Automated approaches to characterizing educational digital library usage: linking computational methods with qualitative analyses

The need for automatic methods capable of characterizing adoption and use has grown in operational digital libraries. This paper describes a computational method for producing two, inter-related, user typologies based on use diffusion. Furthermore, a case study is described that demonstrates the utility and applicability of the method: it is used to understand how middle and high school science teachers participating in an academic year-long field trial adopted and integrated digital library resources into their instructional planning and teaching. Use diffusion theory views technology adoption as a process that can lead to widely different patterns of use across a given population of potential users; these models use measures of frequency and variety to characterize and describe such usage patterns. By using computational techniques such as clickstream entropy and clustering, the method produces both coarse- and fine-grained user typologies. As a part of improving the initial coarse-grain typology, clickstream entropy improvements are described that aim at better separation of users. In addition, a fine-grained user typology is described that identifies five different types of teacher-users, including ??interactive resource specialists?? and ??community seeker specialists.?? This typology was validated through comparison with qualitative and quantitative data collected using traditional educational field research methods. Results indicate that qualitative analyses correlate with the computational results, suggesting automatic methods may prove an important tool in discovering valid usage characteristics and user types.     

Modeling educational usage of Facebook

The purpose of this study is to design a structural model explaining how users could utilize Facebook for educational purposes. In order to shed light on the educational usage of Facebook, in constructing the model, the relationship between users' Facebook adoption processes and their educational use of Facebook were included indirectly while the relationship between users' purposes in using Facebook and the educational usage of Facebook was included directly. In this study, data is collected from Facebook users with an online survey developed by the researchers. The study group consists of 606 Facebook users whose answers were examined by using a structural equation model. The analyses of the 11 observed and 3 latent variables provided by the model showed that 50% of educational usage of Facebook could be explained by user purposes along with the adoption processes of Facebook. It was also found that Facebook adoption processes could explain 86% of all user purposes. Finally, while Facebook adoption processes explained 45% of its educational usage, it could explain 50% of variance in educational usage of Facebook when the user purposes were added into the analyses.     

GOMS analysis as a tool to investigate the usability of web units for disabled users

Guideline compliance is a necessary but not sufficient condition to guarantee the usability of web units by disabled users, since efficiency-related issues can be as exclusive for disabled users as violations to basic guidelines. This paper shows that Goals, Operators, Methods and Selection rules (GOMS) analysis, which is an established method in user interface design, can be adapted to evaluate the efficiency of interface designs for disabled users. As examples, several GOMS models for the interaction behavior of disabled users with web units are described, showing how such models can be used to answer concrete accessibility-related questions. Advantages and limitations of GOMS analysis are also discussed.     

Construction of interaction observation systems for collaboration analysis in groupware applications

Interaction observation systems for groupware applications capture and process all the actions performed by users engaged in workgroups. These actions are then stored in log documents that enable the work process carried out by the users to be analyzed and the interaction between users to be studied. This article proposes an approach, based on ontological models, which is devised to help the developer of an observation system for a groupware application to structure and record user actions. In order to achieve this aim, we present a specific ontology that shapes the collaborative work process of the users so as to obtain an XML-based log document that stores all the actions carried out by the users and facilitates the subsequent analysis of the system usage and users’ behavior. This approach has been used to improve communication and collaboration capabilities in the COLLECE groupware application.     

Understanding post‐adoption IS usage stages: an empirical assessment of self‐service information systems

Organisations have invested in self‐service information systems (IS) to provide a direct interface for service delivery. Enriching the usage of these systems can provide organisations with immense benefits. However, limited research has been directed towards understanding post‐adoption IS usage behaviour in general and specifically in the context of self‐service IS. This study proposes post‐adoption IS usage behaviour as a broader concept constituting feature level usage of IS, integration of IS in the work system and exploration of new uses of IS. We evaluate how the new conceptualisation can be used to classify users at different stages of self‐service IS usage. Further, we examine user perceptions that differentiate among the users situated at different self‐service IS usage stages. Data were collected in the context of a self‐service Web‐based IS to validate the post‐adoption IS usage constructs and to examine the proposed thesis. The newly developed conceptual structure and measures for post‐adoption IS usage behaviour exhibit strong psychometric properties. The analysis shows three distinct post‐adoption IS usage stages and highlights that usefulness, user‐initiated learning, ease of use, satisfaction and voluntariness of use differentiate users at the different stages of post‐adoption IS usage. The results show that these variables aggregate into value confirmation and learning orientation as two higher‐level concepts. Further, we evaluate the predictive efficacy of the research model in classifying users into different post‐adoption self‐service IS usage stages. Implications are drawn for future research.     

