Project-based Learning in a Probabilistic Analysis Course in the CS Curriculum

This paper presents our experience in using a project-based learning approach in a senior-level class, which is not a traditional candidate for learning through projects in the CS curriculum. The objective of this class was to expose students to computer science applications of probabilistic analysis and stochastic processes. The class project expected the students to apply the concepts of Markov Chains constructed during in-class lectures to develop an understanding of the ranking algorithms used by the widely popular search giant, Google. These ranking algorithms sort web pages according to their relevance to a search query. Based on their understanding, the students were also expected to identify and document some strategies that can be employed by owners to improve the rank of their web sites. We found that our project, that guided the students through the four phases of project-based learning, led to unprecedented outcomes in the students' understanding of the subject. Not only did they apply the in-class knowledge to cultivate an understanding of a real-world problem and its solution, but they were also able to project strategies that were external to the class material. In the end-of-the-semester course evaluation, students commented that the project was very cool and well thought out, informally validating the value of our project-based approach to their learning.