4,200 research outputs found
Metrics for the Adaptation of Site Structure
This paper presents an overview of metrics for web site structure and user navigation paths. Particular attention will be paid to the question what these metrics really say about a site and its usage, and how they can be applied for adapting navigation support to the mobile context
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Navigation in hypermedia learning systems: Experts vs. novices
With the advancement of Web technology, hypermedia learning systems are becoming more widespread in educational settings. Hypermedia learning systems present course content with non-sequential formats, so students are required to develop learning paths by themselves. Yet, empirical evidence indicates that not all students can benefit from hypermedia learning. Research into individual differences suggests that prior knowledge has significant effects on student learning in hypermedia systems, with experts and novices showing different preferences to the use of hypermedia learning systems and requiring different levels of navigation support. It is therefore essential to develop a mechanism to help designers understand the needs of experts and novices. To address this issue, this paper presents a framework to illustrate the needs of students with different levels of prior knowledge by analyzing the findings of previous research. The overall aim of this framework is to integrate students’ prior knowledge into the design of hypermedia learning systems. Finally, implications for the design of hypermedia learning systems are discussed
Revisitation Patterns and Disorientation
The non-linear structure of web sites may cause users to become disorientated. In this paper we describe the results of a pilot study to find measures of user revisitation patterns that help in predicting disorientation
Investigating attributes affecting the performance of WBI users
This is the post-print version of the final paper published in Computers and Education. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Numerous research studies have explored the effect of hypermedia on learners' performance using Web Based Instruction (WBI). A learner's performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner's performance using a hypermedia system to accommodate an individual's preferences. The effect of learning performance is investigated to explore relationships between measurement attributes including gain scores (post-test minus pre-test), number of pages visited in a WBI program, and time spent on such pages. A data mining approach was used to analyze the results by comparing two clustering algorithms (K-Means and Hierarchical) with two different numbers of clusters. Individual differences had a significant impact on learner behavior in our WBI program. Additionally, we found that the relationship between attributes that measure performance played an influential role in exploring performance level; the relationship between such attributes induced rules in measuring level of a learners' performance
Hypermedia learning and prior knowledge: Domain expertise vs. system expertise
Prior knowledge is often argued to be an important determinant in hypermedia learning,
and may be thought of as including two important elements: domain expertise and
system expertise. However, there has been a lack of research considering these issues
together. In an attempt to address this shortcoming, this paper presents a study that
examines how domain expertise and system expertise influence students’ learning
performance in, and perceptions of, a hypermedia system. The results indicate that
participants with lower domain knowledge show a greater improvement in their learning
performance than those with higher domain knowledge. Furthermore, those who enjoy
using the Web more are likely to have positive perceptions of non-linear interaction.
Discussions on how to accommodate the different needs of students with varying levels
of prior knowledge are provided based on the results
Evaluation of a prototype interface for structured document retrieval
Document collections often display either internal structure, in the form of the logical arrangement of document components, or external structure, in the form of links between documents. Structured document retrieval systems aim to exploit this structural information to provide users with more effective access to structured documents. To do this, the associated interface must both represent this information explicitly and support users in their browsing behaviour. This paper describes the implementation and user-centred evaluation of a prototype interface, the RelevanceLinkBar interface. The results of the evaluation show that the RelevanceLinkBar interface supported users in their browsing behaviour, allowing them to find more relevant documents, and was strongly preferred over a standard results interface
Assessment of Cognitive Style Preference: A Conceptual Model
Research in adaptive hypermedia educational systems has increased with the growth of the Internet. Currently, all adaptive hypermedia educational systems collect information about cognitive style through completion of a questionnaire based on a psychometric test. This direct measure may be intrusive and annoying to a student and makes an adaptive system aligned to cognitive style unavailable for students that have not completed the questionnaire. It is posited that non-intrusive methods for determining the cognitive style of hypermedia system users are needed to maximize the usability, functionality, and goals of adaptive hypermedia systems. This paper offers a new approach for the autonomous computer-based assessment of preferred cognitive style that can support studies in user modeling and human-computer interface domains. It further posits a conceptual model that attempts to determine the preferred cognitive style of an online educational hypermedia user through click-stream analysis of their web-based hypermedia choices and browsing patterns
A proposal for the evaluation of adaptive information retrieval systems using simulated interaction
The Centre for Next Generation Localisation (CNGL) is involved in building interactive adaptive systems which combine Information Retrieval (IR), Adaptive Hypermedia (AH) and adaptive web techniques and technologies. The complex functionality of these systems coupled with the variety of potential users means that the experiments necessary to evaluate such systems are difficult to plan, implement and execute. This evaluation requires both component-level scientific evaluation and user-based evaluation. Automated replication of experiments and simulation of user interaction would be hugely beneficial in the evaluation of adaptive information retrieval systems (AIRS). This paper proposes a methodology for the evaluation of AIRS which leverages simulated interaction. The hybrid approach detailed combines: (i) user-centred methods for simulating interaction and personalisation; (ii) evaluation metrics that combine Human Computer Interaction (HCI), AH and IR techniques; and (iii) the use of qualitative and quantitative evaluations. The benefits and limitations of evaluations based on user simulations are also discussed
Multi Layer Feed Forward Artificial Neural Network For Learning Styles Identification
Accommodating learning styles in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning process as well as teacher and learner satisfaction. The premise is that a fact that learning in the classroom is less efficient, when teachers will not be able to get insight of each of the student’s learning style; hence, they won't be able to adapt their teaching strategies to match with the student’s learning style. In order to get an insight of the student’s learning style in AEHS, the system must be able to recognize the learning styles of the students. Current methods for recognizing learning styles are less efficient, where questionnaires will lead to tedium and disturbance at learning processes. Thus, this study developed the learning styles based AEHS that utilized Multi Layer Feed-Forward Artificial Neural Network (MLFF) which was used to identify student’s learning styles in real-time. The automatic and real-time learning styles identification was done by analyzing the student’s browsing behavior while they are learning through the proposed AEHS. The system then adaptively presents the learning content that matches with the students’ learning styles by the means of fragment sorting and adaptive annotation technique. At the end of the study, the data triangulation was done to test if incorporating learning styles in learning environments can impact the student achievement. It was done by asking the student to answer the mini quiz after they were using the proposed AEHS with adaptive feature was activated. This study also focused on analysis of the existence of the relationship between the frequencies of students’ click on learning components with their staying time on those particular learning components. The result showed that the proposed MLFF performed well in identifying the students’ learning styles in real-time. Moreover, the analyzed student’s browsing behavior revealed that there was a relationship between the frequencies of the students’ click on learning components with their staying time on those particular components. Furthermore, after the student’s learnt through the proposed AEHS with adaptive feature activated and answered the mini quiz result; most of them could achieve the perfect score. In this case, the mini quiz result showed that incorporating learning styles into learning environment may affect and increase student’s achievements
Personalised trails and learner profiling within e-learning environments
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
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