Traditionally, Cognitive Load Theory (CLT) has focused on instructional methods to decrease extraneous cognitive load so that available cognitive resources can be fully devoted to learning. This article strengthens the cognitive base of CLT by linking cognitive processes to the processes used by biological evolution. The article discusses recent developments in CLT related to the current view in instructional design that real-life tasks should be the driving force for complex learning. First, the complexity, or intrinsic cognitive load, of such tasks is often high so that new methods are needed to manage cognitive load. Second, complex learning is a lengthy process requiring learners’ motivational states and levels of expertise development to be taken into account. Third, this perspective requires more advanced methods to measure expertise and cognitive load so that instruction can be flexibly adapted to individual learners’ needs. Experimental studies are reviewed to illustrate these recent developments. Guidelines for future research are provided.

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Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions

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Online communities – Designing Usability, Supporting Sociability

This study describes the development and validity testing of a field measure of transactive memory systems. Transactive memory systems are especially important for teams designed to leverage members' expertise, but field research has lagged because there are no adequate measures of the construct. The author developed and tested a 15-item scale in a laboratory sample of 124 teams, a field sample of 64 Master of Business Administration consulting teams, and a field sample of 27 teams from technology companies. Results from the present study demonstrate that the scale is internally consistent, related to alternative measures and hypothesized causes and effects, and unrelated to theoretically distinct constructs, providing evidence of convergent, criterion-related, and discriminant validity. Suggestions for improving the scale, future validity testing, and possible boundary conditions are discussed.

Measuring transactive memory systems in the field: Scale development and validation.

Managing virtual teams: A review of current empirical research

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

Handbook of Self-Regulation of Learning and Performance

https://dspace.library.uu.nl/bitstream/handle/1874/16872/kirschner_03_identifying_pitfalls_social_interaction_computer_supported.pdf?sequence=1

Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research

There is much positive research on computer -supported collaborative learning (CSCL) environments in asynchronous distributed learning groups (DLGs). There is also research that shows that contemporary CSCL environments do not completely fulfil expectations on supporting interactive group learning, shared understanding, social construction of knowledge, and acquisition of competencies. There appear to be two major pitfalls impeding achievement of the desired social interaction in CSCL environments: taking social interaction in groups for granted and the lack of attention paid to the social psychological dimension of social interaction outside of the task context. Current solutions offered to avoid the pitfalls placed responsibility on instructors and teachers to encourage collaborative learning and social interaction. To both free educators from this burden and be more cost effective, we propose an intelligent CSCL environment. The environment is based upon a theoretical framework that suggests embedding certain properties in the environment to act as social contextual facilitators - social affordances - to initiate and sustain learner’s social interactions. Finally, a group awareness widget (GAW) - a software tool providing the learner group awareness about the others in the task and in the non-task context - is introduced as an embodiment of this theoretical framework.

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The Sociability of Computer-Supported Collaborative Learning Environments

https://dspace.library.uu.nl/bitstream/handle/1874/16871/kirschner_02_external_representation_argumentation_cscl_management_cognitive_load.pdf;sequence=1

External representation of argumentation in CSCL and the management of cognitive load

Workplace learning in industry is perceived to have a powerful influence on the development of students engineering identity. But, teachers also observed vast developmental differences between students during workplace learning. To find explanations for these differences, we studied students identity development during workplace learning using a self-guiding and mentoring model. Research questions are: (1) To what extent do bachelor engineering students develop their engineering identity during workplace learning? (2) What are students developmental models? (3) Can different effects of these models on student identity development be found?Participants were 256 third-year bachelor student engineers. The data were collected with two written questionnaires, one before and one after workplace learning.Results showed, first, a significant incline and decline on the two aspects of identity development, clarity and identification. Second, combined achievement on the two aspects showed a sort out into fou...

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Wmg Wim Jochems

Papers

Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research

The Sociability of Computer-Supported Collaborative Learning Environments

External representation of argumentation in CSCL and the management of cognitive load

The Development of Engineering Students Professional Identity During Workplace Learning in Industry: A study in Dutch Bachelor Education