Review: Student modeling approaches: A literature review for the last decade
TL;DR: One significant conclusion is that the most preferred technique for representing the student's mastery of knowledge is the overlay approach, and stereotyping seems to be ideal for modeling students' learning styles and preferences.
Abstract: This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10years are described, the aspects of students' characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student's mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students' learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.
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Journal Article•
TL;DR: An overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning is presented and thoughts on possible uncharted key questions to investigate are set.
Abstract: This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008-2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.
507 citations
Cites background from "Review: Student modeling approaches..."
...Updated and extended review on student modeling approaches can be found in Chrysafiadi and Virvou (2013) and in Pena-Ayala (2014)....
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TL;DR: The book of handbook of individual differences learning and instruction, as an amazing reference becomes what you need to get as discussed by the authors, as a source that may involve the facts, opinion, literature, religion and many others are the great friends to join with.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of handbook of individual differences learning and instruction, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.
402 citations
TL;DR: This study has studied various tasks and applications existing in the field of EDM and categorized them based on their purposes, reported a taxonomy of task and compared this study with other existing surveys about EDM.
Abstract: Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.
184 citations
Cites background from "Review: Student modeling approaches..."
...…as analyzing the student’s performance or behaviour, isolating underlying misconceptions, representing students’ goals and plans, identifying prior and acquired knowledge, maintaining an episodic memory and describing personality characteristics (Self n.d.; Chrysafiadi and Virvou 2013)....
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...Based on the literature review in 2013 (Chrysafiadi and Virvou 2013), there are different characteristics in student modelling, namely, knowledge and skills (1), errors and misconceptions (2), learning styles and preferences (3), affective and cognitive factors (4) and meta-cognitive factors (5)....
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...Student modelling is a process devoted to representing cognitive aspects of student activities,such as analyzing the student’s performance or behaviour, isolating underlying misconceptions, representing students’ goals and plans, identifying prior and acquired knowledge, maintaining an episodic memory and describing personality characteristics (Self n.d.; Chrysafiadi and Virvou 2013)....
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16 May 2001
TL;DR: The first assignment of the New Chalk Talk series as discussed by the authors was to discover the learning styles of the students in a Microeconomics class at the University of Sheffield and the results of a questionnaire on learning styles were given to the students.
Abstract: It has come to our attention that Dr Herb Thompson, professor of Economics and Chair of the Economics department, devotes his first assignment to discovering exactly that. He asks his students to fill an on-line questionnaire on Learning Styles and to hand him the results of this questionnaire. We have asked him to write a piece on “learning styles” for our New Chalk Talk series. We have also asked him to share the results of the questionnaires which he has conducted over two semesters in his Microeconomics class. Dr Thompson has agreed to do this and for that we thank him very much. New Chalk Talk will devote the next two or three issues to this subject. They will appear starting next Tuesday.
143 citations
TL;DR: A systematic and up-to-date overview of current approaches to tracing learners’ knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models.
Abstract: Learner modeling is a basis of personalized, adaptive learning The research literature provides a wide range of modeling approaches, but it does not provide guidance for choosing a model suitable for a particular situation We provide a systematic and up-to-date overview of current approaches to tracing learners' knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models We discuss factors that influence the choice of a model and highlight the importance of the learner modeling context: models are used for different purposes and deal with different types of learning processes We also consider methodological issues in the evaluation of learner models and their relation to the modeling context Overall, the overview provides basic guidelines for both researchers and practitioners and identifies areas that require further clarification in future research
129 citations
Cites background or methods from "Review: Student modeling approaches..."
...Although overviews of learner modeling are available in previous works (Desmarais and Baker, 2012; Chrysafiadi and Virvou, 2013; Pavlik et al., 2013), this overview contributes in several directions:...
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...Several previous overview papers include a wide discussion of different types of learner model, see for example Desmarais and Baker (2012), Chrysafiadi and Virvou (2013), Nakic et al. (2015), Valdés Aguirre et al. (2016) and Pavlik et al. (2013)....
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References
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Book•
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01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
Book•
01 Jan 1988TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
15,671 citations
Book•
01 Mar 1981
TL;DR: In this paper, the authors present a classification of MADM methods by data type and propose a ranking method based on the degree of similarity of the MADM method to the original MADM algorithm.
