The application of big data approaches, specifically methods inspired from recommender system domain to predict student performance is largely a new area of research. The types of solutions available depend largely on the type of available data, and problem definition.
For instance, for the purposes of degree planning, one task is to predict grades for a student in a class in the future (or in the next term).
To predict student performance within individual class assessments, either in a typical classroom, an online, or a MOOC setting, students interaction with a learning management system (LMS) provides different ways to address this problem.
In this tutorial we will provide a formal definition of higher educational mining and discuss different opportunities for inter-disciplinary research in this field. Challenges as they relate to data acquisition and ethics will be discussed in detail followed with current practices surrounding the development of degree planning tools and course analytics to assist students and teachers.
Specific topics will include:
• Case Studies of Applications Related to Analytics in Higher Education.
• Specific Applications: Degree Planners, Early Warning Systems, Advisor Systems.
• Methods: Developing Grade Predictors using Recommender Systems and Regression Approaches.
• Envisioning an End-to-End System for Education Analytics.
• Challenges: Use Case and Data Ethics.
Huzefa Rangwala is an Associate Professor at the Department of Computer Science & Engineering, George Mason University. He received his Ph.D. in Computer Science from the University of Minnesota in the year 2008. His research interests include machine learning, learning analytics, bioinformatics and high performance computing. He is the recipient of the NSF Early Faculty Career Award in 2013, the 2014 GMU Teaching Excellence Award, the 2014 Mason Emerging Researcher Creator and Scholar Award, the 2013 Volgenau Outstanding Teaching Faculty Award, 2012 Computer Science Department Outstanding Teaching Faculty Award and 2011 Computer Science Department Outstanding Junior Researcher Award. His research is funded by NSF, NIH, NRL, DARPA, USDA and nVidia Corporation. The tutorial will present material from a combination of his own research as it relates to structured learning and multi-task learning. Recently, he developed a large scale hiearchical classifier using cost sensitive learning (HierCost) and the tutoral will discuss this publicly available package.
Aditya Johri is an Associate Professor in Information Sciences and Technology at George Mason University. He studies the use of information and communication technologies (ICT) for learning and knowledge sharing, with a focus on cognition in informal environments including online communities. He also examines the role of ICT in supporting distributed work among globally dispersed workers and in furthering social development in emerging economies. Johri is especially interested in understanding the relationship between our social and material context. His primary research has expanded from the examination of ethnographic data collection and considerable time observing and talking to people in the course of their daily work/life. Johri’s research has taken him into the classroom environment as well as into organizations where learning and collaboration are keys to success.
George Karypis is the ADC Chair of Digital Technology Center and a Professor at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests spans the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has resulted in the development of software libraries for serial and parallel graph partitioning (METIS and ParMETIS), hypergrap partitioning (hMETIS), for parallel Cholesky factorization (PSPASES), for collaborative filtering-based recommendation algorithms (SUGGEST), clustering high dimensional datasets (CLUTO), finding frequent patterns in diverse datasets (PAFI), and for protein secondary structure prediction (YASSPP). He has coauthored over 250 papers on these topics and two books (Introduction to Protein Structure Prediction: Method and Algorithms (Wiley, 2010) and Introduction to Parallel Computing (Publ. Addison Wesley, 2003, 2nd edition)). In addition, he is serving on the program committees of many conferences and workshops on these topics, and on the editorial boards of the IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Social Network Analysis and Data Mining Journal, International Journal of Data Mining and Bioinformatics, the journal on Current Proteomics, Advances in Bioinformatics, and Biomedicine and Biotechnology.
Asmaa Elbadrawy is a PhD candidate at the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. Her research interests include learning analytics, recommender systems, and the application of data-mining techniques within educational contexts. She has conducted research to address multiple problems including new item recommendation (also known as the cold-start recommendation problem), predicting students grades within course activities, and creating domain-aware recommendation techniques for predicting students grades in future courses and generating personalized course rankings given the students' academic backgrounds.
Tutorial Slides in PDF.