intelligent database systems lab n.y.u.s.t. i. m. 1 mining lms data to develop an early warning...
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![Page 1: 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,](https://reader036.vdocuments.us/reader036/viewer/2022062517/56649f265503460f94c3de2e/html5/thumbnails/1.jpg)
1Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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
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2Intelligent Database Systems Lab
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2
Outline
Motivation
Objective
Data population and context
Experiments
Conclusion
Personal Comments
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3Intelligent Database Systems Lab
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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.
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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?
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Data population and context
The data is from University of British Columbia during 2008. Only students completing all coursework were included in the study ,
this resulted in a sample size of Nstudent = 118 completers.
Use the Blackboard PowerSight kit to access the server logs from BB VistaTM production server.
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6Intelligent Database Systems Lab
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Experiments
Simple correlations of LMS tracking variables with final grade
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7Intelligent Database Systems Lab
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Experiments
A simple correlation analysis of each variable with student final grade was undertaken.
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Experiments
Some students are making more effective strategic decisions about time use within the virtual classroom.
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9Intelligent Database Systems Lab
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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.
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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)
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Experiments
Use the network analysis of asynchronous discussion forums.
The C-grade in this course
The A-grade in this course
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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.
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Comments
Advantage Use Some Regression to generate information about
learning process.
Drawback Too many description in the paper.
Application Teaching / Learning strategies
Learning communities.