In spite of the significant progress in technology for learning, today?s education systems remain largely ?one-size-fits-all?, ignoring the individuality of students and forcing them into artificial timelines for learning that often result in learners either becoming bored or falling behind. As a result, there has been considerable recent interest in personalized learning systems (PLS) such as instructional modules that enable students to learn at their own pace and interactive computer programs designed to respond to the learner?s questions.
While successful, PLSs have been extremely difficult to realize without major investments of time, money, and expertise. Moreover, recent studies indicate that they do not always facilitate improved learning. In this presentation, we will discuss how openness enables new ways and means to advance personalize learning. In particular, we will discuss how OpenStax Tutor, a collaboration between engineers and cognitive scientists at Rice University and Duke University, improves learning by fusing cognitive science learning strategies and modern machine learning algorithms; the result is an automated, personalized, and optimized learning experience for today?s courses and students.
OpenStax Tutor marshals many different open educational resource (OER) sources in its quest to improve student learning, but two repositories stand out: Connexions (cnx.org) for rich e-texts and Quadbase (quadbase.org) for assessments. Connexions is one of the world?s first and largest OER projects. Connexions? repository of free, open-source educational content is accessible to students, instructors, and authors worldwide. Quadbase is an open access question bank, focused on serving instructors and educational platforms with support for multiple question types and embedding options. Both platforms thrive on community-submitted and -curated content, and access remains free to all under a Creative Commons attribution license.
