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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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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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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
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: 30 Mar 2021 15:55


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