http://www.compassproject.net/sadhana/teaching/readings/deftainsworth.pdf

DeFT: A conceptual framework for considering learning with multiple representations

https://telearn.archives-ouvertes.fr/hal-01206700/document

Phases of inquiry-based learning: Definitions and the inquiry cycle

Practicing engineers are hired, retained, and rewarded for solving problems, so engineering students should learn how to solve workplace problems Workplace engineering problems are substantively different from the kinds of problems that engineering students most often solve in the classroom; therefore, learning to solve classroom problems does not necessarily prepare engineering students to solve workplace problems These qualitative studies of workplace engineering problems identify the attributes of workplace problems Workplace problems are ill-structured and complex because they possess conflicting goals, multiple solution methods, non-engineering success standards, non-engineering constraints, unanticipated problems, distributed knowledge, collaborative activity systems, the importance of experience, and multiple forms of problem representation Some implications for designing engineering curricula and experiences that better prepare students for solving workplace problems are considered

Everyday Problem Solving in Engineering: Lessons for Engineering Educators

This article reviews the (quasi)experimental research of the past decade on the learning effects of computer simulations in science education The focus is on two questions: how use of computer simulations can enhance traditional education, and how computer simulations are best used in order to improve learning processes and outcomes We report on studies that investigated computer simulations as a replacement of or enhancement to traditional instruction In particular, we consider the effects of variations in how information is visualized, how instructional support is provided, and how computer simulations are embedded within the lesson scenario The reviewed literature provides robust evidence that computer simulations can enhance traditional instruction, especially as far as laboratory activities are concerned However, in most of this research the use of computer simulations has been approached without consideration of the possible impact of teacher support, the lesson scenario, and the computer simulation's place within the curriculum

The learning effects of computer simulations in science education

Part 1: Establishing the Foundation: Shared Understanding, Integration, & Trust.Introduction: Establishing the Foundation: Shared Understanding, Integration, & Trust (Susan G. Cohen and Cristina B. Gibson).Chapter 1: In the Beginning: Introduction and Framework (Susan G. Cohen and Cristina B. Gibson).Chapter 2: Knowledge Sharing And Shared Understanding In Virtual Teams (Pamela Hinds and Suzanne Weisband).Chapter 3: Managing The Global New Product Development Network: A Sensemaking Perspective (Susan Albers Mohrman, Janice A. Klein, and David Finegold).Chapter 4: Building Trust: Effective Multi-Cultural Communication Processes in Virtual Teams (Cristina B. Gibson and Jennifer A. Manuel).Summary: Establishing the Foundation (Susan G. Cohen and Cristina B. Gibson).Part 2: The Raw Materials: People and Context.Introduction: The Raw Materials: People and Context (Cristina B. Gibson and Susan G. Cohen).Chapter 5: Building a Winning Virtual Team: KSAs, Selection, Training, and Evaluation (Richard Blackburn, Stacie Furst, and Benson Rosen).Chapter 6: Pay Systems for Virtual Teams (Edward E. Lawler III).Chapter 7: Meeting the Performance Challenge: Calculating ROI for Virtual Teams (Alec R. Levenson and Susan G. Cohen).Summary: The Raw Materials (Cristina B. Gibson and Susan G. Cohen).Part 3: Constructing the Design: Leadership, Knowledge Management and Information Sharing.Introduction: Constructing the Design: Leadership, Knowledge Management and Information Sharing (Susan G. Cohen and Cristina B. Gibson).Chapter 8: Exploring Emerging Leadership in Virtual Teams (Kristi Lewis Tyran, Craig K. Tyran, and Morgan Shepherd).Chapter 9: Designing the Knowledge-Management Infrastructure for Virtual Teams: Building and Using Social Networks and Social Capital (Martha L. Maznevski and Nicholas A. Athanassiou).Chapter 10: Overcoming Barriers to Information Sharing in Virtual Teams (Catherine Durnell Cramton and Kara L. Orvis).Summary: Constructing the Design (Susan G. Cohen and Cristina B. Gibson).Part 4: Wiring that Connects: Implementing Information Technology.Introduction: Wiring that Connects: Implementing Information Technology (Susan G. Cohen and Cristina B. Gibson).Chapter 11: Context, Task and the Evolution of Technology Use in Global Virtual Teams (Kenneth Riopelle, Julia Gluesing, Tara Alcordo, Marietta Baba, David Britt, Willie McKether, Leslie Monplaisir, Hilary Ratner, and Kimberly Harris Wagner).Chapter 12: Technology Alignment and Adaptation for Virtual Teams Involved in Unstructured Knowledge Work (Nelson King and Ann Majchrzak).Chapter 13: Team or Community of Practice Aligning Tasks, Structures and Technologies (Arjan Raven).Summary: Wiring that Connects (Susan G. Cohen and Cristina B. Gibson).Part 5: It's All About Action: Processes and Development.Introduction: It's All About Action: Processes and Development (Cristina B. Gibson and Susan G. Cohen).Chapter 14: Influence and Political Processes in Virtual Teams (Efrat Elron and Eran Vigoda).Chapter 15: Conflict and Virtual Teams (Terri L. Griffith, Elizabeth A. Mannix, and Margaret A. Neale).Chapter 16: The Development of Global Virtual Teams (Julia Gluesing, Tara Alcordo, Marietta Baba, David Britt, Kimberly Harris Wagner, Willie McKether, Leslie Monplaisir, Hilary Ratner, and Kenneth Riopelle).Chapter 17: Closing the Time Gap in Virtual Teams (Janice A. Klein & Astrid Kleinhanns).Summary: It's All About Action (Cristina B. Gibson and Susan G. Cohen).Chapter 18: The Last Word: Conclusions and Implications (Cristina B. Gibson and Susan G. Cohen).