User bias in online trust systems: aligning the system designers’ intentions with the users’ expectations

The advent of social networks opened a myriad opportunities for merging the social character of trust with the technical possibilities offered by the Internet and its availability as a mobile service. While most of the computational trust models aim to detect trustworthy entities, much less attention is paid to how these models are perceived by the users who are the core of the system. This paper delves into the workings of online trust systems under user bias and analyses the user behaviour through biases defined by Prospect theory. By performing empirical study on an existing system, we are able to demonstrate that there is a huge discrepancy between the aim of implementation of the online trust models and the users’ perception of those models. Understanding of this relation by the system designers can reduce complexity and improve the user experience and the system performance. The results imply that the tendency of the users to exhibit cognitive biases is not only the cause, but also the effect from the trust system design. These results and the analysis are then used to propose to the system designers a methodology for user bias identification and mitigation in the form of a Choice architecture for trust systems.     

Acquiring user models

Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed.     

Understanding user requirements and preferences for a digital library Web portal

This article reports the findings of a user study conducted in the context of the TELplus project to gain insights about user needs and preferences for the digital library services offered by The European Library Web portal. The user requirements collection for the Web portal was designed by adopting a comprehensive survey approach. This combined explicit user feedback with implicit usage data so as to provide a more in-depth analysis of user experience with the portal. The analysis conducted shed light on likely motivations for both participant usage and reluctance to use the services provided, leading to more informed decisions on how to refine, improve, and present Web portal services to their future users. The lessons learnt from this case study also contributed to the development of an integrated methodological framework which provided insights for the future design and evaluation of digital library Web portals and services.     

Model-Driven Interactive System Design for Therapy Robots

Physiotherapy using intelligent robots is emerging as a new approach to recovery for many stroke patients. Although therapy robots have a strong potential in dealing with therapeutic and other medical applications, they have not been fully utilized in everyday therapy activities due to concerns over safety and the lack of friendly robot user/patient interaction models. From the viewpoint of software engineering, a user-centred design based on UML (Unified Modelling Language) has been known to be one of the best solutions to satisfy usability since the design process relies heavily on the analysis of users and their tasks to reach their goals. Therefore, a model-driven approach to interactive system design via UML for therapy robots is needed to make them usable in the real world. This paper proposes such approach and introduces a new graphical notation that describes user interface elements and the methods of connection with hardware/software objects. With the proposed abstract interaction models, prototyping interactive systems can be made faster and allows for their evaluation by users and system developers before implementation in order to improve usability from the perspectives of users and system developers.     

A Context-Aware Interaction Model for the Analysis of Users’ QoE in Mobile Environments

This article describes a novel approach to model the quality of experience (QoE) of users in mobile environments. The context-aware and ratings interaction model (CARIM) addresses the open questions of how to quantify user experiences from the analysis of interaction in mobile scenarios, and how to compare different QoE records to each other. A set of parameters are used to dynamically describe the interaction between the user and the system, the context in which it is performed and the perceived quality of users. CARIM structures these parameters into a uniform representation, supporting the dynamic analysis of interaction to determine QoE of users and enabling the comparison between different interaction records. Its run-time nature allows applications to make context- and QoE-based decisions in real time to adapt themselves, and thus provide a better experience to users. As a result, CARIM provides unified criteria for the inference and analysis of QoE in mobile scenarios. Its design and implementation can be integrated (and easily extended if needed) into many different development environments. An experiment with real users comparing two different interaction designs and validating user behavior hypotheses proved the effectiveness of applying CARIM for the assessment of QoE in mobile applications.     

Adaptation in virtual environments: conceptual framework and user models

When interacting in a virtual environment, users are confronted with a number of interaction techniques. These interaction techniques may complement each other, but in some circumstances can be used interchangeably. Because of this situation, it is difficult for the user to determine which interaction technique to use. Furthermore, the use of multimodal feedback, such as haptics and sound, has proven beneficial for some, but not all, users. This complicates the development of such a virtual environment, as designers are not sure about the implications of the addition of interaction techniques and multimodal feedback. A promising approach for solving this problem lies in the use of adaptation and personalization. By incorporating knowledge of a user’s preferences and habits, the user interface should adapt to the current context of use. This could mean that only a subset of all possible interaction techniques is presented to the user. Alternatively, the interaction techniques themselves could be adapted, e.g. by changing the sensitivity or the nature of the feedback. In this paper, we propose a conceptual framework for realizing adaptive personalized interaction in virtual environments. We also discuss how to establish, verify and apply a user model, which forms the first and important step in implementing the proposed conceptual framework. This study results in general and individual user models, which are then verified to benefit users interacting in virtual environments. Furthermore, we conduct an investigation to examine how users react to a specific type of adaptation in virtual environments (i.e. switching between interaction techniques). When an adaptation is integrated in a virtual environment, users positively respond to this adaptation as their performance significantly improve and their level of frustration decrease.     

