The effects of an active development of the mental model in the training process: experimental results in a word processing system

Abstract

Three different training programmes for a word processing system were experimentally compared: (1) a sequential programme, which taught low-level skills and which did not help the user actively to develop a mental model, (2) a hierarchical programme, which provided an explicit and integrated conceptual model of the system to the user and (3) a programme in which the users were asked to develop hypotheses on the functioning of the software and in which they were encouraged to use an active and exploratory approach. From an action theory point of view it was hypothesized that the third group would be superior to the first group. In an experimental study with two training sessions each of two hours and a two-hour testing session (n = 15), this was shown to be the case for several performance criteria (error time, transfer and experimenter rating). Additionally, an interindividual difference variable lo measure the individual learning style was used, giving results that could be interpreted in a similar way to the experimental results.     

Group attributional training as an effective approach to human resource development under team work systems

An experimental programme of group attributional training under team work system was conducted as part of human resource development in Chinese industrial enterprises. One hundred and ten shopfloor employees participated in the study. Among them, 58 employees took part in the factorial-designed experiment to find out the effects of attributions on performance, and 52 employees of ten work groups participated in the group attributional training programme twice a week for two months. The results showed that the group attributional training was effective in modifying employees' attributional patterns and enhancing group performance and satisfaction. On the basis of the results, an attributional model of work motivation is proposed, and its theoretical and practical implications for human resource management discussed.     

Computer-assisted learning in young poor readers: The effect of grapho-syllabic training on the development of word reading and reading comprehension

Two experiments examine the effects of two computer-based interventions, one with grapho-syllabic training (GST) and another with grapho-phonemic training (GPT) on the development of word recognition and reading comprehension in French children during Grade 1 and Grade 2. In Exp 1, poor readers (N = 27) in second grade were selected and divided into three equal groups, one GST group, one GPT group and a control group. After the session training (10 h), the children from the grapho-syllabic training group outperformed their counterparts in word reading. In Exp 2, poor readers in first grade (N = 18) were divided in two groups, a GST group and a GPT group. Six sessions were conducted in order to examine the possible long-term effect of training (10 h) during 16 months. The results revealed an effect of grapho-syllabic training on silent word recognition, word reading aloud and reading comprehension. A computer-assisted learning (CAL) system based on the syllable, which is considered to be the phonological and orthographic unit that is used by French young readers, could be a promising tool to help poor readers decode words and consequently boost their word recognition and reading comprehension capabilities.     

Towards the development of a virtual environment-based training system for mechanical assembly operations

In this paper, we discuss the development of Virtual Training Studio (VTS), a virtual environment-based training system that allows training supervisors to create training instructions and allows trainees to learn assembly operations in a virtual environment. Our system is mainly focused on the cognitive side of training so that trainees can learn to recognize parts, remember assembly sequences, and correctly orient the parts during assembly operations. Our system enables users to train using the following three training modes: (1) Interactive Simulation, (2) 3D Animation, and (3) Video. Implementing these training modes required us to develop several new system features. This paper presents an overview of the VTS system and describes a few main features of the system. We also report user test results that show how people train using our system. The user test results indicate that the system is able to support a wide variety of training preferences and works well to support training for assembly operations.

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User participation in the requirements engineering process: An empirical study

In the development of information systems, user participation in the requirements engineering (RE) process is hypothesised to be necessary for RE success. In this paper we develop a theoretical model which predicts that the interaction between user participation in the RE process and uncertainty has an impact on RE success. This theory is empirically tested using survey data. We develop instruments to measure user participation and uncertainty. An existing instrument for measuring RE success was used. This instrument covers two dimensions of RE success: (a) the quality of RE service, and (b) the quality of RE products. The results, indicate that as uncertainty increases, greater user participation alleviates the negative influence of uncertainty on the quality of RE service, and that as uncertainty decreases, the beneficial effects on the quality of RE service of increasing user participation diminish. Furthermore, we did not find that the interaction between user participation and uncertainty had an impact on the quality of RE products. Based on these results, we make recommendations for managing user participation in the RE process, and provide directions for future research.This work has been supported, in part, by the IT Macroscope Project and NSERC Canada.     

