A mobile gamification learning system for improving the learning motivation and achievements

This paper aims to investigate how a gamified learning approach influences science learning, achievement and motivation, through a context‐aware mobile learning environment, and explains the effects on motivation and student learning. A series of gamified learning activities, based on MGLS (Mobile Gamification Learning System), was developed and implemented in an elementary school science curriculum to improve student motivation and to help students engage more actively in their learning activities. The responses to our questionnaire indicate that students valued the outdoor learning activities made possible by the use of a smartphone and its functions. Pre‐ and post‐test results demonstrated that incorporating mobile and gamification technologies into a botanical learning process could achieve a better learning performance and a higher degree of motivation than either non‐gamified mobile learning or traditional instruction. Further, they revealed a positive relationship between learning achievement and motivation. The correlation coefficient for ARCS dimensions and post‐test shows that the ARCS‐A (attention) is greater than ARCS‐R, ARCS‐C and ARCS‐S. This means that the attention (ARCS‐A) of this system is an important dimension in this research. The results could provide parents, teachers and educational organizations with the necessary data to make more relevant educational decision.     

A systematic mapping study of gamification models oriented to motivational characteristics

ABSTRACT

Context: Gamification focuses on the improvement of users' engagement when performing tasks by making use of game mechanics and elements in order to increase their motivations. Many researches have developed gamification models supporting a variety of motivational characteristics to provide engagement solutions for different areas. Objective: This paper carries out a systematic mapping in the field of gamification, looking for models with motivational characteristics in an attempt to characterise the state of the art of this field, identifying gaps and tendencies for further research. Method: We carried out a systematic mapping aiming at finding the primary studies in the existing literature, which were later classified and analysed according to twelve criteria. Results: We analysed 70 papers that resulted in 17 primary studies, published until September 2016. Most of them focus on Education, making use of Gamification to increase the motivation of a learning process. The gamification mechanics and elements most used were Badges/Achievements and Points/ExperiencePoints(XP), and most of the studies were not validated, thus not providing empirical evidence of the impact of gamification. Conclusions: Existing research in the field is somehow preliminary, and more research effort to analyse the applicability of the models and their respective evaluations would be needed.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

基于三步逼近的BD2高精度快速捕获算法研究

针对BeiDou-2（BD2）卫星导航软件接收机捕获模块中捕获精度低和运算量大的问题,提出了一种基于三步逼近的高精度快速捕获算法。算法采用变步长的三步逼近方法逐步缩小多普勒频移搜索范围,快速得到了精确的初始码相位和多普勒频偏;利用频率圆周移位方法,在第一步搜索以一次乘法代替FFT操作,较大地减少了捕获模块的运算负担。实验结果证明,该算法捕获的伪码偏移精确、载频偏移误差在100 Hz以内,且最优情况下的运算量比传统的并行码相位捕获算法平均减少了70.56%,运算效率高。     

Non-visual game design and training in gameplay skill acquisition – A puzzle game case study

This paper reports the results of a study on the design and evaluation of the game and techniques which allow puzzles to be played in the absence of visual feedback. We have demonstrated that a camera-mouse can be used successfully for blind navigation and target location acquisition within a game field. To gradually teach the players the sequential learning method was applied. Blind exploration of the gamespace was augmented with sticky labels and overview sound cues, verbal and non-verbal, which can significantly reduce the cognitive load and facilitate mental matching and integration. The full-sticky labels technique does not require fine motor skills and allows a user to gain control over the game with a minimum level of skills. With the vertical sticky labels technique training was focused on the development of accurate head movements only on a horizontal plane. With practice, the players can use the non-sticky labels technique. After 240 trials (3–4 h), the cumulative experience of the blindfolded players was increased 22.5–27 times compared to the initial 10 trials.     

The training of neural networks to model manufacturing processes

Neural networks have been increasingly used in various areas of manufacturing. Modelling of manufacturing processes, to allow experimentation on the model, is one of the areas in which successful applications have been reported. Most literature in this area is focused on network results. This paper concentrates on methods for training neural networks to model complex manufacturing processes. It summarises the use of neural network for process modelling in the past decade and provides some detailed guidelines for network training. A case study of a complex forming process is used to demonstrate a real implementation case in industry, and the issues arising from this case are discussed.     

A selectionist mechanism for autonomous behavior acquisition

The paper explores a selectionist approach for the autonomous acquisition of new behavior by a robot as it is attempting to stay viable in an ecosystem in which there are increasing pressures. The approach is based on a random exploration and subsequent selection of possible couplings between sensory quantities, actuator quantities, and internal motivational states. The paper gives some examples of behavioral acquisition from simulation experiments.     