Understanding Usability and User Experience of Web-Based 3D Graphics Technology

As Web-based interactive 3D graphics (Web 3D), popularly referred to as Virtual Reality, continue to become more affordable, research and development groups in various fields have been adopting Web 3D technology. In addition to simulation of 3D content, the ability to instantly display alternative looks has been recognized as an innovative way to improve communication in such fields as product design, architecture, and e-commerce. Despite substantial adoption of Web 3D, how and how much the technology benefits target users as well as the providers who choose to adapt the Web 3D technology are not well understood. Previous research has established that interactive 3D graphics provide users with unique human–computer interaction (HCI). However, little is known about how users experience the Web 3D graphics technology and how user–system interaction contributes to system usability. The purpose of this study is to build new knowledge of the user experience with interactive-3D graphics systems used for product demonstration. By testing the impact of the technology on the user–system interaction and usability and comparing this impact with that of conventional two-dimensional (2D) graphics, this study tries to better understand the Web 3D technology from an interdisciplinary view of technology acceptance, sense of presence, and HCI. The study investigated how system usability is affected by HCI in the context of a furniture-style preference survey. The results of the study display the clear advantage for Web 3D for usability and show that perceived usefulness and sense of presence both mediate the effect of the technology treatment on the usability outcomes. The contribution of this study is that it includes empirical data to show how Web 3D benefits users when adopted in the context of a product demonstration and how the advantage is obtained through the user's interaction with the Web 3D technology.     

Assessing User Satisfaction in the Era of User Experience: Comparison of the SUS,UMUX, and UMUX-LITE as a Function of Product Experience

Nowadays, practitioners extensively apply quick and reliable scales of user satisfaction as part of their user experience analyses to obtain well-founded measures of user satisfaction within time and budget constraints. However, in the human–computer interaction literature the relationship between the outcomes of standardized satisfaction scales and the amount of product usage has been only marginally explored. The few studies that have investigated this relationship have typically shown that users who have interacted more with a product have higher satisfaction. The purpose of this article was to systematically analyze the variation in outcomes of three standardized user satisfaction scales (SUS, UMUX, UMUX-LITE) when completed by users who had spent different amounts of time with a website. In two studies, the amount of interaction was manipulated to assess its effect on user satisfaction. Measurements of the three scales were strongly correlated and their outcomes were significantly affected by the amount of interaction time. Notably, the SUS acted as a unidimensional scale when administered to people who had less product experience but was bidimensional when administered to users with more experience. Previous findings of similar magnitudes for the SUS and UMUX-LITE (after adjustment) were replicated but did not show the previously reported similarities of magnitude for the SUS and the UMUX. Results strongly encourage further research to analyze the relationships of the three scales with levels of product exposure. Recommendations for practitioners and researchers in the use of the questionnaires are also provided.     

User Involvement in Automatic Filtering: An Experimental Study

The aim in information filtering is to provide users with a personalised selection of information, based on their interest profile. In adaptive information filtering, this profile partially or completely acquired by automatic means. This paper investigates if profile generation can be partially acquired by automatic methods and partially by direct user involvement. The issue is explored through an empirical study of a simulated filtering system that mixes automatic and manual profile generation. The study covers several issues involved in mixed control. The first issue concerns if a machine-learned profile can provide better filtering performance if generated from an initial explicit user profile. The second issue concerns if user involvement can improve on a system-generated or adapted profile. Finally, the relationship between filtering performance and user ratings is investigated. In this particular study the initial setup of a personal profile was effective and yielded performance improvements that persisted after substantiate training. However, the study showed no correlation between users’ ratings of profiles and profile filtering performance, and only weak indications that users could improve profiles that already had been trained on feedback.     

