Adaptive Hypermedia Research Articles

Interview with Helen Ashman

Helen Ashman has been interested in hypertext and user profiling for two decades now, initially in adaptive hypermedia systems, later including intrusion detection systems, and now in authorship attribution applications such as plagiarism detection and astroturfing detection. She was at the Australian Defence Science and Technology Organisation until 1997, during which time she was posted to the University of Southampton for one year. Later she moved to the University of Nottingham, returning in 2007 to Australia to join the University of South Australia where she is now. She was General Chair of ACM Hypertext 2003 in Nottingham, and joint programme chair in 2004 and 2016, as well as joint programme chair of Adaptive Hypermedia in 2006. She was joint Editor-in-Chief of the ACM Transactions on the Web 2005-2011. She served on the SIGWEB executive 2003-2005.

An Adaptive E-Learning System based on Student's Learning Styles

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's learning achievement. The findings support the use of learning styles as guideline for adaptation into the adaptive e-learning hypermedia systems.

A taxonomy for user models in adaptive systems: special considerations for learning environments

AbstractThis paper outlines the educational advantages provided by adaptive systems and stresses the importance of the user model in the task of adaptation. The paper also establishes a general taxonomy of user models thereby enabling classification of any model according to various features related to its structure and management. This taxonomy allows an exhaustive description of all user models, facilitating analysis and design of the corresponding adaptive systems. Subsequently, the taxonomy is used to describe the user modeling process in a concrete adaptive hypermedia system and to analyze the model of a student appropriate for use in a general educational environment.

Design and Assessment of Adaptive Hypermedia Games for English Acquisition in Preschool

Design and Assessment of Adaptive Hypermedia Games for English Acquisition in Preschool

Interview with Eelco Herder

Eelco Herder works as a senior researcher at the L3S Research Center in Hannover, Germany. His main research interests include Web personalization, User Modeling, usability and HCI in general. Other research topics include Web usage analysis and the development of tools for Personal Information Management. His Ph.D. research at the University of Twente focused on the observation, modeling and prediction of user navigation on the Internet. His work on context-aware revisitation support received the Douglas Engelbart Best Paper Award at Hypertext 2011. Eelco serves as general chair for Hypertext 2016 and served as program, workshop, poster, publicity and local chair at various conferences, among which Hypertext 2014, IUI 2014, CHI 2012, UMAP 2010--2013 and Adaptive Hypermedia 2008.

An experimental study to evaluate learning style personalisation in web-based adaptive e-learning systems

Learning style is one of the individual differences that play an important but controversial role in the learning process. On the other hand, previous research suggested that learning styles significantly affect student learning because they refer to how learners process and organise information. To this end, this paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a learning style based on adaptive e-learning hypermedia system (LS-AEHS) and assess the effect of adapting educational materials individualised to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's learning achievement. The findings support the use of learning styles as a guideline for adaptation into the adaptive e-learning hypermedia systems.

ADAPTIVE SEMANTICS WEB-BASED APPROACH FOR E-EDUCATION USING THE STATIC ANNOTATED CORPUS

In recent era popularity of E-education has been increased. Task of selecting an appropriate content for right education become a real challenge. This paper outline a semantic web based approach for E-learning using the static corpus platform. Adaptive hypermedia semantics technology using the available content become a one of the feasible solution for E-learning. In this paper, we describe a structure that use the semantic technology for exploring the database information by allowing intelligent navigation, personalize of information, retrieval of information by searching and querying. We also describe in detail about the webbased annotation and visualization of content and way to explore the personalized information in both printed and handwritten format on same viewport. The purpose tool also supports the functionality of finding, manipulation and organization of the new content.

Aprendizaje adaptativo en Moodle: tres casos prácticos

Uno de los retos más importantes que habrá de afrontar la educación es la necesidad de adecuar el proceso formativo a las características del estudiante. Hoy día todavía se señala el deficiente apoyo tecnológico y las escasas prácticas de personalización del aprendizaje. La tecnología educativa que apoya el e-learning ha ido desarrollando dos tipos de plataformas: Sistemas de Gestión del Aprendizaje (LMS) y Sistemas Hipermedia Adaptativos dirigidos a la educación. Ambas líneas de desarrollo están convergiendo, de forma que las nuevas versiones de los LMS incorporan capacidades adaptativas que van permitiendo diseñar cursos individualizados o diferenciados. En este artículo se revisan las funcionalidades adaptativas disponibles en Moodle. Se explica cómo se han puesto en práctica tres casos de diseños instruccionales adaptativos. Además, se constata su eficacia, en términos del aprendizaje logrado por el estudiante, y su eficiencia, al reutilizar materiales de experiencias anteriores.

