The growth of on-line learning, massive open on-line courses (MOOCs), technology use within the classroom, and systems that track student behavior during – and sometimes outside of – learning activities has created an abundance of data that could, in principle, be used to improve student learning and retention, personalize instruction, inform decision-making by students and educators, and lead to the development of improved learning systems and experiences. Many challenges face those individuals and institutions that seek to leverage the mounds of data generated during the learning process into the desired benefits. Linking raw data and valid analysis to effective learning adaptations and educational interventions to improved outcomes remains an uncertain endeavor. This session invites contributions that advance our understanding of the use of “big data” to improve learning outcomes and educational processes.
Topics include, but are not limited to,
• learning analytics
• educational data mining
• evidence centered design
• adaptive learning systems
• data visualization
• personalized instruction systems
• student performance prediction