搜索引擎广告用户行为预测与特征分析

介绍了搜索引擎广告系统的基本运作模式。通过对广告四元组的特征提取、特征值平滑等操作, 将广告记录解析成为训练数据, 并将数据分为训练集和测试集, 使用支持向量机算法并利用训练集训练出的模型将测试集分类, 从而预测出用户的行为。通过对特征的分析, 得出对用户行为预测准确率影响最大的特征是点击率。实验证明, 在使用该模型中所有特征的情况下, 分类的准确率能够达到83. 17%。     

Quality control in the optical industry: From a work analysis of lens inspection to a training programme, an experimental case study

A cognitive work analysis of quality inspection in the optical industry has been carried out in order to devise a training programme. The task concerned the inspection of high quality human eyeglass lenses. We conducted an experimental investigation of defect detection and acceptability decision-making tasks in 18 experts and novice inspectors. Detection and decision-making were investigated together and separately in two experimental sessions. We showed the effect of expertise on reaction times and errors, and we described the cognitive processes of novice inspectors. On the basis of the processing differences between the two groups, a training programme for new inspectors was devised and described. Finally, training effects were tested.     

一种结合时间因子聚类的群组兴趣点推荐模型

推荐系统本质上是一种信息检索工具,它检索出有用信息并推荐给特定的用户.组推荐系统通过不同的融合策略融合群组偏好,支持群组用户访问当前的热门兴趣点.传统组推荐模型没有将时间因子对用户选择兴趣点的影响计算在内,且传统协同过滤推荐算法往往对数据的稀疏性较为敏感.本文提出一个混合推荐模型(AGRT),综合K-均值聚类算法和隐语义模型(LFM)技术,将其应用于群组兴趣点.考虑到用户在不同时间点的不同兴趣偏好,AGRT利用K-means算法对用户数据集合基于时间点聚类,划分为不同的簇,在与当前推荐时间最为接近的用户数据簇上进行兴趣点推荐,采用LFM隐语义模型对用户数据进行矩阵分解,通过将分解矩阵再次相乘获得用户对未评分地点的评分数据,解决用户数据稀疏性的问题.实验结果表明,AGRT模型在低相似度(随机)群组和高相似度群组评测条件下下较文献[3]中提出的HAaB提高了5. 19%和2. 06%,具有有效的改进.     

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

Achieving dialogue with children with severe autism in an adaptive multisensory interaction: the "MEDIAte" project

This paper presents an adaptive physical environment that allows children with severe autism to successfully interact with multimodal stimuli, giving them a sense of control of the interaction and, hence, providing them with a sense of agency. This has been an extremely important effort for two main reasons: 1) This user group cannot be typified, hence making the design of an interactive system to fit all the spectrum of individuals a very complex task; 2) each individual PAS (person on the autistic spectrum) user must be able to develop himself within the environment according to his own capacities and potentiality. Qualitative evaluation by psychologists shows very good results and sketches an encouraging future for research on these environments.     

基于评分相似性的群稀疏矩阵分解推荐算法

如何提高系统的推荐精度,是当前推荐系统面临的重要问题。对矩阵分解模型进行了研究,针对评分数据的群结构性问题,提出了一种基于评分相似性的群稀疏矩阵分解模型（SSMF-GS）。首先,根据用户的评分行为对评分数据矩阵进行分群,获得相似用户群评分矩阵;然后,通过SSMF-GS算法对相似用户群评分矩阵进行群稀疏矩阵分解;最后,采用交替优化算法对模型进行求解。所提模型可以筛选出不同用户群的偏好潜在项目特征,提升了潜在特征的可解释性。在GroupLens网站上提供的MovieLens数据集上进行仿真实验,实验结果表明,所提算法可以显著提高预测精度,平均绝对误差（MAE）及均方根误差（RMSE）指标均表现出良好的性能。     

Modeling user context with applications to media retrieval

In this paper, we develop a theoretical understanding of multi-sensory knowledge and user context and their inter-relationships. This is used to develop a generic representation framework for multi-sensory knowledge and context. A representation framework for context can have a significant impact on media applications that dynamically adapt to user needs. There are three key contributions of this work: (a) theoretical analysis, (b) representation framework and (c) experimental validation. Knowledge is understood to be a dynamic set of multi-sensory facts with three key properties – multi-sensory, emergent and dynamic. Context is the dynamic subset of knowledge that affects the communication between entities. We develop a graph based, multi-relational representation framework for knowledge, and model its temporal dynamics using a linear dynamical system. Our approach results in a stable and convergent system. We applied our representation framework to a image retrieval system with a large collection of photographs from everyday events. Our experimental validation with the retrieval evaluated against two reference algorithms indicates that our context based approach provides significant gains in real-world usage scenarios.     