ELM-ART – An Interactive and Intelligent Web-Based Electronic Textbook

This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms - Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of collaboration of two researchers with complementary ideas supported by talented students and innovative Web software. The authors present a brief account of this collaborative work and its outcomes. We start with explaining the “roots” of ELM-ART, explain the emergence of the “intelligent textbook” paradigm behind the system, and discuss the follow-up and the impact of the original project.

Content Analysis of Studies Conducted on Adaptive Educational Hypermedia Environments in Turkey

The purpose of this study is to analyze various studies on adaptive educational hypermedia environments, regarded as a new field of research and application, in a comprehensive way. To this end, all postgraduate thesis studies conducted in Turkey, articles in the journals whose scope covers educational sciences and which published in Turkey, and articles with Turkey address in the international journals indexed in SSCI were reviewed and content analysis was employed. The study will present current situation of this field and also, inform those who are interested in adaptive educational hypermedia environments concerning deficiencies in the field in the light of similar studies conducted abroad.

Modeling citizens’ urban time-use using adaptive hypermedia surveys to obtain an urban planning, citizen-centric, methodological reinvention

Traditional urban planning methodologies cannot solve today’s planning challenges that cities pose. Citizens’ needs have substantially changed the last decade while urban planning methods have remained the same since the 70s. A more citizen-centric urban planning is presented in this paper. To that end, and to effectively deal with citizens’ larger data needed for the urban planning methodological reinvention, interdisciplinary work is supported with information and communication technologies. We conducted a survey of citizens on urban time-use (Marsal MLl and López MB (2014) Smart urban planning: Designing urban-land-use from urban time-use. Journal of Urban Technology 21(1): 39–54). The results on urban-time-use distribution were converted into urban-land-uses as pioneering methodology for the reinvention of urban planning methodologies. In order to get the highest voluntary participation from citizens to ensure a good representation of all ages and social groups, the survey has to be designed with adaptive hypermedia techniques. The adaptive hypermedia techniques we propose in this paper combine stereotype and feature-based models which we explore for the purpose to include them in the survey. The combination of stereotype and feature-based models has different advantages, among others: stereotype techniques avoid to initiate survey profiles from scratch and feature-based techniques allow a personalized questionnaire to be employed. Moreover, personalization, in combination with user profiles, allows prediction which is of great interest for this research due to its planning purposes.

Development and Testing of New E-Learning Hypermedia System

Adaptive E-learning Hypermedia Systems (AEHS) are an innovative approach to a web learning experience delivery. They try to solve main shortcomings of classical hypermedia e-learning applications: “one-size-fits-all” approach and “lost-in-hyperspace” phenomena by adapting the learning content and its presentation to needs, goals, thinking styles and learning styles of every individual learner. This paper outlines a new approach to automatically detect learner's thinking and learning styles, and taking into account that thinking and learning styles may change during the learning process in an unexpected and unpredictable way. The authors' approach is based on the Felder Learning Styles Model and Hermann thinking styles model.

Constructing Interpretative Views of Learners’ Interaction Behavior in an Open Learner Model

In this paper, we discuss how externalizing learners' interaction behavior may support learners' explorations in an adaptive educational hypermedia environment that provides activity-oriented content. In particular, we propose a model for producing interpretative views of learners' interaction behavior and we further apply this model to INSPIREus for visualizing specific indicators. In the proposed approach, we collect raw data from learners' interaction at various grains, model the state of interaction using a set of indicators that combine temporal, navigational and performance data with semantic data of content and available tools, and visualize this information alongside with comparative information coming from the instructor or peers. In this way, we provide users (learners, tutors, peers) with a mirror of learners' behavior and a point of reference such as the instructor's proposal or peers' behavior, in order to enable monitoring and reflection. An empirical study with students is also described investigating how they interpret the visualizations of their interaction data provided by INSPIREus, the metacognitive skills they cultivate, and the personal data of peers they value for selecting collaborators. Preliminary results provide evidence about the understandability and expressiveness of the indicators of effort, progress, working style, and the visualizations used.

Use of infographics in virtual environments for personal learning process on Boolean algebra

Los avances que presenta la tecnología de vanguardia abren nuevas oportunidades para planear e implementar diversas estrategias pedagógicas en los entornos virtuales que permitan el desarrollo eficiente de las competencias en los estudiantes. Por consiguiente, las universidades enfrentan el reto de implementar innovadoras herramientas tecnológicas en el proceso de enseñanza-aprendizaje para facilitar la asimilación y utilización del conocimiento. En particular, el docente encargado de la asignatura Matemáticas Computacionales en el ciclo escolar 2014-II ha decidido utilizar la infografía en los contenidos audiovisuales que presenta el Sistema Hipermedia Adaptativo Usable (SHAU) con el propósito de mejorar el canal de comunicación entre la interfaz web y el usuario. Este sistema personaliza la información considerando el nivel de conocimiento que posee los estudiantes sobre el Álgebra Booleana y los estilos de aprendizaje visual-auditivo. El Enfoque cuantitativo a través del método ANOVA permite a esta investigación analizar el desarrollo del conocimiento de los 30 estudiantes antes y después de utilizar el SHAU 2.0. Finalmente, los resultados obtenidos permiten afirmar que el uso de la infografía junto con la tecnología de punta facilita el proceso educativo por medio de la construcción de los contenidos audiovisuales en base al Diseño Gráfico.