Abstract: I. Introduction.- II. Multiple Attribute Decision Making - An Overview.- 2.1 Basics and Concepts.- 2.2 Classifications of MADM Methods.- 2.2.1 Classification by Information.- 2.2.2 Classification by Solution Aimed At.- 2.2.3 Classification by Data Type.- 2.3 Description of MADM Methods.- Method (1): DOMINANCE.- Method (2): MAXIMIN.- Method (3): MAXIMAX.- Method (4): CONJUNCTIVE METHOD.- Method (5): DISJUNCTIVE METHOD.- Method (6): LEXICOGRAPHIC METHOD.- Method (7): LEXICOGRAPHIC SEMIORDER METHOD.- Method (8): ELIMINATION BY ASPECTS (EBA).- Method (9): LINEAR ASSIGNMENT METHOD (LAM).- Method (10): SIMPLE ADDITIVE WEIGHTING METHOD (SAW).- Method (11): ELECTRE (Elimination et Choice Translating Reality).- Method (12): TOPSIS (Technique for Order Preference by Similarity to Ideal Solution).- Method (13): WEIGHTED PRODUCT METHOD.- Method (14): DISTANCE FROM TARGET METHOD.- III. Fuzzy Sets and their Operations.- 3.1 Introduction.- 3.2 Basics of Fuzzy Sets.- 3.2.1 Definition of a Fuzzy Set.- 3.2.2 Basic Concepts of Fuzzy Sets.- 3.2.2.1 Complement of a Fuzzy Set.- 3.2.2.2 Support of a Fuzzy Set.- 3.2.2.3 ?-cut of a Fuzzy Set.- 3.2.2.4 Convexity of a Fuzzy Set.- 3.2.2.5 Normality of a Fuzzy Set.- 3.2.2.6 Cardinality of a Fuzzy Set.- 3.2.2.7 The mth Power of a Fuzzy Set.- 3.3 Set-Theoretic Operations with Fuzzy Sets.- 3.3.1 No Compensation Operators.- 3.3.1.1 The Min Operator.- 3.3.2 Compensation-Min Operators.- 3.3.2.1 Algebraic Product.- 3.3.2.2 Bounded Product.- 3.3.2.3 Hamacher's Min Operator.- 3.3.2.4 Yager's Min Operator.- 3.3.2.5 Dubois and Prade's Min Operator.- 3.3.3 Full Compensation Operators.- 3.3.3.1 The Max Operator.- 3.3.4 Compensation-Max Operators.- 3.3.4.1 Algebraic Sum.- 3.3.4.2 Bounded Sum.- 3.3.4.3 Hamacher's Max Operator.- 3.3.4.4 Yager's Max Operator.- 3.3.4.5 Dubois and Prade's Max Operator.- 3.3.5 General Compensation Operators.- 3.3.5.1 Zimmermann and Zysno's ? Operator.- 3.3.6 Selecting Appropriate Operators.- 3.4 The Extension Principle and Fuzzy Arithmetics.- 3.4.1 The Extension Principle.- 3.4.2 Fuzzy Arithmetics.- 3.4.2.1 Fuzzy Number.- 3.4.2.2 Addition of Fuzzy Numbers.- 3.4.2.3 Subtraction of Fuzzy Numbers.- 3.4.2.4 Multiplication of Fuzzy Numbers.- 3.4.2.5 Division of Fuzzy Numbers.- 3.4.2.6 Fuzzy Max and Fuzzy Min.- 3.4.3 Special Fuzzy Numbers.- 3.4.3.1 L-R Fuzzy Number.- 3.4.3.2 Triangular (or Trapezoidal) Fuzzy Number.- 3.4.3.3 Proof of Formulas.- 3.4.3.3.1 The Image of Fuzzy Number N.- 3.4.3.3.2 The Inverse of Fuzzy Number N.- 3.4.3.3.3 Addition and Subtraction.- 3.4.3.3.4 Multiplication and Division.- 3.5 Conclusions.- IV. Fuzzy Ranking Methods.- 4.1 Introduction.- 4.2 Ranking Using Degree of Optimality.- 4.2.1 Baas and Kwakernaak's Approach.- 4.2.2 Watson et al.'s Approach.- 4.2.3 Baldwin and Guild's Approach.- 4.3 Ranking Using Hamming Distance.- 4.3.1 Yager's Approach.- 4.3.2 Kerre's Approach.- 4.3.3 Nakamura's Approach.- 4.3.4 Kolodziejczyk's Approach.- 4.4 Ranking Using ?-Cuts.- 4.4.1 Adamo's Approach.- 4.4.2 Buckley and Chanas' Approach.- 4.4.3 Mabuchi's Approach.- 4.5 Ranking Using Comparison Function.- 4.5.1 Dubois and Prade's Approach.- 4.5.2 Tsukamoto et al.'s Approach.- 4.5.3 Delgado et al.'s Approach.- 4.6 Ranking Using Fuzzy Mean and Spread.- 4.6.1 Lee and Li's Approach.- 4.7 Ranking Using Proportion to The Ideal.- 4.7.1 McCahone's Approach.- 4.8 Ranking Using Left and Right Scores.- 4.8.1 Jain's Approach.- 4.8.2 Chen's Approach.- 4.8.3 Chen and Hwang's Approach.- 4.9 Ranking with Centroid Index.- 4.9.1 Yager's Centroid Index.- 4.9.2 Murakami et al.'s Approach.- 4.10 Ranking Using Area Measurement.- 4.10.1 Yager's Approach.- 4.11 Linguistic Ranking Methods.- 4.11.1 Efstathiou and Tong's Approach.- 4.11.2 Tong and Bonissone's Approach.- V. Fuzzy Multiple Attribute Decision Making Methods.- 5.1 Introduction.- 5.2 Fuzzy Simple Additive Weighting Methods.- 5.2.1 Baas and Kwakernaak's Approach.- 5.2.2 Kwakernaak's Approach.- 5.2.3 Dubois and Prade's Approach.- 5.2.4 Cheng and McInnis's Approach.- 5.2.5 Bonissone's Approach.- 5.3 Analytic Hierarchical Process (AHP) Methods.- 5.3.1 Saaty's AHP Approach.- 5.3.2 Laarhoven and Pedrycz's Approach.- 5.3.3 Buckley's Approach.