Thesis (S.T.L.)--Catholic University of America, 1993. / Includes bibliographical references (leaves 89-92).

Thesis (Ph. D.)--University of Notre Dame, 2006. / Thesis directed by Michael DePaul and Alvin Plantinga for the Department of Philosophy. "April 2006." Includes bibliographical references (leaves 238-244).

This research examines how reading and writing on digital platforms establishes public and private spheres in Tokyo, Japan. Based upon findings from a group of students at an international University, I develop new modes of thinking about people and their use of Internet capable devices by exploring the paradoxes present in contemporary literacies. Contextualizing reading and writing within the speech patterns and exchange rituals (aisatsu) which mark public spheres in Japan, writing practices are found to reflect multiple nuanced identity performances in which the varied use of the cultural principles uchi/soto (inside/outside) and ura/omote (back/front) create parallel publics. Constructed by authors and recognized by readers, these parallel publics are the result of student agency as well as the materiality of platform programing and device capabilities. Contemporary literacies have developed conventions which account for the message recipient carrying an ever-present Internet capable device, leading authors to utilize message practices which align the proximity of a platform to levels of intimacy in a relationship. Authors also compose messages which are less likely to require the receiver to excuse themselves from any given social situation. The ubiquity of human-device pairs has also impacted memory practices, with youths prioritizing recognition skills over memorization.

The emergence of new trends sometimes carries the risk that established, well-proven concepts rooted in other disciplines are not properly integrated into new approaches. As Learning Analytics seems to be evolving into a highly multidisciplinary field, we would like to demonstrate the importance of embedding classic theories and concepts into a Learning Analytics, system-data-driven setting. Our results confirm that classical factors that are operationalized with the help of system-generated data outperform more recent survey-based models. Therefore, we want to stress the point that system-generated data should not be left behind in the quickly evolving field of Learning Analytics.