Knowledge Spaces Applications in Education Knowledge Spaces

Jean-Claude Falmagne · Dietrich Albert Christopher Doble · David Eppstein Xiangen Hu Editors Knowledge Spaces Applications in Education Knowledge Spaces Jean-Claude Falmagne • Dietrich Albert Christopher Doble • David Eppstein • Xiangen Hu Editors Knowledge Spaces Applications in Education Editors Jean-Claude Falmagne Dietrich Albert School of Social Sciences, Department of Psychology Dept. Cognitive Sciences University of Graz University of California, Irvine Graz, Austria Irvine, CA, USA Christopher Doble David Eppstein ALEKS Corporation Donald Bren School of Information Irvine, CA, USA & Computer Sciences University of California, Irvine Irvine, CA, USA Xiangen Hu Department of Psychology University of Memphis Memphis, TN, USA ISBN 978-3-642-35328-4 ISBN 978-3-642-35329-1 (eBook) DOI 10.1007/978-3-642-35329-1 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013942001 © Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface As can be gathered from the database maintained by Cord Hockemeyer at the University of Graz1, the literature on Knowledge Spaces is expanding rapidly. The recent book by Falmagne and Doignon (2011) gives a comprehensive account of the theory up to its date of publication. However there have been important developments after that. We thought that a volume gathering some of these developments would be timely. This volume has two parts. Part I describes a number of chosen empirical works. Part II deals with recent theoretical results. Two chapters play a special role. The first chapter in Part I, called ‘Overview’, gives an informal, intuitive presentation of all the important concepts of learning space theory, which is an important special case of knowledge space theory. This chapter is intended for readers primarily interested in the applications of learning space theory. Chapter 8, the first chapter in Part II, gives a technical presentation of the theory, including all the main theorems (without the proofs) and a description of some important algorithms. This chapter may serve as the basic reference for the works described in Part II. In Part I, Chapters 2 to 6 describe some applications of the ALEKS system in education. The ALEKS system is the most elaborate application of learning space theory to date. It is equipped with an assessment module and a learning module. This system is bilingual (English and Spanish) and currently covers all of K–12 mathematics2 (excluding calculus), and beginning chemistry. Us- ing extensive assessment and learning data, Chapter 2 investigates various statistical measures of the validity of the assessment and learning modules of the ALEKS system. Chapter 3 reports an application of the ALEKS system at the University of Illinois. The chapter demonstrates that the use of ALEKS as a placement test replacing the SAT and the ACT resulted in a substantial de- crease of F grades and withdrawals. Chapter 4 describes two studies evaluating whether online individualized instruction by the ALEKS system will increase student scores on a standardized high-stakes test. (The answer is ‘Yes.’) The students were elementary and middle school students of the Memphis area schools. The ALEKS system was used at the University of Memphis to teach a statistics course to students majoring in psychology or social sciences. Chapter 5 compares the results for black and white students in this course with those obtained in a traditional lecture type course. While the white students, on the 1See: http://liinwww.ira.uka.de/bibliography/Ai/knowledge.spaces.html. 2Some of these subjects are also used in colleges and universities. V VI Preface average, do much better than the black students in the lecture type course— which is consistent with traditional results—the discrepancy disappears in the online course. Chapter 6 describes an application of the ALEKS system to the teaching of General Chemistry to college students. The chapter presents a selection of learning and assessment data for the course, and gives interpreta- tions of those data in the framework of knowledge space theory. Chapter 7 analyzes the ability of students to make logical connections between fundamental chemical principles and the various representations of chemical phenomena. The ALEKS system is not used in this study. The authors build their own knowledge structures. As mentioned earlier, the introductory chapter of Part II, Chapter 8, gives a condensed description of the most important mathematical results. Distin- guishing between behavioral performance and its underlying skills and competencies, Chapters 9 and 10 deal with performance while Chapters 11 and 12 focus on competencies; all of them go well beyond Albert and Lukas (1999). Chapter 9 describes recent extensions in knowledge space theory (multiple answer alternatives, relations between sets/tests), relationships between knowledge space theory and other theoretical approaches (formal concept analysis, FCA; latent class analysis, LCA; item response theory, IRT) as well as meth- ods for data driven generation of knowledge structures, their empirical vali- dation (item tree analysis, ITA; inductive item tree analysis, IITA; measures and indices of t) and respective software resources. Methodological consider- ations and applications in Chapter 10 exemplify empirical research dealing with generating and validating knowledge structures for sets of items or tests. The different skill- and competence-oriented approaches have been developed independently. Thus, Chapter 11 for the first time relates systematically these approaches to each other by presenting a united framework which allows for identifying their commonalities and differences. These approaches are further developed in Chapter 12, which asks how to deal with distributed information, how to formulate a probabilistic approach, how to link observable navi- gation and problem solving behavior to cognitive and competence states, how to support self-regulated learning behavior, and how to assess competencies in educational games noninvasively. Furthermore, respective applications in technology enhanced learning and competence management are described. Chapters 13 and 14 describe a data structure, the learning sequence, that can be used to efficiently implement learning-space based systems. In Chap- ter 13, learning sequences are applied to the tasks of generating the states of a learning space and using learning spaces to assess the knowledge of a learner. Chapter 14 discusses the use of learning sequences to project learning spaces onto smaller sets of concepts (important for the efficiency of assessment in large learning spaces) and to modify learning spaces by adding or removing states. Preface VII We are most grateful to all the referees whose reports led to improvements of the presentation of the works described in this volume. We thank in par- ticular Eric Cosyn, Cornelia Dowling, Yung-Fong Hsu, Mathieu Koppen, Jeff Matayoshi, Alexander Nussbaumer, Martin Schrepp, Luca Stefanutti, Rein- hard Suck, and Nicolas Thiéry. We also thank Brian Junker and Don Laming for their useful reactions to a presentation of the material in Chapter 2. The Editors March 11, 2013. Contents Part I LEARNING IN A KNOWLEDGE SPACE 1Overview.................................................. 3 Jean-Claude Falmagne and Christopher Doble 1.1 Introduction............................................ 3 1.2 BasicConcepts:KnowledgeStatesandSpaces............... 5 1.3 UncoveringaKnowledgeState............................ 13 1.4 TheCaseofVeryLargeKnowledgeSpaces.................. 18 1.5 HistoricalNote.......................................... 21 1.6 AgraphofaLargerStructure ............................ 22 1.7 NontechnicalGlossary...................................

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