Designing a Better User Interface with a Context Path: An Empirical Study on User Learning Styles

This paper primarily examines the differences between how various users with different learning style preferences interact with computers in the global hypertext information network of the World Wide Web. Is it possible to design user interfaces that are equally effective for all the various computer users? This study was undertaken with two main goals in mind. Two aspects of research findings from empirical studies on usability of user interfaces on the World Wide Web are that: (1) it examines users who have varied styles of learning from visual, audio, to kinesthetic and their interactions with various user interfaces; and (2) it compares task performance, confidence, and satisfaction among various user interfaces on the Web. Implications of these results for various interfaces are discussed

Apply the We! Design methodology in E-learning 2.0 system design : a pilot study

During the emergence of Web 2.0, the methodologies and technologies of E-learning have developed to a new era, E-learning 2.0, emphasises on social learning and the use of social interaction tools. The students are the main end-user of the E-learning 2.0 systems, so it is essential to take students' opinions into consideration during the design process of such systems. The We!Design participatory design methodology is proposed for incorporating undergraduate students in the development of educational systems. This pilot study aims to investigate how the We!Design methodology would work and what the results might propose, and gather initial preferences and improve the quality and efficiency of the larger scale studies in the future

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients

Enhancing User Experience for Mobile Learning Using Augmented Reality and Learning Style

Current m-learning media are available in both native application and web-based application forms which create different user experience. Also, as the learners generally have diverse preferences and needs, a single style of m-learning may not meet such requirements. In order to formulate better and more attractive interaction between the learner and m-learning system, this study introduces augmented reality (AR) to both native and web applications as a means to improve captivation and create new user experience. A single group of learners is selected to try both types of application. The researcher groups the learner by learning style, using Felder-Silverman Learning Style Model, and then determines effectiveness of both forms of m-learning through an experiment. Experiment showed that all 4 learner groups were more satisfied with AR-enhanced native application based on user experience design due to its attractiveness and entertainment

Comparison of websites and mobile applications for e-learning

Information and communication technology (ICT) applied in the field of education is diverse in nature, and it is progressing continually. Advances in the development of smart phones in terms of both software and hardware capabilities have been considerable, and have provided new opportunities for e-learning. It can be argued that a key goal of companies is to produce applications that are productive, and more importantly, user friendly in nature so that they can deliver the best user experience to their customers. This paper reports on an investigation of user preferences when using an e-learning application designed to meet the needs of e-learners. Data was collected to gather evidence of their preferences with respect to both web and mobile applications. This study is part of a large research project, which aim to investigate the potential of e-learning within higher education using multiple e-learning applications. This paper undertakes the first phase of this research project. In the first phase, two user groups with a relatively similar age group (21-30 years) were asked to experiment the use of two different interfaces: one of a mobile application and the second of a web application. Both applications include information that aim to support international students. The information provided was based on one of the universities located in the USA. The information was obtained from the international office, which included facilities available, directions, events and workshops, important contacts, etc. Feedback on the use of both mobile and web applications was gathered using semi-structured interviews. Four interviews were conducted with two participants from each of the user groups within this study. The results indicate that both background and experience of using ICT applications highly influenced how both (web and mobile) applications were perceived. The analysis show that type of information and its representation play an important role in determining its efficiency and usefulness for the user. This study draws an important insight into the future of both web and mobile applications within the higher education environment. The next phase following this study aims to examine the results gathered in this study on a wider audience. This study provides an important foundation towards support understanding potentials and limitations for both web and mobile applications

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches