On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE

Educational software games aim at increasing the students’ motivation and engagement while they learn. However, if software games are targeted to school classrooms they have to be usable and likeable by all students. Usability of virtual reality games may be a problem because these games tend to have complex user interfaces so that they are more attractive. Moreover, if the games acquire an educational content they may lose the attractiveness and appeal that they have on users who are familiar with commercial games. Consequently, likeability may also be questioned. In this paper, we address the issue of usability and likeability of a virtual reality game that is meant to teach students geography. We describe the evaluation experiments conducted, which involved three categories of students in terms of their level of game-playing expertise: novice, intermediate and expert game players. The evaluation results showed that the game was indeed usable and likeable but there was scope for usability and likeability improvement so that the educational benefits may be maximised for all categories of students. The evaluation studies reported in this paper, revealed important issues about further research on virtual reality educational games.     

A formative analysis of individual differences in the effectiveness of learning objects in secondary school

The purpose of this study was to examine individual differences in the effectiveness of learning objects in secondary school classrooms. Specifically, gender, age, grade, subject area, and computer comfort (self-efficacy) were examined in 850 students. Effectiveness was measured in terms of student attitude (learning, quality, and engagement) and student performance. No gender differences were observed between males and females with respect to student attitudes or performance. Age was significantly correlated with student attitudes and performance, however correlation coefficients were small. Grade 12 students were more positive about learning objects and performed better than grade 9 and 10 students. Science students had significantly more positive attitudes and performed better than mathematics students. Finally, students who were more comfortable about computers, appreciated learning objects more than their less confident peers, however performance was unaffected.     

Use of a simulation game in delivering blended lifelong learning in the construction industry – Opportunities and Challenges

Continuing professional development (CPD) and life-long learning are vital to both individual and organisational success. For higher education, the intensive resource requirements requisite in the development of e-learning content and the challenges in accommodating different learning styles, developing an e-learning program can be a resource intensive exercise. A blended learning program has been developed in Ireland in an attempt to address the CPD needs of Irish construction professionals. This initiative attempts to strike a balance between the considerable resources required in the development of an e-learning initiative while addressing staff concerns in integrating technology in the delivery of programs. This pilot explores the issues encountered in integrating a simulation game, called MERIT, on a module as part of this blended learning program. The key finding from this research indicates that simulation games can play a very effective role in the delivery of lifelong learning opportunities aimed at the construction industry. However, management of the integration of a simulation game into a program requires careful planning, establishing key milestone dates and encouraging online collaboration through assigning of marks for effort and use of voice over IP communications.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

Using a style-based ant colony system for adaptive learning

Adaptive learning provides an alternative to the traditional “one size fits all” approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.     

Ad hoc网络中一种基于学习Petri网的入侵检测模型

移动自组织网络是由无线移动节点组成的复杂分布式通信系统.研究了移动自组织网络的入侵检测问题,对当前Ad hoc网络上的入侵行为和入侵检测技术进行了分析,论述了学习Petri网络应用于入侵检测系统中的优势,给出了一个基于学习Petri网络的入侵检测实施模型,并在网络仿真软件ns2中对其进行了评估.     

一种快速映射Isomap算法

传统的Isomap算法仅侧重于当前数据的分析,不能提供由高维空间到低维空间的快速直接映射,因此无法用于特征提取和高维数据检索.针对这一问题,文中提出一种基于Isornap的快速数据检索算法.该算法能够快速得到新样本的低维嵌入坐标,并基于此坐标检索与输入样本最相似的参考样本.在典型测试集上的实验结果表明,该算法在实现新样本到低维流形快速映射的同时,能较好保留样本的近邻关系.     

基于实例重要性的SVM解不平衡数据分类

在不平衡数据分类问题中,作为目标对象的少数类往往不易识别.常见方法存在需要显式设置实例重要度、仅仅间接支持少数类的识别等缺点.由此,文中提出基于实例重要性的支持向量机--ⅡSVM.它分为3个阶段.前两个阶段分别采用单类支持向昔机和二元支持向量机,将数据按照"最重要"、"较重要",和"不重要"3个档次重新组织.阶段3首先选择最重要的数据训练初始分类器,并通过显式设置早停止条件,直接支持少数类的识别.实验表明,ⅡSVM的平均分类性能优于目前的主流方法.     

基于改进量子遗传算法的过程神经元网络训练

针对过程神经元网络由于模型参数较多BP算法不易收敛的问题,提出一种基于量子位Bloch坐标的量子遗传算法.将该算法融合于过程神经网络的训练.按权值参数的个数确定量子染色体上的基因数并完成种群编码,通过新的量子旋转门完成个体的更新.算法中的每条染色体携带3条基因链,因此可扩展对解空间的遍历性,加速优化进程.以两组二维三角函数的模式分类问题为例,仿真结果表明该方法不仅收敛速度快,而且寻优能力强.