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- W2396716007 abstract "University professors of conventional offline classes are often experts in their research fields, but have little training on educational sciences. Current educational data mining techniques offer little support to them. In this paper we propose a novel algorithm, Analyzing CurrIculum Decisions (ACID), that leverages collective intelligence to model student opinions to help instructors of traditional classes. ACID mines publicly available educational websites, such as student ratings 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 introductory courses, the grading criteria and the posting of a publicly available online syllabus." @default.
- W2396716007 created "2016-06-24" @default.
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- W2396716007 date "2014-01-01" @default.
- W2396716007 modified "2023-09-26" @default.
- W2396716007 title "Mining the Web to Leverage Collective Intelligence and Learn Student Preferences." @default.
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