1Brynjolfsson, E. Erik Brynjolfsson on Big Data: A Revolution in Decision-Making Improves Productivity [EB/OL]. http://mitsloanexperts. mit.edu/erik -brynjolfsson -on -big -data -a -revolution -in -decision - making-improves-productivity/.
2Schleicher, A. Big Data and PISA [EB/OL]. http://www. huffingtonpost.Com/andreas -schleicher/big -data -and -pisa_b_3633558. html.
4Lohr, S. The Age of Big Data [N]. New York Times, 2012-2-11 (10).
5Grafsgaard, J., K. Boyer & J. Lester. "Toward a Machine Learning Framework for Understanding Affective Tutorial Interaction" [A]. In Proceedings of the 1 lth International Conference on Intelligent Tutoring Systems, 2012,52-58.
6Bienkowski, M., M. Feng & B. Means. Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief [M]. Washington, DC:Office of Educational Technology, U. S. Department of Education,2012.
7Corbett, A. & J. Anderson. Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge [J]. User Modeling and User- Adapted Interaction, 1995,4,253-278.
8Calvani, A.,A. Fini, M. Molino, & M. Ranieri. Visualizing and monitoring effective interactions in online collaborative groups. British Journal of Educational Technology,2009,41 (2), 213-226.
9Beck, J & J. Mostow. "'How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students" [A]. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 2008,353 - 362.
10Hamilton, L., R. Halverson, S. Jackson, E. Mandinach,.J. Snpovitz, & J. Wayman. Using student achievement data to support instructional decision making [M]. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education,2009.
