Novice programmers sometimes trace code incorrectly due to misconceptions. However, tracing errors do not always reflect flawed mental models, since they can also be caused by cognitive slips. Instructors may wish to respond differently to errors caused by slips rather than misconceptions, but these errors can be difficult to tell apart. This paper describes one instructor's search for an effective way to detect slips. Asking students to sketch or explain their traces seems insufficient, but engaging students in analyzing their own errors shows more promise.

This paper discusses the Turing Test as an educational activity for undergraduate students. It describes in detail an experiment that we conducted in a first-year non-CS course. We also suggest other pedagogical purposes that the Turing Test could serve.

This article introduces a teacher–student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

We present a framework for knowledge transfer from one reinforcement learning task to a related task through advice-taking mechanisms. We discuss the importance of transfer in complex domains such as RoboCup soccer, and show how to use automatically generated advice to perform transfer.