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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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Outline

Motivation

Objective

Data population and context

Experiments

Conclusion

Personal Comments

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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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Experiments

Simple correlations of LMS tracking variables with final grade

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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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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.