The new trends in adaptive educational hypermedia systems

This paper aims to give a general review of existing literature on adaptive educational hypermedia systems and to reveal technological trends and approaches within these studies. Fifty-six studies conducted between 2002 and 2012 were examined, to identify prominent themes and approaches. According to the content analysis, the new technological trends and approaches were grouped into seven categories: standardization, semantic web, modular frameworks, data mining, machine learning techniques, social web, and device adaptation. Furthermore, four challenges are suggested as explanation why adaptive systems are still not used on a large scale: inter-operability, open corpus knowledge, usage across a variety of delivery devices, and the design of meta-adaptive systems.

An individualised navigation's assistance approach: application and evaluation with the HiPPY prototype

We describe how to set-up an adaptive hypermedia supporting a fine grain learning resources called epistemes. This epistemic HyPermedia to learn PYthon language (HiPPY) is based on a graph of epistemes, a dynamic navigation and an individualised epistemic diagnosis. The process of usage analysis should enclose, building resources and pathways, installation of indicators from traces and mining educational trends. As a case study, we apply this process to integrate the HiPPY tool into the France-IOI platform. France-International Olympiads in Informatics (IOI) non-profit association is to prepare French teenagers for IOI. We used data mining algorithms and techniques to study trajectories and characterise solving process. We give first highlights about eventual strategies in relation with the use of the HiPPY graph. We also provide original elements concerning connecting effective traces and declarative ones to confirm or invalidate several hypotheses taking care of privacy issues.

The Application of Multi Layer Feed Forward Artificial Neural Network for Learning Style Identification

Accommodating learning styles in adaptive educational hypermedia systems (AEHS) improves students' learning performance in web-based learning. Hence, in implementing the systems, the students' learning style identification process is important. Most of today's AEHS rely on a traditional technique of identifying students' learning styles, which is using questionnaires. However, using questionnaires for this purpose is less efficient, cumbersome and may not be that feasible. This study proposes a real-time learning style identification technique by recording students' browsing behaviors and analyzing them by using multi-layer feed forward artificial neural network (MLFF). The result suggests that there is a relationship between the frequencies of students' click on learning components with their staying time on those components. It also indicates that the proposed identification technique performs well in identifying students' learning styles.

Authoring Adaptive Hypermedia using Ontologies

Adaptive hypermedia has been developed to overcome the problems of disorientation by providing personalized presentation and link structure. An adaptive hypermedia system consists of an adaptation model, a domain model, and a user model. The user model describes various aspects of a user such as interests, knowledge, preferences, etc. The domain model describes the whole knowledge accessible in adaptive hypermedia. The adaptation model consists of adaptation rules that define both how to generate the personalized presentation and update the user model [12]. Authoring adaptive hypermedia typically starts by designing a domain model so that apprropiate adaptation model and user model can be created based the domain model. While there are authoring tools developed for creating domain model of an adaptive hypermedia, authors need to manually create basic concepts as well as their relationships for a domain of interests. In this paper, we present a system that transforms an ontology into the domain and adaptation model of adaptive hypermedia so that the authors can generate adaptive hypermedia easily. The system transforms classes and relationships between the classes defined in OWL ontology [7] into concepts and relationships between the concepts defined in the domain model of AHA! system which is one of best known open source general-purpose adaptive hypermedia systems [1]. Using our system, authors can utilize well-defined knowledge structure in ontologies for authoring adaptive hypermedia. On top of this, since designing domain model is generally an initial step to author adaptive hypermedia, our system can help authors reduce tasks to create adaptive hypermedia by automatically generating domain model from an ontology.

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Dynamic Learning Style Prediction Method Based on a Pattern Recognition Technique

During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of “learning deviation” and “cognitive overload.” In this study, we propose a learning style prediction method based on a pattern recognition technique. The main contributions of this method are: (1) it is a form of middleware that can be applied to other intelligent tutoring systems, and (2) it can process topic-dependent data to make predictions and update learning style profiles in a recursive manner. Experimental evaluations demonstrated the effectiveness of this prediction method.