While many computer tutoring systems have long been delivered as desktop applications, these systems have only recently begun to appear on mobile devices. In this work we apply principles of mobile human computer interaction and mobile learning to the design and development of an intelligent tutoring system delivered on a personal digital assistant. We developed a proof-of-concept mobile tutor for business math and tested it in a course designed for first-year college students. Our experiences with the tutor suggest that a tutor based on principles of both mobile learning and mobile human computer interaction can provide a mobile tutoring system capable of providing support to students consistent with mobile device usage patterns.

Engineering education has evolved from providing students solely with technical skills to providing them with courses that provide students with the non-technical "soft skills". Among the soft skills desired by employers are student's ability to receive and respond to feedback, manage multiple requirements, and work in multidisciplinary teams. The use of Capstone courses in Computer Science education has been a means of providing students with the aforementioned soft skills and experiences prior to graduation. We define and implement a model for an educational video game design and development course that provides students with real-world game design experiences. Students learn the complexity of developing an educational game while functioning in multidisciplinary teams. Additionally, we provide students with an opportunity to visit K-12 schools to witness, first hand, the conditions in which their video games will be used. Finally, we present lessons learned and discuss methods for the successful development of multidisciplinary courses in Computer Science Education.

Cognitive modeling has evolved into a powerful tool for understanding and predicting user behavior. Higher-level modeling frameworks such as GOMS and its variants facilitate fast and easy model development but are sometimes limited in their ability to model detailed user behavior. Lower-level cognitive architectures such as EPIC, ACT-R, and Soar allow for greater precision and direct interaction with real-world systems but require significant modeling training and expertise. In this paper we present a modeling framework, ACT-Simple, that aims to combine the advantages of both approaches to cognitive modeling. ACT-Simple embodies a "compilation" approach in which a simple description language is compiled down to a core lower-level architecture (namely ACT-R). We present theoretical justification and empirical validation of the usefulness of the approach and framework.