Basics of qualitative research: Grounded theory procedures and techniques

Experience and Educationis the best concise statement on education ever published by John Dewey, the man acknowledged to be the pre-eminent educational theorist of the twentieth century. Written more than two decades after Democracy and Education(Dewey's most comprehensive statement of his position in educational philosophy), this book demonstrates how Dewey reformulated his ideas as a result of his intervening experience with the progressive schools and in the light of the criticisms his theories had received. Analysing both "traditional" and "progressive" education, Dr. Dewey here insists that neither the old nor the new education is adequate and that each is miseducative because neither of them applies the principles of a carefully developed philosophy of experience. Many pages of this volume illustrate Dr. Dewey's ideas for a philosophy of experience and its relation to education. He particularly urges that all teachers and educators looking for a new movement in education should think in terms of the deeped and larger issues of education rather than in terms of some divisive "ism" about education, even such an "ism" as "progressivism." His philosophy, here expressed in its most essential, most readable form, predicates an American educational system that respects all sources of experience, on that offers a true learning situation that is both historical and social, both orderly and dynamic.

经验与教育 = Experience and education

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

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

Course Description In this course, we will explore the question of the actual and potential connections between democracy and education. Our focus of attention will be placed on a critical examination of democratic theory and its implications for the civic education roles and contributions of teachers, adult educators, community development practitioners, and community organizers. We will survey and deal critically with a range of competing conceptions of democracy, variously described as classical, republican, liberal, radical, marxist, neomarxist, pragmatist, feminist, populist, pluralist, postmodern, and/or participatory. Using narrative inquiry as a means for illuminating and interpreting contemporary practice, we will analyze the implications of different conceptions of democracy for the practical work of civic education.

Democracy and education.

Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it.

Cognitive Architecture and Instructional Design

https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog1302_1

Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems

Chapter headings: Towards an Interdisciplinary Learning Science (P. Reimann, H. Spada). A Cognitive Psychological Approach to Learning (S. Vosniadou). Learning to Do and Learning to Understand: A Lesson and a Challenge for Cognitive Modeling (S. Ohlsson). Machine Learning: Case Studies of an Interdisciplinary Approach (W. Emde). Mental and Physical Artifacts in Cognitive Practices (R. Saljo). Learning Theory and Instructional Science (E. De Corte). Knowledge Representation Changes in Humans and Machines (L. Saitta and Task Force 1). Multi-Objective Learning with Multiple Representations (M. Van Someren, P. Reimann). Order Effects in Incremental Learning (P. Langley). Situated Learning and Transfer (H. Gruber et al.). The Evolution of Research on Collaborative Learning (P. Dillenbourg et al.). A Developmental Case Study on Sequential Learning: The Day-Night Cycle (K. Morik, S. Vosniadou). Subject index. Author index.

Learning in Humans and Machines : Towards an Interdisciplinary Learning Science

Massive open online courses (MOOCs) have exploded onto the scene, promising to satisfy a worldwide thirst for a high-quality, personalized, and free education. This article explores where MOOCs fit within the e-learning and Artificial Intelligence in Education (AIED) landscape.

MOOCs: So Many Learners, So Much Potential ...

Although temporality is a key characteristic of the core concepts of CSCL—interaction, communication, learning, knowledge building, technology use—and although CSCL researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order. This is particularly problematic when collaboration and learning processes are studied in groups that work together over weeks, and months, as is often the case. The quantitative method dominant in the social and learning sciences—variable-centred variance theory—is of limited value for studying change on longer time scales. We introduce the event-centred view of process as a more generally applicable approach, not only for quantitative analysis, but also for providing closer links between qualitative and quantitative research methods. A number of methods for variable- and event-centred analysis of process data are described and compared, using examples from CSCL research. I conclude with suggestions on how experimental, descriptive, and design-oriented research orientations can become better integrated.

Time is precious: Variable- and event-centred approaches to process analysis in CSCL research

Referring to current research on self-regulated learning, we analyse individual regulation in terms of a set of specific sequences of regulatory activities. Successful students perform regulatory activities such as analysing, planning, monitoring and evaluating cognitive and motivational aspects during learning not only with a higher frequency than less successful learners, but also in a different order—or so we hypothesize. Whereas most research has concentrated on frequency analysis, so far, little is known about how students’ regulatory activities unfold over time. Thus, the aim of our approach is to also analyse the temporal order of spontaneous individual regulation activities. In this paper, we demonstrate how various methods developed in process mining research can be applied to identify process patterns in self-regulated learning events as captured in verbal protocols. We also show how theoretical SRL process models can be tested with process mining methods. Thinking aloud data from a study with 38 participants learning in a self-regulated manner from a hypermedia are used to illustrate the methodological points.

Peter Reimann

Papers

Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems

Learning in Humans and Machines : Towards an Interdisciplinary Learning Science

MOOCs: So Many Learners, So Much Potential ...

Time is precious: Variable- and event-centred approaches to process analysis in CSCL research

Process mining techniques for analysing patterns and strategies in students’ self-regulated learning