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Integrating knowledge tracing and item response theory: A tale of two frameworks

Khajah, MM and Huang, Y and González-Brenes, JP and Mozer, MC and Brusilovsky, P (2014) Integrating knowledge tracing and item response theory: A tale of two frameworks. In: UNSPECIFIED.

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Abstract

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Khajah, MM
Huang, Yyuh43@pitt.eduYUH43
González-Brenes, JP
Mozer, MC
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: CEUR Workshop Proceedings
Volume: 1181
Page Range: 7 - 15
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISSN: 1613-0073
Date Deposited: 24 Aug 2015 14:45
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/26044

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