Adaptation Strategies: A Comparison between E-Learning and E-Commerce Techniques

Abstract

Abstract. The importance of e-learning and e-commerce applications has significantly increased in the past few years. Seeking better design and implementation principles is a research goal with, potentially, a significant impact. One of the commonalities of both applications is user-centricity. Understanding user behavior is critical especially in user-centered applications such as e-commerce and e-learning. In this work we discuss some of the fundamental similarities and differences in e-commerce and formal e-learning adaptation and discuss lessons that could be learned. We argue that current user pattern mining techniques should take into account behavioral and educational theories for distance learning in order to be efficient.Keywords: Adaptation strategies, comparison study, E-learning, E-commerce. IntroductionThe convergence of telecommunication and information technologies has enabled the realization of advanced tools for e-learning, on-line collaboration and content delivery. Two of the most popular applications of web based systems nowadays are elearning and e-commerce. Despite their differences, both types of applications are facing similar challenges: they rely on a "pull" model of information flow, they are hypermedia based, they use similar techniques for adaptation and they benefit from semantic technologies and Knowledge Management. The underlying business models also share the same basic principle: users access digital resources from a distance without the physical presence of a teacher or a seller. Since the importance of both applications has increased, better ways of designing and implementation are needed. Designers, developers and researchers of e-learning systems may learn some useful lessons from the e-commerce domain and vice-versa. There is a lot of common ground between the two types of applications; in this paper the focus is on understanding user behavior and the mechanisms that analyze, model and take advantage of it.

A survey of adaptive context-aware learning environments

“…A helpful question for classifying the method and the mechanism of adaption is: "How to adapt?" Previous works indicated two ways of answering the question: there are substitutional or structural methods of adaptation [117], and there are rule-based or algorithmic mechanisms of adaptation [110]. The proposed classification of adaptation methods, as originally proposed by Wilke and Bergmann [117], was initially intended to be used for case-based reasoning; some of its approaches were used in this study as a general adaptation classification.…”

“…The classification of adaptation mechanisms is based on the approach of Vassiliadis and Stefani [110], which was originally used for adaptive hypermedia systems. Rule-based adaptation is a mechanism where a learning environment assigns a value to the adapted target with help of predefined rules (e.g.…”

“…For the same reason, the number of algorithmic mechanism of adaptation is also likely to increase. Vassiliadis and Stefani [110] mentioned the potential efficiency of hybrid mechanisms that combine rule-based and algorithmic approaches for adaptation, but this idea has neither been proven nor discussed in depth. This efficiency is expected due to the powerful nature of a combination of complex algorithms that can handle massive amounts of data and user-defined rules, which are powered by human intelligence.…”