Mining the Web to Leverage Collective Intelligence and Learn Student Preferences.
Antonio Khalil Moretti, José P. González-Brenes, Katherine McKnight · 2014
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.