AnswerTree – a hyperplace-based game for collaborative mobile learning

In this paper we present AnswerTree, a collaborative mobile location-based educational game designed to teach 8-12 year olds about trees and wildlife within the University of Nottingham campus. The activity is designed around collecting virtual cards (similar in nature to the popular Top TrumpsTM games) containing graphics and information about notable trees. Each player begins by collecting one card from a game location, but then he or she can only collect further cards by answering questions – whose solutions are obtainable through sharing knowledge with other cardholders. This ostensibly allows each player to become a subject expert at the start of the game, encouraging collaborative interaction for the game to be successfully completed. In this initial paper we will outline the structure and background of this location based game. AnswerTree has been authored within the Hyperplace framework, and is a first implementation of a wider process to develop a flexible, multi-purpose platform for both individual and group location-based mobile learning

An Introduction to 3D User Interface Design

3D user interface design is a critical component of any virtual environment (VE) application. In this paper, we present a broad overview of three-dimensional (3D) interaction and user interfaces. We discuss the effect of common VE hardware devices on user interaction, as well as interaction techniques for generic 3D tasks and the use of traditional two-dimensional interaction styles in 3D environments. We divide most user interaction tasks into three categories: navigation, selection/manipulation, and system control. Throughout the paper, our focus is on presenting not only the available techniques, but also practical guidelines for 3D interaction design and widely held myths. Finally, we briefly discuss two approaches to 3D interaction design, and some example applications with complex 3D interaction requirements. We also present an annotated online bibliography as a reference companion to this article

Automated Interactive Visualization on Abstract Concepts in Computer Science

The paper presents CSVisFrame, a framework formaking visualizations, which solves the understanding difficultyon learning abstract concepts in computer science including datastructures and algorithms. With the framework, instructors anddevelopers can develop all varieties of interactive visualizations,with which students can learn and understand abstract conceptsin computer science more easily.CSVisFrame has been applied to both offline and onlinecomputer courses. Students from Sichuan Normal Universityhave enjoyed visualizations based on CSVisFrame in their AlgorithmDesign and Analysis course, and thousands of students ofJisuanke have benefitted from online CSVisFrame-based visualizedcomputer science courses. The effectiveness of CSVisFramebasedvisualizations has been demonstrated by our sample survey,which shows that visualizations are widely accepted, and almostall students can achieve a better learning. CSVisFrame is opensourced1,and demonstrations based on CSVisFrame are free2

Addressing data management training needs: a practice based approach from the UK

In this paper, we describe the current challenges to the effective management and preservation of research data in UK universities, and the response provided by the JISC Managing Research Data programme. This paper will discuss, inter alia, the findings and conclusions from data management training projects of the first iteration of the programme and how they informed the design of the second, paying particular attention to initiatives to develop and embed training materials

The Boston University Photonics Center annual report 2015-2016

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2015-2016 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that this year the Center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.9M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and cooperated in supporting National Science Foundation sponsored Sites for Research Experiences for Undergraduates and for Research Experiences for Teachers. As a community, we emphasized the theme of “Frontiers in Plasmonics as Enabling Science in Photonics and Beyond” at our annual symposium, hosted by Bjoern Reinhard. We continued to support the National Photonics Initiative, and contributed as a cooperating site in the American Institute for Manufacturing Integrated Photonics (AIM Photonics) which began this year as a new photonics-themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Development of Less Toxic Treatment Strategies for Metastatic and Drug Resistant Breast Cancer Using Noninvasive Optical Monitoring led by Professor Darren Roblyer, continued support of our NIH-sponsored, Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and an exciting confluence of new grant awards in the area of Neurophotonics led by Professors Christopher Gabel, Timothy Gardner, Xue Han, Jerome Mertz, Siddharth Ramachandran, Jason Ritt, and John White. Neurophotonics is fast becoming a leading area of strength of the Photonics Center. The Industry/University Collaborative Research Center, which has become the centerpiece of our translational biophotonics program, continues to focus onadvancing the health care and medical device industries, and has entered its sixth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

Ten Quick Tips for Using a Raspberry Pi

Much of biology (and, indeed, all of science) is becoming increasingly computational. We tend to think of this in regards to algorithmic approaches and software tools, as well as increased computing power. There has also been a shift towards slicker, packaged solutions--which mirrors everyday life, from smart phones to smart homes. As a result, it's all too easy to be detached from the fundamental elements that power these changes, and to see solutions as "black boxes". The major goal of this piece is to use the example of the Raspberry Pi--a small, general-purpose computer--as the central component in a highly developed ecosystem that brings together elements like external hardware, sensors and controllers, state-of-the-art programming practices, and basic electronics and physics, all in an approachable and useful way. External devices and inputs are easily connected to the Pi, and it can, in turn, control attached devices very simply. So whether you want to use it to manage laboratory equipment, sample the environment, teach bioinformatics, control your home security or make a model lunar lander, it's all built from the same basic principles. To quote Richard Feynman, "What I cannot create, I do not understand".Comment: 12 pages, 2 figure