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In this paper, we describe experiments conducted on identifying a person using a novel unique correlated corpus of text and audio samples of the person's communication in six genres. The text samples include essays, emails, blogs, and chat. Audio samples were collected from individual interviews and group discussions and then transcribed to text. For each genre, samples were collected for six topics. We show that we can identify the communicant with an accuracy of 71% for six fold cross validation using an average of 22,000 words per individual across the six genres. For person identification in a particular genre (train on five genres, test on one), an average accuracy of 82% is achieved. For identification from topics (train on five topics, test on one), an average accuracy of 94% is achieved. We also report results on identifying a person's communication in a genre using text genres only as well as audio genres only.

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An experiment in collaborative learning was conducted in two introductory programming courses at Loyola College in Maryland. Data collected included background information on students; course evaluations; and before and after measures on programming knowledge and attitudes.The collaborative learning class showed more improvement pre-test to post-test than did the control class and rated the course somewhat higher. Attitudes of both groups towards the field of computing and towards the value of group discussion in class were more resistant to change.

A genetic algorithm is used to search for linear binary codes with optimal minimum distance for a fixed length n and dimension k. Several modifications to the algorithm are compared to find an algorithm best suited to this application. The code is parallelized and mn on a multi-processor and speedup determined.