Data models built for analyzing student data often obfuscate temporal relationships for reasons of simplicity, or to aid in generalization. We present a model based on temporal relationships of heterogeneous data as the basis for building predictive models. We show how within- and between-semester temporal patterns can provide insight into the student experience. For example, in a within-semester model, the prediction of the final course grade can be based on weekly activities and submissions recorded in the LMS. In the between-semester model, the prediction of success or failure in a degree program can be based on sequence patterns of grades and activities across multiple semesters. The benefits of our sequence data model include temporal structure, segmentation, contextualization, and storytelling. To demonstrate these benefits, we have collected and analyzed 10 years of student data from the College of Computing at UNC Charlotte in a between-semester sequence model, and used data in an introductory course in computer science to build a within-semester sequence model. Our results for the two sequence models show that analytics based on the sequence data model can achieve higher predictive accuracy than non-temporal models with the same data.
Stephen's research focuses on how people collaboratively make sense of complex, 'wicked' problems. Wicked problems are dynamic and constantly changing. They involve multiple stakeholders, often with conflicting requirements. To address these challenges, Stephen develops sociotechnical systems that collect, organize, and use data to support reflection and collective action. He received his Ph.D. at UNC in Charlotte and is currently a postdoctoral researcher in the Design Lab at UC San Diego.Dr. Mohsen M Dorodchi, UNC, Charlotte Dr. Dorodchi has been teaching in the field of computing for over 30 years of which 20 years as educator. He has taught majority of the courses in the computer science and engineering curriculum over the past 20 years such as introductory programming, data structures, databases, software engineering, system programming, etc. He is involved in multiple NSF supported research projects including Learning and Predictive Analytics Research, Research Practitioner Partnership, Implementing Teaching Methods to help Students learn more efficiently in active learning, etc.
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