On challenges of model transformation from UML to Alloy

The Unified Modeling Language (UML) is the de facto language used in the industry for software specifications. Once an application has been specified, Model Driven Architecture (MDA) techniques can be applied to generate code from such specifications. Since implementing a system based on a faulty design requires additional cost and effort, it is important to analyse the UML models at earlier stages of the software development lifecycle. This paper focuses on utilizing MDA techniques to deal with the analysis of UML models and identify design faults within a specification. Specifically, we show how UML models can be automatically transformed into Alloy which, in turn, can be automatically analysed by the Alloy Analyzer. The proposed approach relies on MDA techniques to transform UML models to Alloy. This paper reports on the challenges of the model transformation from UML class diagrams and OCL to Alloy. Those issues are caused by fundamental differences in the design philosophy of UML and Alloy. To facilitate better the representation of Alloy concepts in the UML, the paper draws on the lessons learnt and presents a UML profile for Alloy.     

ANATAGONOMY: a personalized newspaper on the World Wide Web

This paper describes a personalized newspaper on the World Wide Web (WWW), called ANATAGONOMY. The main feature of this system is that the newspaper is personalized without asking the users to specify their preferences explicitly. The system monitors user operations on the articles and reflects them in the user profiles. Differently from conventional newspapers on the WWW, our system sends an interaction agent implemented as a Java applet to the client side, and the agent monitors the user operations and creates each user's newspaper pages automatically. The server side manages user profiles and anticipates how interesting an article would be for each user. The interaction agent on the client side manages all the user interactions, including the automatic layout of pages. Our system has page multiple layout algorithms and the user can switch from one view to another anytime, according to the preference or machine environment. On one of the views, the user can even see all the articles sequentially without performing any operations. We evaluated a scheme in which the user scores each article explicitly, and a scheme in which all the personalization is done automatically. The results show that automatic personalization works well when some parameters are set properly.     

Prediction of individual learning curves across information visualizations

Confident usage of information visualizations is thought to be influenced by cognitive aspects as well as amount of exposure and training. To support the development of individual competency in visualization processing, it is important to ascertain if we can track users’ progress or difficulties they might have while working with a given visualization. In this paper, we extend previous work on predicting in real time a user’s learning curve—a mathematical model that can represent a user’s skill acquisition ability—when working with a visualization. First, we investigate whether results we previously obtained in predicting users’ learning curves during visualization processing generalize to a different visualization. Second, we study to what extent we can make predictions on a user’s learning curve without information on the visualization being used. Our models leverage various data sources, including a user’s gaze behavior, pupil dilation, and cognitive abilities. We show that these models outperform a baseline that leverages knowledge on user task performance so far. Our best performing model achieves good accuracies in predicting users’ learning curves even after observing users’ performance on a few tasks only. These results represent an important step toward understanding how to support users in learning a new visualization.     

基于用户日志的查询扩展统计模型

信息检索长期存在着用词歧义性问题,在Web搜索上的表现更加突出.提出了一种基于用户查询日志的查询扩展统计模型,将用户查询中使用的词或短语与文档中出现的相应词或短语以条件概率的形式连接,利用贝叶斯公式挑选出文档中与该查询关联最紧密的词加入原查询,以达到扩展优化的目的.实验结果表明,该方法更适宜改进Web上的信息检索,相对传统的查询扩展算法可以大幅度提高查询精度.     

Predicting user personality by mining social interactions in Facebook

Adaptive applications may benefit from having models of users? personality to adapt their behavior accordingly. There is a wide variety of domains in which this can be useful, i.e., assistive technologies, e-learning, e-commerce, health care or recommender systems, among others. The most commonly used procedure to obtain the user personality consists of asking the user to fill in questionnaires. However, on one hand, it would be desirable to obtain the user personality as unobtrusively as possible, yet without compromising the reliability of the model built. On the other hand, our hypothesis is that users with similar personality are expected to show common behavioral patterns when interacting through virtual social networks, and that these patterns can be mined in order to predict the tendency of a user personality. With the goal of inferring personality from the analysis of user interactions within social networks, we have developed TP2010, a Facebook application. It has been used to collect information about the personality traits of more than 20,000 users, along with their interactions within Facebook. Based on all the collected data, automatic classifiers were trained by using different machine-learning techniques, with the purpose of looking for interaction patterns that provide information about the users? personality traits. These classifiers are able to predict user personality starting from parameters related to user interactions, such as the number of friends or the number of wall posts. The results show that the classifiers have a high level of accuracy, making the proposed approach a reliable method for predicting the user personality     

UCS在AutoCAD三维绘图中的应用和技巧

用户坐标系(UCS)的三维变换是AutoCAD三维绘图的关键,它能为操作人员绘图带来极大方便。但是UCS的灵活多变性往往让初学者很难把握。本文介绍了UCS的定义和使用方法,并通过实例介绍了UCS在实体建模中的具体应用和技巧。