Learning With Concept and Knowledge Maps: A Meta-Analysis
01 Jan 2006-Review of Educational Research (Sage PublicationsSage CA: Thousand Oaks, CA)-Vol. 76, Iss: 3, pp 413-448
TL;DR: In this article, a meta-analysis review of experimental and quasi-experimental studies in which students learned by constructing, modifying, or viewing node-link diagrams was conducted, and 67 standardized mean difference effect sizes were extracted from 55 studies involving 5,818 participants.
Abstract: This meta-analysis reviews experimental and quasi-experimental studies in which students learned by constructing, modifying, or viewing node-link diagrams. Following an exhaustive search for studies meeting specified design criteria, 67 standardized mean difference effect sizes were extracted from 55 studies involving 5,818 participants. Students at levels ranging from Grade 4 to postsecondary used concept maps to learn in domains such as science, psychology, statistics, and nursing. Posttests measured recall and transfer. Across several instructional conditions, settings, and methodological features, the use of concept maps was associated with increased knowledge retention. Mean effect sizes varied from small to large depending on how concept maps were used and on the type of comparison treatment. Significant heterogeneity was found in most subsets.
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19 Nov 2008TL;DR: This meta-analyses presents a meta-analysis of the contributions from the home, the school, and the curricula to create a picture of visible teaching and visible learning in the post-modern world.
Abstract: Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References
6,776 citations
01 Jan 2001
TL;DR: A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers.
Abstract: A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.
2,940 citations
TL;DR: The ICAP hypothesis as discussed by the authors predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase and suggest possible knowledge-change processes that support the hypothesis.
Abstract: This article describes the ICAP framework that defines cognitive engagement activities on the basis of students’ overt behaviors and proposes that engagement behaviors can be categorized and differentiated into one of four modes: Interactive, Constructive, Active, and Passive. The ICAP hypothesis predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase. We suggest possible knowledge-change processes that support the ICAP hypothesis and address the limitations and caveats of the hypothesis. In addition, empirical validation for the hypothesis is provided by examining laboratory and classroom studies that focus on three specific engagement activities: note taking, concept mapping and self-explaining. We also consider how ICAP can be used as a tool for explaining discrepant findings, dictate the proper choice of a control condition, and evaluate students’ outputs. Finally, we briefly compare ICAP to existing...
1,258 citations
Cites background from "Learning With Concept and Knowledge..."
...Two meta-analyses (Horton, McConney, Gallo, Woods, Senn, & Hamelin, 1993; Nesbit & Adesope, 2006) have found a positive effect of concept mapping on knowledge acquisition when compared to other learning activities....
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15 May 2011
TL;DR: Self-Regulation of learning and performance has been studied extensively in the literature as mentioned in this paper, with a focus on the role of self-regulation in the development of learners' skills and abilities.
Abstract: Contents Historical, Contemporary, and Future Perspectives on Self-Regulated Learning and Performance Dale H. Schunk and Jeffrey A. Greene Section I. Basic Domains of Self-Regulation of Learning and Performance Social Cognitive Theoretical Perspective of Self-Regulation Ellen L. Usher and Dale H. Schunk Cognition and Metacognition Within Self-Regulated Learning Philip H. Winne Developmental Trajectories of Skills and Abilities Relevant for Self-Regulation of Learning and Performance Rick H. Hoyle and Amy L. Dent Motivation and Affect in Self-Regulated Learning: Does Metacognition Play a Role? Anastasia Efklides, Bennett L. Schwartz, and Victoria Brown Self-Regulation, Co-Regulation and Shared Regulation in Collaborative Learning Environments Allyson Hadwin, Sanna Jarvela, and Mariel Miller Section II. Self-Regulation of Learning and Performance in Context Metacognitive Pedagogies in Mathematics Classrooms: From Kindergarten to College and Beyond Zemira R. Mevarech, Lieven Verschaffel, and Erik De Corte Self-Regulated Learning in Reading Keith W. Thiede and Anique B. H. de Bruin Self-Regulation and Writing Steve Graham, Karen R. Harris, Charles MacArthur, and Tanya Santangelo The Self-Regulation of Learning and Conceptual Change in Science: Research, Theory, and Educational Applications Gale M. Sinatra and Gita Taasoobshirazi Using Technology-Rich Environments to Foster Self-Regulated Learning in the Social Studies Eric G. Poitras and Susanne P. Lajoie Self-Regulated Learning in Music Practice and Performance Gary E. McPherson, Peter Miksza, and Paul Evans Self-Regulation in Athletes: A Social Cognitive Perspective Anastasia Kitsantas, Maria Kavussanu, Deborah B. Corbatto, and Pepijn K. C. van de Pol Self-Regulation: An Integral Part of Standards-Based Education Marie C. White and Maria K. DiBenedetto Teachers as Agents in Promoting Students' SRL and Performance: Applications for Teachers' Dual-Role Training Program Bracha Kramarski Section III. Technology and Self-Regulation of Learning and Performance Emerging Classroom Technology: Using Self-Regulation