also has potential to support peer grading of assignments. Teaching assistants of introductory programming level courses were interviewed to learn about the different grading methods they use and were asked their opinion of our new grading interface. TAs were also asked to grade assignments using the traditional paper method as well as the computer using our new grading tool for comparison. Finally, an anonymous survey was sent out to various computer science faculties in different universities to gather information about the expectations they have with respect to TA grading activities for programming assignments and the learning outcomes that these professors desire for their students. Master of Science