A conceptual framework for the integrative design of adaptable representation for learning

International audienceIn this work we argue in favor of an integrative design approach supporting the idea that teachers and learners in a technology enhanced learning environment should be able to adapt the affordances of the representations in order to achieve an optimum coupling of the interface to the socio-cognitive learning needs in the specific context. An adaptable interface can appear as a reactive, coactive, proactive or enactive interface depending on the specific learning demands. Such an interface can also function as a metacognitive tool, enhancing users' understanding of the different modes of the instructional process depending on the varied socio-cognitive contextual factors. We present our very first theoretical considerations pertaining to such an integrative design framework and show how they can be applied in the algorithm visualization domain

How to improve the peer review method: Free-selection vs assigned-pair protocol evaluated in a computer networking course

This study provides field research evidence on the efficiency of a “free-selection” peer review assignment protocol as compared to the typically implemented “assigned-pair” protocol. The study employed 54 sophomore students who were randomly assigned into three groups: Assigned-Pair (AP) (the teacher assigns student works for review to student pairs), Free-Selection (FS) (students are allowed to freely explore and select peer work for review), and No Review (NR) (control group). AP and FS student groups studied and reviewed peer work in the domain of Computer Networking, supported by a web-based environment designed to facilitate the two peer review protocols. Our results indicate that students following the Free Selection protocol demonstrate (a) better domain learning outcomes, and (b) better reviewer skills, compared to the AP condition. Overall, the study analyzes the benefits and shortcomings of the FS vs AP review assignment protocol, providing evidence that the FS condition can be multiply beneficial to students who engage in peer review activities

Adaptation patterns in systems for scripted collaboration

Abstract: This work presents a prototype method (DeACS) for identifying useful adaptation patterns to be embedded in systems for adaptive collaboration scripting. Collaboration scripts are didactic scenarios that guide and support the collaborative learning activity while adaptive collaboration scripting is the idea that computer-supported collaboration scripts can be adapted during run time, to provide learning experiences tailored to individual and group characteristics. An adaptation pattern is described as a well-defined adaptation process that can be initiated by the system when specific conditions are identified during script implementation. In order to model the proposed method twelve postgraduate students were engaged in a pyramid-type collaboration script and the analysis of the learning experience provided the basis for identifying a number of possible adaptation patterns. The paper discusses also next steps for advancing the design and evaluation of adaptation patterns in systems for scripted collaboration

Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligent adaptive learning and collaborative systems that are brought by the advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems. Given the variety of learning needs as well as the existence of different technological solutions, the book exemplifies the methodologies and best practices through several case studies and adaptive real-world collaborative learning scenarios, which show the advancement in the field of analysis, design and implementation of intelligent adaptive and personalized systems

External Representations for Learning

International audienceThis chapter provides the state of the art on learning with external digital representations and elaborates on some landmarks for understanding the design and use of emergent learning technologies. We start by identifying a pervasive underlying distinction into dyadic and triadic views of representation which triggers the question of the role of culture and context in the study of the construction and the interpretation of digital representations. Three issues are discussed in more depth: learning with a multiplicity of digital representations, adjusting the representational density of digital representations and externalizing symbolic processing to the computer. Based on these issues, we conjecture that the future of digital learning might require bringing together a variety of spheres of representational practice, namely those of domain experts, teachers and learners, as well as those of researchers and developers in the field of learning technologies

Semantic Maps Capturing Organization Knowledge in e-Learning

Abstract: e-learning, shows much promise in accessibility and opportunity to learn, due to its asynchronous nature and its ability to transmit knowledge fast and effectively. However without a universal standard for online learning and teaching, many systems are proclaimed as "e-learning-compliant", offering nothing more than automated services for delivering courses online, providing no additional enhancement to reusability and learner personalization. Hence, the focus is not on providing reusable and learner-centered content, but on developing the technology aspects of e-learning. This current trend has made it crucial to find a more refined definition of what constitutes knowledge in the elearning context. We propose an e-learning system architecture that makes use of a knowledge model to facilitate continuous dialogue and inquiry-based knowledge learning, by exploiting the full benefits of the semantic web as a medium capable for supplying the web with formalized knowledge