- 5.4 Fuzzy Conjunctive/Disjunctive Method.- 5.4.1 Dubois, Prade, and Testemale's Approach.- 5.5 Heuristic MAUF Approach.- 5.6 Negi's Approach.- 5.7 Fuzzy Outranking Methods.- 5.7.1 Roy's Approach.- 5.7.2 Siskos et al.'s Approach.- 5.7.3 Brans et al.'s Approach.- 5.7.4 Takeda's Approach.- 5.8 Maximin Methods.- 5.8.1 Gellman and Zadeh's Approach.- 5.8.2 Yager's Approach.- 5.9 A New Approach to Fuzzy MADM Problems.- 5.9.1 Converting Linguistic Terms to Fuzzy Numbers.- 5.9.2 Converting Fuzzy Numbers to Crisp Scores.- 5.9.3 The Algorithm.- VI. Concluding Remarks.- 6.1 MADM Problems and Fuzzy Sets.- 6.2 On Existing MADM Solution Methods.- 6.2.1 Classical Methods for MADM Problems.- 6.2.2 Fuzzy Methods for MADM Problems.- 6.2.2.1 Fuzzy Ranking Methods.- 6.2.2.2 Fuzzy MADM Methods.- 6.3 Critiques of the Existing Fuzzy Methods.- 6.3.1 Size of Problem.- 6.3.2 Fuzzy vs. Crisp Data.- 6.4 A New Approach to Fuzzy MADM Problem Solving.- 6.4.1 Semantic Modeling of Linguistic Terms.- 6.4.2 Fuzzy Scoring System.- 6.4.3 The Solution.- 6.4.4 The Advantages of the New Approach.- 6.5 Other Multiple Criteria Decision Making Methods.- 6.5.1 Multiple Objective Decision Making Methods.- 6.5.2 Methods of Group Decision Making under Multiple Criteria.- 6.5.2.1 Social Choice Theory.- 6.5.2.2 Experts Judgement/Group Participation.- 6.5.2.3 Game Theory.- 6.6 On Future Studies.- 6.6.1 Semantics of Linguistic Terms.- 6.6.2 Fuzzy Ranking Methods.- 6.6.3 Fuzzy MADM Methods.- 6.6.4 MADM Expert Decision Support Systems.- VII. Bibliography.
8,629 citations
01 Jan 1988
TL;DR: A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages.
Abstract: When Linda Silverman and I wrote this paper in 1987, our goal was to offer some insights about teaching and learning based on Dr. Silverman’s expertise in educational psychology and my experience in engineering education that would be helpful to some of my fellow engineering professors. When the paper was published early in 1988, the response was astonishing. Almost immediately, reprint requests flooded in from all over the world. The paper started to be cited in the engineering education literature, then in the general science education literature; it was the first article cited in the premier issue of ERIC’s National Teaching and Learning Forum; and it was the most frequently cited paper in articles published in the Journal of Engineering Education over a 10-year period. A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages that I know about and probably more that I don’t, even though it has not yet been validated. The 1988 paper is still cited more than any other paper I have written, including more recent papers on learning styles.
5,195 citations
Book•
29 Jul 1988TL;DR: In this paper, a cognitive theory of emotion is proposed, which describes the organization of emotion types and the implications of the emotions-as-valenced-reactions claim, and the boundaries of the theory Emotion words and cross-cultural issues.
Abstract: 1. Introduction The study of emotion Types of evidence for theories of emotion Some goals for a cognitive theory of emotion 2. Structure of the theory The organisation of emotion types Basic emotions Some implications of the emotions-as-valenced-reactions claim 3. The cognitive psychology of appraisal The appraisal structure Central intensity variables 4. The intensity of emotions Global variables Local variables Variable-values, variable-weights, and emotion thresholds 5. Reactions to events: I. The well-being emotions Loss emotions and fine-grained analyses The fortunes-of-others emotions Self-pity and related states 6. Reactions to events: II. The prospect-based emotions Shock and pleasant surprise Some interrelationships between prospect-based emotions Suspense, resignation, hopelessness, and other related states 7. Reactions to agents The attribution emotions Gratitude, anger, and some other compound emotions 8. Reactions to objects The attraction emotions Fine-grained analyses and emotion sequences 9. The boundaries of the theory Emotion words and cross-cultural issues Emotion experiences and unconscious emotions Coping and the function of emotions Computational tractability.
4,942 citations