Lucia Mason

Bio: Lucia Mason is an academic researcher from University of Padua. The author has contributed to research in topics: Reading comprehension & Reading (process). The author has an hindex of 40, co-authored 105 publications receiving 4943 citations.

Reconsidering conceptual change: issues in theory and practice

Abstract: Contributors. Preface. Introduction M. Limon, L. Mason. Part I: Theoretical Perspectives. The Processes and Challenges of Conceptual Change M.T.H. Chi, R.D. Roscoe. Why 'Conceptual Ecology' is a Good Idea A.A. diSessa. On the Nature of Naive Physics S. Vosniadou. Map Reading Versus Mind Reading: Revisiting Children's Understanding of the Shape of the Earth J. Ivarsson, et al. Understanding Conceptual Change: A Commentary R.E. Mayer. Part II: Motivational, Social and Contextual Aspects. The Role of Motivational Beliefs in Conceptual Change E.A. Linnenbrink, P.R. Pintrich. Situating the Question of Conceptual Change O. Hallden, et al. Participative Learning and Conceptual Change M. Gorodetsky, S. Keiny. Cognitive Variability in the Development of the Concept of Family: A Contextualist or a Gradualist View? M.J. Rodrigo, et al. Motivational, Social, and Contextual Aspects of Conceptual Change: A Commentary G.M. Sinatra. Part III: Domain Specificity and Learning. The Role of Students' Epistemological Knowledge in the Process of Conceptual Change in Science J. Leach, J. Lewis. Intuitive Rules: The Case of 'More A - More B' R. Stavy, et al. Conceptual Change in Mathematics: Understanding the Real Numbers K. Merenluoto, E. Lehtinen. Conceptual Change in History M. Limon. Content and Conceptual Change: A Commentary R. White. Part IV: Instructional Practices to Promote Conceptual Change in Classroom. Developing Epistemological Thinking to Foster Conceptual Change in Different Domains L. Mason. Science Learning Through Text: The Effect of Text Design and Text Comprehension Skills on Conceptual Change M. Mikkila-Erdmann. Computer-BasedInteractions for Conceptual Change in Science M. Wiser, T.G. Amin. Knowledge Assessment and Conceptual Understanding J. Alonso-Tapia. Change as a Process and a Disposition: A Commentary P. Boscolo.

On warm conceptual change: The interplay of text, epistemological beliefs, and topic interest.

Abstract: The aim of this study was to go further than considering only cognitive factors to extend the understanding of the complex, dynamic underlying knowledge revision processes. Fifth graders were assigned to 2 reading conditions. Participants in 1 condition read a refutational text about light, whereas participants in the other read a traditional text. Within each reading condition, students had more or less advanced beliefs about scientific knowledge (complex and evolving vs. simple and certain), as well as high or low topic interest. Overall findings from pretest to immediate and delayed posttests showed that knowledge revision was affected by several interactions among the variables examined. Students who attained the highest scores at both the immediate and delayed posttests were those who had read the refutational text and had high topic interest, as well as more advanced beliefs about scientific knowledge. In particular, the refutational text was more powerful in prompting a restructuring of alternative conceptions about 2 of the 3 light phenomena examined. In addition, students preferred the innovative text to the traditional textbook text.

Do fourth graders integrate text and picture in processing and learning from an illustrated science text? Evidence from eye-movement patterns

Abstract: This study used eye-tracking methodology in the school setting to examine fourth graders' online processing of text and graphics while reading an illustrated science text. We were interested in identifying patterns of visual behavior, which was examined considering individual differences in reading comprehension, prior knowledge, and spatial ability. We also investigated the outcomes of learning from text by measuring free recall, factual knowledge, and transfer of knowledge. For an important advancement of research in this area, the link between processing and learning was also examined. Forty-nine 4th graders participated in a pretest, immediate, and delayed posttest design. Results of a cluster analysis using indices of first- and second-pass eye-fixation, as well as integrative saccades revealed three patterns of visual behavior varying for the level of integration of text and picture. Significant associations between eye-tracking data and reading comprehension and prior knowledge emerged. Moreover, the three patterns of visual behavior were significantly related to students' performances in the various learning tasks at both testing times. The greater integrative processing of the illustrated text was associated with higher learning performances. The significance of the study for educational implications is outlined.

Modern Applied Statistics With S

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Multimedia in Learning

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.

Establishing the norms of scientific argumentation in classrooms

Abstract: Basing its arguments in current perspectives on the nature of the scientific enterprise, which see argument and argumentative practice as a core activity of scientists, this article develops the case for the inclusion and central role of argument in science education. Beginning with a review of the nature of argument, it discusses the function and purpose of dialogic argument in the social construction of scientific knowledge and the interpretation of empirical data. The case is then advanced that any education about science, rather than education in science, must give the role of argument a high priority if it is to give a fair account of the social practice of science, and develop a knowledge and understanding of the evaluative criteria used to establish scientific theories. Such knowledge is essential to enhance the public understanding of science and improve scientific literacy. The existing literature, and work that has attempted to use argument within science education, is reviewed to show that classroom practice does provide the opportunity to develop young people's ability to construct argument. Furthermore, the case is advanced that the lack of opportunities for the practice of argument within science classrooms, and lack of teacher's pedagogical skills in organizing argumentative discourse within the classroom are significant impediments to progress in the field. © 2000 John Wiley & Sons, Inc. Sci Ed84:287–312, 2000.

Analyzing linguistic data: a practical introduction to statistics using R

Abstract: The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Metacognition and learning: conceptual and methodological considerations

Abstract: This is the first issue of Metacognition and Learning, a new international journal dedicated to the study of metacognition and all its aspects within a broad context of learning processes. Flavell coined the term metacognition in the seventies of the last century (Flavell, 1979) and, since then, a huge amount of research has emanated from his initial efforts. Do we need metacognition as a concept in learning theory? Already in 1978, Brown posed the question whether metacognition was an epiphenomenon. Apparently, she was convinced otherwise as she has been working fruitfully for many years in the area of metacognition. Moreover, a review study by Wang, Haertel, and Walberg (1990) revealed metacognition to be a most powerful predictor of learning. Metacognition matters, but there are many unresolved issues that need further investigation. This introduction will present ten such issues, which are by no means exhaustive. They merely indicate what themes might be relevant to the journal.