Alexandra I. Cristea

Authoring of learning styles in adaptive hypermedia: problems and solutions

Learning styles, as well as the best ways of responding with corresponding instructional strategies, have been intensively studied in the classical educational (classroom) setting. There is much less research of application of learning styles in the new educational space, created by the Web. Moreover, authoring applications are scarce, and they do not provide explicit choices and creation of instructional strategies for specific learning styles. The main objective of the research described in this paper is to provide the authors with a tool which will allow them to incorporate different learning styles in their adaptive educational hypermedia applications. In this way, we are creating a semantically significant interface between classical learning styles and instructional strategies and the modern field of adaptive educational hypermedia.

The three layers of adaptation granularity

In spite of the interest in AHS, there are not as many applications as could be expected. We have previously pinpointed the problem to rely on the difficulty of AHS authoring. Adaptive features that have been successfully introduced and implemented until now are often too fine grained, and an author easily loses the overview. This paper introduces a three-layer model and classification method for adaptive techniques: direct adaptation rules, adaptation language and adaptation strategies. The benefits of this model are twofold: on one hand, the granulation level of authoring of adaptive hypermedia can be precisely established, and authors therefore can work at the most suitable level for them. On the other hand, this is a step towards standardization of adaptive techniques, especially by grouping them into a higher-level adaptation language or strategies. In this way, not only adaptive hypermedia authoring, but also adaptive techniques exchange between adaptive applications can be enabled.

Adaptive web-based educational hypermedia

This chapter describes recent and ongoing research to automatically personalize a learning experience through adaptive educational hypermedia. The Web has made it possible to give a very large audience access to the same learning material. Rather than offering several versions of learning material about a certain subject, for different types of learners, adaptive educational hypermedia offers personalized learning material without the need to know a detailed classification of users before starting the learning process. We describe different approaches to making a learning experience personalized, all using adaptive hypermedia technology. We include research on authoring for adaptive learning material (the AIMS and MOT projects) and research on modeling adaptive educational applications (the LAOS project). We also cover some of our ongoing work on the AHA! system, which has been used mostly for educational hypermedia but has the potential to be used in very different application areas as well.

Ontological Support for Web Courseware Authoring

In this paper we present an ontology-oriented authoring support system for Web-based courseware. This is an elaboration of our approach to knowledge classification and indexing in the previously developed system AIMS (Agent-based Information Management System) aimed at supporting students while completing learning tasks in a Web-based learning/training environment. By introducing ontology-based layers in the courseware authoring architecture we aim at using subject domain ontology as a basis for formal semantics and reasoning support in performing generic authoring tasks. We also focus on cooperative authoring, which allows re-usage and sets the basis for authoring collaboration. To exemplify our method we define a set of generic tasks related to concept-based courseware authoring and present their ontological support by the newly added operational and assistant layers in the AIMS architecture.

The LAG grammar for authoring the adaptive Web

The 3-layers of granularity model (LAG) have been introduced in A.I. Cristea and L. Calvi (2003) as a model for the adaptive behavior within adaptive hypermedia (AH). LAG is destined for authors of AH entering this process at the different levels of difficulty and flexibility: direct adaptation rules, adaptation language and adaptation strategies. The focus is on the different levels of the implied adaptation language grammars for conceptual structure and interface. We show their design, creation and implementation in MOT, an on-line adaptive hypermedia authoring system [MOT, http://wwwis.win.tue.nl//spl sim/acristea/mot.html]. We also show how the basic adaptation language can be extended by AH designers, via the adding of adaptive procedures.

Towards Generic Adaptive Systems: Analysis of a Case Study

This paper analyses one of the most well-known general purpose adaptive hypermedia systems, AHA!, and, based on this analysis, make some general observations about adaptive hypermedia systems and some improvement suggestions for the AHA! system. We suggest here a concept-based approach to the structuring of adaptive hypermedia systems, as well as an extension of the well-known rule-based overlay method for user-adaptation. This approach is another step towards flexible generic-purpose adaptive hypermedia.

Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA!, offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model, CAM, which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools.

Adaptation languages as vehicles of explicit intelligence in adaptive hypermedia

This paper deals with a new challenge in Adaptive Hypermedia (AH) and web-based systems: finding the adaptation language to express, independently from the domain model or platform, the intelligent, adaptive behaviour of personalised web courseware. The major requirements for the ideal language are: reuse, flexibility, high level semantics, and ease of use. To draw closer to this ideal language, we compare two such language proposals: LAG, a generic adaptation language, and a new XML adaptation language for Learning Styles (LS) in AHA!, LAG-XLS.

The next generation authoring adaptive hypermedia: using and evaluating the MOT3.0 and PEAL tools

Adaptive hypermedia allows for customization to the needs of the user. The authoring process however is not trivial, and is often the main hurdle to overcome in order to bring this useful paradigm to a greater number of users. In this paper, we discuss the major problems occurring in authoring of adaptive hypermedia, and propose a set of generic authoring imperatives, to be consulted by any system implementing creation tools for customization of content. Based on these imperatives, in this paper we extensively illustrate and discuss recent extensions and improvements we have implemented in the My Online Teacher (MOT) adaptation authoring tool set, including the MOT3.0 content authoring and labeling tool and the PEAL adaptation strategy author. Furthermore, we evaluate, compare and discuss two long term uses of the MOT tool set, first in 2008 and the second in 2009.

Towards Understanding Learning Behavior Patterns in Social Adaptive Personalized E-Learning Systems

Implicit user modeling has always long since played an important role in supporting personalized web-based e-learning environments and is increasingly important in other learning environments such as serious games. Its main concern is to unobtrusively and ubiquitously learn from a learner’s previous experiences and characteristics, in order to adapt the services to their personal needs. An empirical investigation for understanding learning behavior patterns forms the basis for establishing stronger implicit user modeling mechanisms and this study aims to get a better insight into types of learning behavior. The proposed usage of data mining and visualization elicited some interesting learning behavior patterns . We analyzed these from two perspectives: action frequency and action sequences , based on an expert-designed classification of behavior patterns that helped rank the various action categories according to significance from a user’s perspective. The initial results of the study are promising and suggest possible directions for further improving implicit user modeling.