Design and evaluation of robot patient for nursing skill training in patient transfer

We developed a robot patient for patient transfer training for simulating a patient’s performance during patient transfer and for enabling nurses to practice their nursing skills on it. To realize the robot patient, we focused on addressing the problems of designing its limb actions to enable it to respond to nurses’ operations. RC servos and electromagnetic brakes were installed in the joints to enable the robot to simulate a patient’s limb actions, such as embracing and remaining standing. To enable the robot to automatically respond to nurses’ operations, an identification method for these operations was developed that used voice commands and the features of the limbs’ posture measured by angle sensors installed in the robot’s joints. The robot patient’s performance was examined by a control test in which four experienced nursing teachers performed patient transfer with the robot patient and a human-simulated patient. The results revealed that the robot patient could successfully simulate the actions of a patient’s limbs according to the nursing teachers’ operations and that it is suitable for nursing skill training.     

针对色彩教育的游戏化设计及效果分析

当前的研究普遍关注于运用游戏化来提高学习的参与度，但对于色彩教育这样的特定领域的游戏化研究还不充分，而且缺少对游戏化要素及学习效果影响因素的分析。针对这一问题，设计了一款用于训练色彩辨识能力的游戏模型。首先设计了核心玩法相同、但交互方式不同的两种玩法；然后在这两种玩法中都加入了相同的虚拟奖励；最后分别比较在有或无虚拟奖励的情况下两种玩法对训练效果的影响，以及在相同玩法下有无虚拟奖励对训练效果的影响。结果显示，玩法设计影响学习效率，而虚拟奖励显著影响参与度。     

The cognitive benefits of dynamic representations in the acquisition of spatial navigation skills

A representational theory of the mind suggests that human experiences and activities are underpinned by mental representations. This abstract task representation paradigm may explain a cognitive benefit of dynamic instructional visualisations over static alternative in the acquisition of novel procedural motor skills. In this sequel work, we explore and extend this view through empirical investigations of novel skill acquisitions in a separate but related domain of spatial navigation. We compare the post-learning virtual maze navigational performance of sixty novel learners across two groups. After controlling for spatial orientation ability and prior video gaming experience, participants that learned the task using dynamic instructional visualisations recorded significantly better performance measures than those in the static group. Additionally, within-group comparisons also show that the beneficial advantage of dynamic instructional visualisations over statics remained consistent across different task complexities. These findings provide further evidence to support the view that dynamic instructional visualisations afford more efficient transfer of novel procedural skills through computer based training than static visualisations. This has implications for instructional design especially when rapid novel situational awareness is desired such as in briefings for emergency firefighting or tactical military operations.     

一种针对机器阅读理解中答案获取的序列生成模型

针对机器阅读理解任务中的答案问题获取提出一种序列生成模型SGN,首先,SGN在问题矩阵空间获取问题与文章的匹配表示,并参照潜在的问题信息生成当前节点的词向量;然后使用一个选择门结构从文章或者字典中选择当前词汇,并且自发学习和归纳OOV（out-of-vocabulary）单词,解决语义表述不准确的问题;最后使用改进的覆盖机制消除生成序列中的冗余问题,从而提高可读性。实验通过人工数据集SQuAD进行验证,其结果表明,在阅读理解任务上SGN生成的目标序列与基准模型seq2seq相比可读性更加优异,并且与原文语义更贴近。     

A computer-based system for the acquisition and analysis of data from a field tillage study

A computer-based data acquisition system is described that enables both continuous real-time control of data collection from some 250 sensors deployed at a remote field site, and immediate conversion from analogue data to the required physical parameter. In addition, data analysis programs can be developed and run concurrently with data collection. Important aspects of the computer operating system are discussed, as is the overall programming strategy for a field experiment. An account is given of the control of scanning and measuring instruments (connected to the computer via a standard parallel-interface bus), and of the technique adopted for utilizing the file management software to store processed data in an easily retrievable form. A FORTRAN program to control the measurement of soil temperatures is discussed; and an assessment is given of the equipment's performance in use.     