Double‐fading support — a training approach to complex software systems

Abstract A new approach to software training is presented, the so‐called Double‐Fading Support (DFS) approach. According to this approach, which is based on Carroll's training‐wheels idea and on cognitive theories of skill acquisition, two types of user support when learning to use a complex software system — locking the software's functionality and detailed guidance — are faded out gradually during the training course, so that the learners are able to use the complex software with minimal instructional support at the end of the training. Two 30‐hour training experiments with two different CAD software systems and CAD‐inexperienced university students were conducted. The results of Experiment 1 with 88 participants indicate the effectiveness of the DFS‐approach for CAD software with a deeply structured menu system. Participants working with the initially reduced software outperformed participants of the full software functionality group; additionally, participants of the slowly faded guidance group outperformed participants receiving medium, fast or no fading of guidance at all. Results of Experiment 2 with 120 participants, however, indicate less effectiveness of the DFS‐approach for an icon‐based CAD software in which most of relevant functions are permanently visible to the user. It seems that the two factors (fading out the locking of software's functionality and fading out detailed guidance) overcompensate each other.     

It won’t happen to me: Promoting secure behaviour among internet users

Fraudulent activity on the Internet, in particular the practice known as ‘Phishing’, is on the increase. Although a number of technology focussed counter measures have been explored user behaviour remains fundamental to increased online security. Encouraging users to engage in secure online behaviour is difficult with a number of different barriers to change. Guided by a model adapted from health psychology this paper reports on a study designed to encourage secure behaviour online. The study aimed to investigate the effects of education via a training program and the effects of risk level manipulation on subsequent self-reported behaviour online. The training program ‘Anti-Phishing Phil’ informed users of the common types of phishing threats and how to identify them whilst the risk level manipulation randomly allocated participants to either high risk or low risk of becoming a victim of online fraud. Sixty-four participants took part in the study, which comprised of 9 males and 55 females with an age range of 18–43 years. Participants were randomly allocated to one of four experimental groups. High threat information and/or the provision of phishing education were expected to increase self-reports of secure behaviour. Secure behaviour was measured at three stages, a baseline measure stage, an intention measure stage, and a 7-day follow-up measure stage. The results showed that offering a seemingly tailored risk message increased users’ intentions to act in a secure manner online regardless of whether the risk message indicated they were at high or low risk of fraud. There was no effect of the training programme on secure behaviour in general. The findings are discussed in relation to the model of behaviour change, information provision and the transferability of training.     

The impact of self-efficacy and perceived system efficacy on effectiveness of virtual training systems

This study developed and tested a research model which examined the impact of user perceptions of self-efficacy (SE) and virtual environment (VE) efficacy on the effectiveness of VE training systems. The model distinguishes between the perceptions of one's own capability to perform trained tasks effectively and the perceptions of system performance, regarding the established parameters from literature. Specifically, the model posits that user perceptions will have positive effects on task performance and memory. Seventy-six adults participated in a VE in a controlled experiment, designed to empirically test the model. Each participant performed a series of object assembly tasks. The task involved selecting, rotating, releasing, inserting and manipulating 3D objects. Initially, the results of factor analysis demonstrated dimensionality of two user perception measures and produced a set of empirical validated factors underlining the VE efficacy. The results of regression analysis revealed that SE had a significant positive effect on perceived VE efficacy. No significant effects were found of perceptions on performance and memory. Furthermore, the study provided insights into the relationships between the perception measures and performance measures for assessing the efficacy of VE training systems. The study also addressed how well users learn, perform, adapt to and perceive the VE training, which provides valuable insight into the system efficacy. Research and practical implications are presented at the end of the paper.     

System design and user evaluation of Co-Star: An immersive stereoscopic system for cable harness design

The system design and results of a user evaluation of Co-Star an immersive design system for cable harness design is described. The system used a stereoscopic head mounted graphical display, user motion tracking and hand-gesture controlled interface to enable cable harnesses to be designed using direct 3D user interaction with a product model. In order to determine how such a system interface would be used by a designer and to obtain user feedback on its main features a practical user evaluation was undertaken involving ten participants each completing three cable harness design tasks with the system. All user interactions with the system were recorded in a time stamped log file during each of the tasks, which were also followed by questionnaire (5 point scale) and interview sessions with each participant. The recorded interaction data for the third task was analysed using functional decomposition techniques and used to construct a single activity profile for the task based on the mean results obtained from the participant group. The goal was to identify in general terms the relative distribution of user activity between specific purposes during practical system operation, and it was found that in this task Navigation accounted for 41%, Design 27%, System Operation 23% and looking at Task Instructions 9% of all user activity. The scored questionnaire data collected immediately after the completion of each task was used to rank the major features of the system according to user opinion. This was further enhanced by also collecting real interview comments from the user group about these same features. The combination of both quantitative performance analysis and subjective user opinion data obtained during a practical design exercise has enabled an in depth evaluation of the system, leading to a much greater understanding of many of the key user and interface requirements that should be considered during the development of immersive interfaces and systems for practical engineering applications.     

A correlative denoising autoencoder to model social influence for top-N recommender system

In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance.In most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training data.However,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network model.Towards this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation.We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,respectively.Especially,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same users.Moreover,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model.We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task.The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.