The classes I teach have a predictive modeling component. As a student, having participated in blind protein structure prediction competitions (CASP, http://predictioncenter.org) and data mining competitions like KDD Cup, I have implemented this form of competitions in my bioinformatics and data mining classes. This semester I extended this idea to a different class (Parallel Computing). Specifically, as part of an assignment (or final project) students have to train a predictive models to distinguish a specific class of proteins called "solenoids" using the available protein sequence information. As part of this competition, the truth-values are hidden from the students and they have to make a prediction (guess) and submit their results to the instructor. The instructor then evaluates the results using the truth-values and provides a ranking of the class students based on the predictive performance. The concepts introduced in class allow the students to build base line predictive models, but to improve performance, students have to research, think critically and come up with innovative solutions. In my past two implementations of this project, I have used an in-house evaluation script and requested participants to send me solutions via a simple web server. Both times, the assignment was run for a 4-week period. I have also used technologies like Kaggle to setup this competition. In the future, I would like to implement the competition for the duration of the semester. Students would be taught a concept, and they would implement the same towards improving their predictive models and engineer better solutions as new, advanced concepts are taught.I am also developing a model that allows students to achieve these projects in a collaborative fashion by enabling resources like Wikis and other tools. As such, this session will be an introduction to the tools used and how they could be adapted to general purpose classes