不完备混合决策系统的三支决策模型与规则获取方法

现有三支决策主要针对各类完备信息系统或不完备单一型信息系统进行研究,而现实应用领域中数据往往呈现不完备性和复杂性等特征,为此,构建面向不完备混合决策系统的三支决策模型与规则获取方法。首先,计算不完备混合数据的完备邻域容差类,并将其代替等价类计算三支决策模型的条件概率;然后,根据扩展的损失函数区间概念获取各对象在乐观、折中和悲观决策下的不同阈值,进而针对不完备混合决策系统构造三种决策风险下的三支决策模型。最后,通过理论分析和医疗诊断实例详细分析了算法的有效性和可解释性,并通过实验比较和分析可知,所构模型较其他已有模型的分类过程更加合理有效,同时该模型也扩充了三支决策模型和知识发现的理论与应用研究。     

Windows下的数据采集和A/D转换

主要论述了使用Visual C 6.0在Windows下实现数据的实时采集和信号的图形显示，其中包括数据采集、A／D转换以及信号的实时显示等。使用面向对象的编程方法，构造了一个通用的A／D转换类，具有很好的可重用性。     

A test of the Beggs' model for self-paced movements

Abstract

The model of human motor control developed by Beggs, Howarth and co-workers (eg., Beggs and Howarth 1970, Howarth et al. 1971) shows that in a paced terminal control task the variance of the lateral error distribution, σ², is a linear function of the square of the uncontrolled distance, d_v ². This formulation was tested for a self-paced path control task. Six subjects drew along tracks of four widths with the illumination removed at four different distances from the end of the path. Speeds and errors showed close correspondence to previous path control tasks. Lateral error did increase linearly with the square of the uncontrolled distance but at a lower rate for longer dark distances. Poorer control at shorter dark distances was interpreted as the subjects producing sufficient rather than optimal performance.     

高等职业学院物流信息技术专业人才培养目标定位分析

物流业的高速发展急需既懂物流管理又懂信息技术的复合型现代物流人才,这就要求高等职业学院(以下简称高职)在培养这种复合型人才时有明确的人才培养目标定位。本文通过对物流信息技术人才需求和高职物流信息技术专业(以下简称物信专业)面向的岗位群的分析,进行了目标技能分解,提出了物信专业人才培养目标定位的三个层次。     

切削刀具综合管理系统的数据采集和存储设计研究*

为建立满足汽车发动机制造企业需求的切削刀具综合管理系统,本文研究了该系统数据库的基础数据采集和存储问题。针对生产现场数据采集困难的问题,设计基于射频识别技术的数据采集方法,提高采集的效率和数据的可靠度。提出刀具与工步对应的存储方法,将刀具信息与生产线相应信息有效衔接。建立系统E-R模型,应用关系规范化理论进行数据存储分析,设计数据表及其相互关系,解决数据库存储的冗余问题。以上述研究成果为基础建立切削刀具数据库,并验证数据库企业实施的可行性。研究成果为建立具有高可靠性和操作性的刀具综合管理系统提供了理论和实践保障。     

A fast procedure for the training of neural networks

A new training paradigm for artificial neural networks is described. The technique utilizes a polynomial approximation to the sigmoidal processing function and directly integrates principal components analysis (PCA) into the network training philosophy. A major benefit of the new technique is that off-line network training is ‘one-shot’, contrary to the standard iterative techniques available in the literature. Further training may be performed on-line in a recursive fashion, yielding an adaptive neural network. Additionally, the new philosophy incorporates a systematic procedure for determining the number of neurons in the hidden layer of the network. The training procedure is first described and the implications of the training philosophy discussed. Some results, including applications to industrial chemical processes, are then presented to highlight the power of the technique. The systems considered are a continuous stirred tank reactor and a polymerization reactor.     

面向模型并行训练的模型拆分策略自动生成方法

随着训练数据规模的增大以及训练模型的日趋复杂,深度神经网络的训练成本越来越高,对计算平台提出了更高的算力需求,模型训练并行化成为增强其应用时效性的迫切需求。近年来基于分布式训练的AI加速器（如FPGA、TPU、AI芯片等）层出不穷,为深度神经网络并行训练提供了硬件基础。为了充分利用各种硬件资源,研究人员需要在集合了多种不同算力、不同硬件架构AI加速器的计算平台上进行神经网络的模型并行训练,因此,如何高效利用各种AI加速器计算资源,并实现训练任务在多种加速器上的负载均衡,一直是研究人员关心的热点问题。提出了一种面向模型并行训练的模型拆分策略自动生成方法,该方法能够基于静态的网络模型自动生成模型拆分策略,实现网络层在不同AI加速器上的任务分配。基于该方法自动生成的模型分配策略,能够高效利用单个计算平台上的所有计算资源,并保证模型训练任务在各设备之间的负载均衡,与目前使用的人工拆分策略相比,具有更高的时效性,节省拆分策略生成时间100倍以上,且降低了由于人为因素带来的不确定性。