About the CRLT The CRLT has as its mission to promote and support a community of scholars dedicated to research on the design, use, and implementation of technology to improve learning. Three primary themes underlie the work at the Center: • research that contributes to the development of new pedagogical models for continuing professional development in the 21st century; • research on and evaluation of interactive distance learning environments that inform our understanding of student learning; and • research on teaching strategies for using current and emerging technologies to support student interaction, collaboration, and engagement in the issues being studied. This report is one of a series from our on-going research on learning and technology. If you have any questions or comments on this report, or if you would like to find out more about the activities of the CRLT, contact:

Abstract: This paper presents an update of our work on developing a distributed architecture for

objects from a practical application at the Centre for Economic Development and Applied

Abstract not found

This paper outlines our experiences with applying collaborative tagging in e-learning systems to supplement more traditional metadata gathering approaches. Over the last 10 years, the learning object paradigm has emerged in e-learning and has caused standards bodies to focus on creating metadata repositories based upon strict domain-free taxonomies. We argue that the social collection phenomena and flexible metadata standards are key in collecting the kinds of metadata required for adaptable online learning. This paper takes a broad look at tagging within elearning. It first looks at the implications for tagging within the domain through an analysis of tags students provided when classifying learning objects. Next, it looks at two case studies based on novel interfaces for applying tagging. These two systems emphasize tags being applied within learning content through the use of a highlighting metaphor.

This paper describes knowledge components that are thought to be appropriate and sufficient to precisely describe certain types of cognitive subject matter content (knowledge). It also describes knowledge structures that show the relationships among these knowledge components and among other knowledge objects. It suggests that a knowledge structure is a form of schema such as those that learners use to represent knowledge in memory. A mental model is a schema plus cognitive processes for manipulating and modifying the knowledge stored in a schema. We suggested processes that enable learners to manipulate the knowledge components of conceptual network knowledge structures for purposes of classification, generalization, and concept elaboration. We further suggested processes that enable learners to manipulate the knowledge components of process knowledge structures (PEAnets) for purposes of explanation, prediction, and trouble shooting. The hypothesis of this paper is that knowledge components and knowledge structures, such as those described in this paper, could serve as meta mental models that would enable learners to more easily acquire conceptual and causal networks and their associated processes. The resulting specific mental models would facilitate their ability to solve problems of conceptualization and interpretation.

Abstract. Despite the growing popularity of user modeling servers, little attention has been paid to optimizing and evaluating the performance of these servers. We argue that implementation issues and their influence on server performance should become the central focus of the user modeling community, since there is a sharply increasing real-life load on user modeling servers, This paper focuses on a specific implementation-level aspect of user modeling servers – the choice of push or pull approaches to evidence propagation. We present a new push-based implementation of our user modeling server CUMULATE and compare its performance with the performance of the original pull-based CUMULATE server. 1

The Essen Learning Model is a generic development model supporting development processes on three levels: development of curricula, learning sequences, and learning units. We focus on finding an adequate combination of didactical methods and educational technologies. Secondly, we focus on interoperability, reusability, and ease of use, using standards on the modeling level (ARIS), architecture level (LTSA, IMS), and implementation level (XML). The approach will enable educators, project managers, and authors to efficiently develop and implement Computer Supported Learning Environments.

Digital game-based learning is a hot topic of research and development. Since the advent of computer and video games, educators were inherently interested in utilizing the beneficial aspects of computer games for educational purposes. These factors are primarily the intrinsic motivation of games, immersive environments, engaging stories, and an artful balance between challenges and continuously growing abilities. Proponents of computer games delivered a large number of empirical investigations revealing that games may foster the development of abilities and competencies. Besides the advantageous aspects of computer games, a variety of problems were reported by researchers. Due to the high costs of professional game development, many educational games are technologically poor and cannot compete with entertainment games in terms of visual design, possibilities for interactions, or storytelling. Moreover, many current educational games do not incorporate a sound psychological, pedagogical, or didactic background; instead they are focusing on transmission or rehearsal of isolated facts or skills. Finally, such games lack the ability to adapt to individual competencies failing to balance challenge and abilities regarding knowledge or skills. The ELEKTRA project, introduced in this article, aims for addressing these problems relying on an interdisciplinary approach of cognitive science, neuroscience, pedagogy, game design, and game development. The project will develop an adventure game that can keep up with commercial games and that focuses on primarily curriculum-related educational purposes by incorporating a sound psychological and pedagogical framework. Moreover, the project will prove the outcomes of research and development by a comprehensive game demonstrator.

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education