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Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction

Sahebi, Shaghayegh and Huang, Yun and Brusilovsky, Peter (2014) Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction. In: The second Workshop on AI-supported Education for Computer Science (AIEDCS), 06 June 2014 - 06 June 2014, Honolulu, Hawaii.

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Abstract

In this paper, we study the effect of using domain knowledge structure on predicting student performance with parameterized Java programming exercises. Domain knowledge structure defines connections between elementary knowledge items. While known to be beneficial in general, it has not been used to predict performance.We compare five different approaches for this purpose: Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), and three dimensional Bayesian Probabilistic Tensor Factorization (3D-BPTF), that are not able to take into account knowledge structure; and four-dimensional Bayesian Probabilistic Tensor Factorization (4D-BPTF) and Feature-Aware Student Knowledge Tracing (FAST), that can take into account knowledge structure. We approach the problem using both topic-level and question-level Knowledge Components (KCs) and test the methods on a dataset of parameterized questions. Our work is the first in the field that models students’ behavior in a four dimensional tensor. Our experiments show that, when having only the knowledge-item-level information, all of the models work similarly in predicting student performance, but adding the topic-level information that integrates knowledge items changes the performance of these models in different directions.

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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sahebi, Shaghayeghshs106@pitt.eduSHS106
Huang, Yunyuh43@pitt.eduYUH43
Brusilovsky, Peterpeterb@pitt.eduPETERB0000-0002-1902-1464
Date: June 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: The second Workshop on AI-supported Education for Computer Science (AIEDCS)
Page Range: 61 - 70
Event Title: The second Workshop on AI-supported Education for Computer Science (AIEDCS)
Event Dates: 06 June 2014 - 06 June 2014
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 19 Jun 2014 14:44
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/21915

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