Mobile intelligent tutoring system: moving intelligent tutoring systems off the desktop

The prominence of computers in the 21st Century has caused educators to reexamine the needs of today's K–12 students. The proposed 21st Century Skills center on the use of technology and thus millions of students are unable fully to master these skills due to their minimal access to technology. The Digital Divide, a correlation between an individual's access to technology and their socio-economic status, is poised to prevent large segments of society from advancing and thriving in this technology based economy. One proposed solution to this lack of computing resources lay in the transformation of mobile devices, like cell phones, from single-purpose communication tools to multipurpose computing resources. The smaller scale of mobile devices mandates the design of mobile learning applications be more than miniature desktop learning applications. Whether the applications are based on existing desktop applications or presenting new paradigms of learning activities there are human-computer-interaction and education concerns to address. This thesis describes the design, implementation, and evaluation of a complete mobile intelligent tutoring system (ITS). The research questions investigated address the design and evaluation of the implemented system. To answer first question, “How might the design of an ITS be adapted for delivery on a mobile device?” a set of general mobile ITS guidelines is described. To answer the second question, “Can a mobile ITS provide learning gains greater than traditional instructional activities?” data was gathered from a controlled study to compare the performance of students using the mobile ITS with those who did not. These data reveal that students using the mobile ITS achieved gains greater than students receiving standard instruction. The third question, “What teaching strategy best supports a mobile ITS?”, is answered through data gathered from the comparison of two teaching strategies, long and short. These data suggest that the long strategy, in which students are required to calculate the final answer to the problem, best facilitates student performance gains. This thesis lays the foundation for future research that explores the delivery of intelligent tutoring systems off the desktop as well as for research in methods of transforming desktop learning applications for mobile devices.