Abstract: This paper presents an innovative approach that can facilitate Web-based collaborative hands-on activities in engineering education. We present a framework that helps to sustain the continuity of interaction in flexible learning. The proposed approach is based on Activity Theory and focuses on the mediation role of collaboration artifacts within a learning community in Web-based experimentation environments. The artifacts serve both as a medium and as a product of the collaboration process in the community. We have developed the eJournal as an implementation of collaboration artifacts to support knowledge acquisition and reinforcement in a collaborative way. The eJournal is integrated into the eMersion environment, which is currently used to supply online experiments for various practical courses offered by the School of Engineering at the École Polytechnique Fédérale de Lausanne (EPFL). The paper also presents two case studies and some evaluation results.

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Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated children’s exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naïve adult demonstrated the function, and at baseline. Preschoolers in the pedagogical condition focused almost exclusively on the target function; by contrast, children in the other conditions explored broadly. In Experiment 2, we show that children restrict their exploration both after direct instruction to themselves and after overhearing direct instruction given to another child; they do not show this constraint after observing direct instruction given to an adult or after observing a non-pedagogical intentional action. We discuss these findings as the result of rational inductive biases. In pedagogical contexts, a teacher’s failure to provide evidence for additional functions provides evidence for their absence; such contexts generalize from child to child (because children are likely to have comparable states of knowledge) but not from adult to child. Thus, pedagogy promotes efficient learning but at a cost: children are less likely to

Abstract. Machine Learning of Natural Language provides a rich environment for exploring supervised and unsupervised learning techniques including soft clustering and rough sets. This keynote presentation will trace the course of our Natural Language Learning as well as some quite intriguing spin-off applications. The focus of the paper will be learning, by both human and computer, reinterpreting our work of the last 30 years [1-12,20-24] in terms of recent developments in Rough Sets.

A Massively Multiplayer Online Game (MMOG) framework will be utilized to enhance learning in a distributed collaborative learning environment (CLE). The goal is to create a single interconnected learning environment that spans multiple courses. These online courses will share a reusable knowledge repository that contains information relevant to a field of study. We have selected four interrelated network security courses; computer networks, computer security, computer forensics and emergency management to create a self-sustaining interconnected learning environment. This model can then be utilized in other disciplines. A five- level MMOG game will be developed that requires the collaborative skills of students in multiple courses to engage in problem solving scenarios. As the students’ knowledge progresses they will be assigned to increasingly higher levels of problem- solving complexity, incorporated into an engaging 3D gaming framework. It is our hypothesis that students will learn faster and develop more complex interdisciplinary skills utilizing an MMOG than the current method of teaching individual separate courses.

Co-Principal Investigators. Any opinions, findings, conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation. Models and Moves Extensive research on students ’ understanding of science has documented persistent shortfalls at all ages. One way to account for students ’ difficulties is to consider the particular challenges posed by individual science concepts. This article offers an alternative view. We argue that students’ difficulties in large part reflect unfamiliarity with a small number of causal modeling styles charac-teristic of received scientific models. These include, for instance, explaining surface phenomena with an underlying mechanism, relying on constraint-system explanations as in Ohm’s law, includ-ing probabilistic elements as in chaos theory, and acknowledging causal webs and self-organizing systems as in ecologies--in sum, aspects of “complex causality. ” A further barrier to understanding is students ’ unfamiliarity with epistemic moves that might challenge their initial explanations, such as looking for missing links in a causal story or putting a model at risk. The article offers a frame-work for classifying aspects of complex causality in modeling and for supporting epistemic moves. Empirical research both from the literature and our own work is presented in support of the framework, including evidence that instruction based on causal models and epistemic moves enhances students ’ understanding of science concepts. 2

Abstract: A group of lower primary children (age from 8-9 years old) was given a task to present spatial information to communicate with their peers about wayfinding to their school from the nearby mass transit railway station. The children were left with their own preferential choice to decide what mode of spatial communication was used to do the task. Different frameworks of cartographic analysis were used to understand the mapping quality of such spatial communication if children opted to draw map to show the spatial information. Selected children from distinctive categories were interviewed to further illuminate their spatial consideration when drawing the map to complete the task. The outcome of this study is hoped to provide insight to teachers the venue to understand their children better in terms of their giftedness in spatial and visual ability. Such understanding enables teachers to better prepare for meaningful and effective teaching in spatial related topics and issues.

All sentences or passages quoted in this report from other people's work have been specifically acknowledged by clear cross-referencing to author, work and page(s). Any illustrations which are not the work of the author of this report have been used with the explicit permission of the originator and are specifically acknowledged. I understand that failure to do this amounts to plagiarism and will be considered grounds for failure in this project and the degree examination as a whole.

Mr. Chairman and members of the Panel, I thank you for the opportunity to speak today. I have spent 30 years researching the development of mathematical thinking and cognitive function. I have written curriculum for early elementary learners, and developed instruments for measuring early math skills. I am currently developing mCLASS:Math, a handheld computer-based assessment that guides teachers through diagnostic interviews that reveal K-3 students’ mathematical thinking and learning needs. Where this research-based formative assessment and diagnosis has been done in the past, it has led to significant learning gains (Black & Wiliam,

and practices