Virtual teams that work : creating conditions for virtual team effectiveness

Current views on science learning state that this should not involve learning just about the established results of science, including well-established theories such as Newtonian mechanics or the evolution of species as well as important empirical discoveries such as Young’s double slit experiment or the structure of DNA. Instead science learning should also focus on the processes and methods used by scientists to achieve such results. One obvious way to bring students into contact with the scientific way of working is to have them engage in the processes of scientific inquiry themselves, by offering them environments and tasks that allow them to carry out the processes of science: orientation, stating hypotheses, experimentation, creating models and theories, and evaluation (de Jong 2006a). Involving students in the processes of science brings them into the closest possible contact with the nature of scientific understanding, including its strengths, problems and limitations (Dunbar 1999). This is the main claim of inquiry learning: engaging learners in scientific processes helps them build a personal knowledge base that is scientific, in the sense that they can use this knowledge to predict and explain what they observe in the natural world. For about the last 20 years, computers have been used to create environments that engage learners in scientific inquiry activities. The virtue of the computer is that it allows the scaling down of inquiry tasks to a manageable size for learners who are inexperienced with inquiry processes. There are several ways in which computers can help create challenging and manageable environments for inquiry learning: • Replacing the natural world by a computer simulation can help make available on a wide scale the phenomena to be investigated. Moreover, the simulation may be simplified and/or emphasize certain aspects of the domain that can help learners observe critical features of the domain (van Joolingen & de Jong 1991a; de Jong & van Joolingen 1998; de Jong 2006a). • The computer can offer tools that support the inquiry processes, such as tools to analyse or visualize data, tools that help learners state hypotheses and tools that help learners manage the learning process (van Joolingen 1999; Linn et al. 2004a; Quintana et al. 2004; de Jong 2006b). • The computer can support collaboration between learners, allowing them to communicate, share data, results and ideas, and discuss consequences for the knowledge that is under construction (Okada & Simon 1997; van Joolingen et al. 2005). • Computer-based modeling tools allow learners to express their theories in models that can be simulated. In this way learners can use their theories operationally, confronting themselves with the consequences of their ideas (Hestenes 1987; Schecker 1993; Jackson et al. 1996; Fretz et al. 2002; Zhang et al. 2002; Schwarz & White 2005).

Issues in computer supported inquiry learning in science

Acquiring knowledge in science and mathematics : the use of multiple representations in technology based learning environments

This study examined whether online tool support for regulation promotes student learning during collaborative inquiry in a computer simulation-based learning environment. Sixty-one students worked in small groups to conduct a scientific inquiry with fluid dynamics. Groups in the experimental condition received a support tool with regulatory guidelines; control groups were given a version of this tool from which these instructions were removed. Results showed facilitative effects for the fully specified support tool on learning outcomes and initial planning. Qualitative data elucidated how regulative guidelines enhanced learning and suggests ways to further improve regulative processes within collaborative inquiry learning settings.

Regulative Support for Collaborative Scientific Inquiry Learning.

Sim Quest is an authoring system for creating computer simulations embedded in an instructional environment. A typical learning environment created with Sim Quest allows learners to engage in an activity of discovery learning with a simulation, supported by instructional measures from within the environment. Some of these instructional measures adapt themselves to the interaction of the learner with the simulation. Sim Quest allows the author to create various kinds of instructional support for the learner. Assignments provide the learner with short term goals for the learning process, explanations offer additional information, a monitoring tool allows the learner to record and replay experiments, and, moreover, the domain can be arranged according to different levels of model progression. Recent developments are a hypothesis scratchpad, intelligent feedback, a modelling tool, and facilities for collaborative learning in Sim Quest. Sim Quest supports the full process of authoring simulation based learning environments, from creating the simulation model to defining the instructional interaction of the learning environment with the learner. Authoring a Sim Quest learning environment is done in an object oriented way. The author selects templates from a library to create building blocks of the learning environment, which are then edited to match the author’s requirements. The Sim Quest architecture, with a simulation context as central element takes care of the interaction between the various building blocks. The author is supported by the contents of the library, on-line help, on-line pedagogical advice, and a wizard that can automate part of the authoring process. Sim Quest has been used to create about twenty learning environments, some of which have been evaluated extensively with students. Currently Sim Quest is used by a group of teachers to create learning material for use in their classrooms. A number of Sim Quest learning environments are now published by commercial publishers.

SimQuest : authoring educational simulations

SIMQUEST is an authoring system for designing and creating simulation-based learning environments. The special character of SIMQUEST learning environments is that they include cognitive support for learners which means that they provide learners with support in the discovery process. In SIMQUEST learning environments, a balance is sought between direct guidance of the learning process and sufficient freedom for learners to regulate the learning process themselves. This paper describes the basic mechanisms of the SIMQUEST learning and authoring environments. The functionality authors have in providing the learner with guidance and some of the experiences on how authors use these opportunities and learners employ the cognitive support are reported.

https://telearn.archives-ouvertes.fr/hal-00190679/document

T. de Jong

Papers

Issues in computer supported inquiry learning in science

Acquiring knowledge in science and mathematics : the use of multiple representations in technology based learning environments

Regulative Support for Collaborative Scientific Inquiry Learning.

SimQuest : authoring educational simulations

Self-directed learning in simulation-based discovery environments