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An Associative Based Approach to Analyzing an Online Learning Environment

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

Emerging Computing and Information Technologies

Tagged Division

Computing & Information Technology

Page Count


Page Numbers

23.154.1 - 23.154.12



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Paper Authors


Bahareh Azarnoush Arizona State University

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BAHAREH AZARNOUSH is a PhD student at the School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, PO Box 878809, Tempe, AZ 85287-8809, Her research interest is statistical learning.

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Jennifer M Bekki Arizona State University

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Bianca L. Bernstein Arizona State University

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George C Runger Arizona State University

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An Associative Based Approach to Analyzing an Online Learning Environment Recent years have witnessed a tremendous proliferation of the World Wide Web. This haschanged the way we experience many aspects of everyday life including the ways we communi-cate, conduct business, and educate. Since its advent in the mid-1970s, great advances in educationdelivery through the Web have been made. Indeed, online learning environments (OLE) and effortstowards their improvement have become increasingly common. This paper presents two different approaches for analyzing OLE activity through the mining ofrules. These rules shed light into the users’ learning interests and needs and depict the relationshipbetween different components of the OLE and the resulting learning outcome. The approachesare demonstrated for the existing OLE, CareerWISE (, which is a sitedesigned to teach resilience skills to women students in STEM doctoral programs. The first approach involves the discovery of the correlation among different pages of the OLEwhich may be useful in recommending pages that are likely to be of interest to particular users.Rules in the form of {Px , Py , ...} −→ Pz where Pi denotes page i of the OLE are of interest. Suchrules may shed light on the users’ learning needs and interests, and this can be used in recommend-ing Pz to a user upon observing {Px , Py , ...} in his/her active session window. Also, there are typically a large set of navigation paths that can be taken in exploring any web-site. Since an OLE is usually designed with a specific goal of generating some learning outcomewithin the user, there will be some gain in discovering the navigation paths that are associatedwith achieving this goal. Using this intuition our second approach is based on the discovery of thecorrelation among the page visits and the score on a learning instrument. Such relationships arecaptured in rules in the form of visit to a subset o f pages → target = high. In this approach, it isassumed that an instrument that quantifies learning from the OLE is available and is taken afterexploring the OLE (much like a standard test in the usual educational system). Together, the application of these rules provides guidance on navigating the OLE and allowsusers to reach components of the OLE that may have not been reached otherwise. This, in turn,improves the OLE’s efficiency at achieving learning objectives among users. 1

Azarnoush, B., & Bekki, J. M., & Bernstein, B. L., & Runger, G. C. (2013, June), An Associative Based Approach to Analyzing an Online Learning Environment Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19168

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