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Challenges of using observational data to determine the importance of example usage

Huang, Y and González-Brenes, JP and Brusilovsky, P (2015) Challenges of using observational data to determine the importance of example usage. In: UNSPECIFIED.

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Abstract

© Springer International Publishing Switzerland 2015. Educational interventions are often evaluated with randomized control trials, which can be very expensive to conduct. One of the promises of “Big Data” in education is to use non-experimental data to discover insights. We focus on studying the impact of example usage in a Java programming tutoring system using observational data. For this, we compare different formulations of a recently proposed generalized Knowledge Tracing framework called FAST. We discover that different formulations can have the same predictive performance; yet their coefficients may have opposite signs, which may lead researchers to contradictory conclusions. We discuss implications of using fully data-driven approaches to study non-experimental data.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Huang, Yyuh43@pitt.eduYUH43
González-Brenes, JP
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 9112
Page Range: 633 - 637
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-319-19773-9_79
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 9783319197722
ISSN: 0302-9743
Date Deposited: 27 Aug 2015 19:03
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/26056

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