Mining the Web to Leverage Collective Intelligence and Learn Student Preferences

University professors of conventional offline classes are often experts in their research fields, but have little training on educational sciences. Current educational data mining tech-niques offer little support to them. In this paper we propose a novel algorithm, Analyzing CurrIculum Decisions (ACID), that leverages collective intelligence to model student opin-ions to help instructors of traditional classes. ACID mines publicly available educational websites, such as student rat-ings of professors and course information, and learns student opinions within a statistical framework. We demonstrate ACID to discover patterns in learner feedback and factors that affect Computer Science instruction. Specifically, we investigate the choice of a programming language for intro-ductory courses, the grading criteria and the posting of a publicly available online syllabus