Principles as a Guide for Effective Implementation Daniel C. Moos Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation With Advanced Learning Technologies Roger Azevedo, Michelle Taub, and Nicholas V. Mudrick The Role of Self-Regulated Learning in Digital Games John L. Nietfeld Self-Regulation of Learning and Performance in Computer-Supported Collaborative Learning Environments Peter Reimann and Maria Bannert Section IV. Methodology and Assessment of Self-Regulation of Learning and Performance Validity and the Use of Self-Report Questionnaires to Assess Self-Regulated Learning Christopher A. Wolters and Sungjun Won Capturing and Modeling Self-Regulated Learning Using Think-Aloud Protocols Jeffrey A. Greene, Victor M. Deekens, Dana Z. Copeland, and Seung Yu Assessing Self-Regulated Learning Using Microanalytic Methods Timothy J. Cleary and Gregory L. Callan Advancing Research and Practice About Self-Regulated Learning: The Promise of In-Depth Case Study Methodologies Deborah L. Butler and Sylvie C. Cartier Examining the Cyclical, Loosely Sequenced, and Contingent Features of Self-Regulated Learning: Trace Data and Their Analysis Matthew L. Bernacki Data Mining Methods for Assessing Self-Regulated Learning Gautam Biswas, Ryan S. Baker, and Luc Paquette Section V. Individual and Group Differences in Self-Regulation of Learning and Performance 26. Calibration of Performance and Academic Delay of Gratification: Individual and Group Differences in Self-Regulation of Learning Peggy P. Chen and Hefer Bembenutty 27. Academic Help Seeking as a Self-Regulated Learning Strategy: Current Issues, Future Directions Stuart A. Karabenick and Eleftheria N. Gonida 28. The Three Faces of Epistemic Thinking in Self-Regulated Learning Krista R. Muis and Cara Singh 29. Advances in Understanding Young Children's Self-Regulation of Learning Nancy E. Perry, Lynda R. Hutchinson, Nikki Yee, and Elina Maatta 30. Self-Regulation: Implications for Individuals With Special Needs Linda H. Mason and Robert Reid 31. Culture and Self-Regulation in Educational Contexts Dennis M. McInerney and Ronnel B. King
981 citations
Book•
01 Jan 2008
TL;DR: Learning and Memory - A Comprehensive Reference presents an extensive, integrated summary of the present state of research in the neurobiology and psychology of learning and memory and covers an enormous range of intellectual territory.
Abstract: The study of Learning and Memory is a central topic in Neuroscience and Psychology. It is also a very good example of a field that has come into maturity on all levels - in the protein chemistry and molecular biology of the cellular events underlying learning and memory, the properties and functions of neuronal networks, the psychology and behavioural neuroscience of learning and memory. Many of the basic research findings are directly applicable in the treatment of diseases and aging phenomena, and have found their way into educational theory and praxis. Learning and Memory - A comprehensive reference is the most comprehensive source of information about learning and memory ever assembled, and the definitive reference work on the topic. In four volumes, Editor-in-Chief John H. Byrne (University of Texas), together with volume editors Howard Eichenbaum (Boston University) for Systems and Neuroscience, Randolf Menzel (Freie Universit t Berlin) for Behavioral Approaches, Henry Roediger (Washington University) for Cognitive Psychology, and David Sweatt (University of Alabama, Birmingham) for Molecular Mechanisms, have put together a truly authoritative collection of overview articles in 159 chapters on over 3000 pages. Learning and Memory - A Comprehensive Reference presents an extensive, integrated summary of the present state of research in the neurobiology and psychology of learning and memory and covers an enormous range of intellectual territory. With topics ranging from the neurochemistry and neurobiology of learning at the cellular and synaptic levels, systems neurobiology, the study of remarkable capabilities in animals (such as homing), ethological and behavioristic analyses, mechanisms, psychology, and disorders of learning and memory in humans, the work broadly covers all topics in the neurobiology and psychology of learning and memory. There is no other handbook with such a comprehensive coverage and depth. The authors selected are the leading scholars for the particular topics on which they write. * The most comprehensive and authoritative resource available on the topic of learning and memory and its mechanisms * Representing outstanding scholarship, each chapter is written by a leader in the field and an expert in the topic area * Relevant and useful for newcomers and advanced researchers alike * All topics represent the most up to date research * A must have set for all medical, neuroscience and psychology libraries, and of great value to neighbouring disciplines, including education * Selected chapters from the on-line version can be used to great effect in teaching * Full color throughout, hundreds of illustrations, four volumes, 159 chapters, 3000 pages * Available in print and on-line
687 citations
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01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
01 Jan 2007
18,170 citations
"Learning With Concept and Knowledge..." refers result in this paper
...…the study observed participants who had characteristics apparently similar to other samples in this analysis, the effect size was not deleted but, rather, was adjusted downward to a value (g = +2.2) slightly greater than the next-largest effect size, as recommended by Tabachnick and Fidell (2001)....
