Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Mining LMS data to develop an early warning system for educators : A proof of concept Presenter : Wu,

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Presentation transcript:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Mining LMS data to develop an early warning system for educators : A proof of concept Presenter : Wu, Jia-Hao Authors : Leah P. Macfadyen, Shane Dawson CE (2010) 國立雲林科技大學 National Yunlin University of Science and Technology

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 22 Outline Motivation Objective Data population and context Experiments Conclusion Personal Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation Some studies have suggested that higher education institutions could harness the predictive power  They use the Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. Internet and communication technology (ICT) integration into teaching and learning  Most LMSs are web-based platforms that bring together tools and materials to support learning.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective Use some regression model and NetDraw to identify the data variables that would inform the development of a data visualization tool for instructors. The Research questions  Which LMS tracking data variables correlate significantly with student achievement?  How accurately can measures of student online activity predict student achievement in the course under study?  Can tracking data offer pedagogically meaningful insights into development of a student learning community?

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Data population and context The data is from University of British Columbia during  Only students completing all coursework were included in the study, this resulted in a sample size of N student = 118 completers. Use the Blackboard PowerSight kit to access the server logs from BB Vista TM production server.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Experiments Simple correlations of LMS tracking variables with final grade

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Experiments A simple correlation analysis of each variable with student final grade was undertaken.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Experiments Some students are making more effective strategic decisions about time use within the virtual classroom.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Experiments Use a linear multiple regression analysis and logistic regression analysis.  A predictive model of student final grade.  A linear combination of the LMS tracking data variables measuring only three online activities.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experiments -Logistic regression model In the UBC grading scheme, <60% represents a grade of C- or poorer ; < 50% is considered a failing grade. 15 ( only four actually failed the course ) Predictive failure rate of only 3.4% (4/118)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Experiments Use the network analysis of asynchronous discussion forums. The C-grade in this course The A-grade in this course

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Conclusion The authors use regression model of student success, developed using tracking variables relevant to the instructors’ intentions and to online course website design. The network analysis have demonstrated that robust and diverse peer networks are an important influencing factor on student study persistence and overall academic success.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Comments Advantage  Use Some Regression to generate information about learning process. Drawback  Too many description in the paper. Application  Teaching / Learning strategies  Learning communities.