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Knowledge Maximizer: Concept-Based Adaptive Problem Sequencing for Exam Preparation

Hosseini, Roya and Brusilovsky, Peter and Guerra, Julio (2013) Knowledge Maximizer: Concept-Based Adaptive Problem Sequencing for Exam Preparation. In: Artificial Intelligence in Education.

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Abstract

To support introductory Java programming students in preparing for their exams, we developed Knowledge Maximizer as a concept-based problem sequencing tool that considers a fine-grained concept-level model of student knowledge accumulated over the semester and attempts to bridge the possible knowledge gaps in the most efficient way. This paper presents the sequencing approach behind the Knowledge Maximizer and its classroom evaluation.


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hosseini, Royaroh38@pitt.eduROH38
Brusilovsky, Peterpeterb@pitt.eduPETERB0000-0002-1902-1464
Guerra, Julio
Date: January 2013
Date Type: Publication
Journal or Publication Title: Artificial Intelligence in Education
Volume: 7926
Publisher: Springer Berlin Heidelberg
Page Range: 848 - 851
Event Title: Artificial Intelligence in Education
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-39112-5_128
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 19 Jun 2014 14:46
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/21830

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