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Book•
01 Jan 1985
TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Abstract: Preface. Introduction. Data Sets. Tests of Statistical Significance of Combined Results. Vote-Counting Methods. Estimation of a Single Effect Size: Parametric and Nonparametric Methods. Parametric Estimation of Effect Size from a Series of Experiments. Fitting Parametric Fixed Effect Models to Effect Sizes: Categorical Methods. Fitting Parametric Fixed Effect Models to Effect Sizes: General Linear Models. Random Effects Models for Effect Sizes. Multivariate Models for Effect Sizes. Combining Estimates of Correlation Coefficients. Diagnostic Procedures for Research Synthesis Models. Clustering Estimates of Effect Magnitude. Estimation of Effect Size When Not All Study Outcomes Are Observed. Meta-Analysis in the Physical and Biological Sciences. Appendix. References. Index.
9,769 citations
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01 Jan 2014
TL;DR: The Taxonomy of Educational Objectives as discussed by the authors is a taxonomy of educational objectives that is based on the concepts of knowledge, specificity, and problems of objectives, and is used in our taxonomy.
Abstract: List of Tables and Figures. Preface. Foreword. SECTION I: THE TAXONOMY, EDUCATIONAL OBJECTIVES AND STUDENT LEARNING. 1. Introduction. 2. The Structure, Specificity, and Problems of Objectives. SECTION II: THE REVISED TAXONOMY STRUCTURE. 3. The Taxonomy Table. 4. The Knowledge Dimension. 5. The Cognitive Process Dimension. SECTION III: THE TAXONOMY IN USE. 6. Using the Taxonomy Table. 7. Introduction to the Vignettes. 8. Nutrition Vignette. 9. Macbeth Vignette. 10. Addition Facts Vignette. 11. Parliamentary Acts Vignette. 12. Volcanoes? Here? Vignette. 13. Report Writing Vignette. 14. Addressing Long-standing Problems in Classroom Instruction. APPENDICES. Appendix A: Summary of the Changes from the Original Framework. Appendix B: Condensed Version of the Original Taxonomy of Educational Objectives: Cognitive Domain. References. Credits. Index.
9,708 citations
Book•
01 Jan 2001
TL;DR: The Taxonomy of Educational Objectives as mentioned in this paper is a taxonomy of educational objectives that is based on the concepts of knowledge, specificity, and problems of objectives, and is used in our taxonomy.
Abstract: List of Tables and Figures. Preface. Foreword. SECTION I: THE TAXONOMY, EDUCATIONAL OBJECTIVES AND STUDENT LEARNING. 1. Introduction. 2. The Structure, Specificity, and Problems of Objectives. SECTION II: THE REVISED TAXONOMY STRUCTURE. 3. The Taxonomy Table. 4. The Knowledge Dimension. 5. The Cognitive Process Dimension. SECTION III: THE TAXONOMY IN USE. 6. Using the Taxonomy Table. 7. Introduction to the Vignettes. 8. Nutrition Vignette. 9. Macbeth Vignette. 10. Addition Facts Vignette. 11. Parliamentary Acts Vignette. 12. Volcanoes? Here? Vignette. 13. Report Writing Vignette. 14. Addressing Long-standing Problems in Classroom Instruction. APPENDICES. Appendix A: Summary of the Changes from the Original Framework. Appendix B: Condensed Version of the Original Taxonomy of Educational Objectives: Cognitive Domain. References. Credits. Index.
7,339 citations