V. Wade, H. Ashman, and B. Smyth (Eds.): AH 2006, LNCS 4018, pp. 462 – 466, 2006. © Springer-Verlag Berlin Heidelberg 2006

Performance Enhancement for Open Corpus Adaptive Hypermedia Systems

Lejla Rovcanin1,2, Cristina Hava Muntean1, and Gabriel-Miro Muntean1

1 School of Electronic Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

{lejlar, havac, munteang}@eeng.dcu.ie 2 School of Electronic and Communication Engineering, Dublin Institute of Technology,

Dublin 2, Ireland lejla.rovcanin@dit.ie

Abstract. Adaptive Hypermedia Systems adjust the content to best suit users’ personal characteristics, but rarely consider delivery performance. Performance issues are even more significant in distributed architectures such as that of an Open Corpus Adaptive Educational Hypermedia System (OAEHS). This paper introduces a Performance Oriented Adaptation Agent (POAA) that enhances OAEHS by taking into consideration not only user personal characteristics but also network delivery conditions in the content selection process. The usage of POAA is expected to bring significant delivery performance improvements in terms of learner satisfaction and learning outcome.

1 Introduction

1.1 Adaptive Hypermedia Systems

The delivery of informational content to heterogeneous e-users presents significant challenges, which have been addressed in various areas of research. Adaptive Hyper-media Systems (AHS) identify user categories and deliver differentiated content tailored to individuals or groups based on user characteristics such as skills, goals, capabilities, knowledge, interests and preferences [1]. The AHS approach involves monitoring of the user’s interactions with the system, building of a user profile and, based on it, adapting various aspects of the delivered content to suit the user [2]. Possi-ble adaptations include content modifications (the content is adapted to best suit the users) and link adjustments (the link structure is tailored to guide the users towards relevant and interesting information) [1]. A comprehensive review of techniques used by the proposed AHS is provided in [2].

AHS proposed in education aim to improve both the overall learning outcome and the quality of user interaction with the system. Adaptive Educational Hypermedia Systems (AEHS) such as AHA! [3], InterBook [4], and ISIS-Tutor [5] seek to opti-mize learner experience with their online course material by personalizing this mate-rial to the learner’s individual learning requirements.

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1.2 Open Corpus Adaptive Hypermedia Systems

Existing AHS and especially AEHS are stand-alone systems dealing with a limited number of well-structured resources known at system design time. They are so-called closed corpus systems that use proprietary repositories to store the information to be delivered based on users’ characteristics and requests. The design and the develop-ment of such a repository are difficult and time-consuming tasks. These systems, although deployed in the Web context, provide no support to incorporate information from arbitrary Web locations.

The Web plays a key role in information access and dissemination, and it has be-come an integral part of the learning environment. Some issues, such as intellectual property rights, privacy, peer review, validity and quality of Web information are still to be addressed. These issues are addressed to a limited extent within existing Digital Educational Repositories (DER) that allow for safe storage, delivery, reuse and sharing of information. The reuse of existing information makes economic sense, improves efficiency, allows for interdisciplinary sharing of expertise and provides up-to-date and accurate learning resources.

Open Adaptive Educational Hypermedia Systems (OAEHS) are AEHS that operate with existing information resources such as DER. These systems use an open corpus of documents and adapt hypermedia documents to the individual needs of the user regardless of the origin or location of the materials. For example, the materials may be part of a tutorial, may refer to content from a personal Web page or could be learning objects (LOs) that belong to a digital repository of learning material. Such information space must be searchable, interoperable and accessible. OAEHS interoperability and content reusability have been addressed with a number of communication protocols (e.g. Open Hypermedia Protocol), guidelines and standards for the representation of resources (e.g. CMI). Many of the OAEHS separate links from documents. Links are kept in centralized locations for easy maintenance and are processed separately from the media to which they relate [6].

1.3 Performance Issues for OAEHS

Distributed computing, including devices for user input and display, the network capacity, connectivity, and costs may all change over time and place. The perfor-mance of distributed systems, including OAEHS is determined by both network con-ditions and end-user devices. Currently, Open AH research in the area of education places very little emphasis on delivery performance and its effect on the learning process. Context-related issues are addressed by Smith [7] and Dagger [8] who focus on the end-user device and Muntean [9] who considers network-related factors.

Existing DERs are large collections of LOs. In consequence oversupply of infor-mation may occur, disorienting the learner. In this context OAEHS are of significant benefit as they provide support for the selection of the best LOs for a particular learner, based on the learner’s interests, goals, background knowledge, learning style, etc. An issue that may arise is the network connection performance between the OAEHS front-end server and the source of the LO or the learner.

OAEHS can select an LO that perfectly coincides with the given learner’s profile, however, due to network performance issues, the user might perceive an unacceptable

464 L. Rovcanin, C. Muntean, and G.-M. Muntean

download time and be unhappy with their experience. Therefore learners have differ-ent perceptions of the same content and performance factors.

The delivery network conditions can change significantly, sometimes even within the duration of a given learning session. Metrics such as delay, jitter, loss, download time, etc. reflect the state of the network and can be monitored in order to determine performance-based adaptation measures. Such measures can be used to guide the selection of the LOs in response to existing network conditions. Therefore there is a need for performance-aware OAEHS that select the best LOs based both on perform-ance and the learner specific characteristics.

In this context, this paper introduces a Performance Oriented Adaptation Agent (POAA) that enhances OAEHS by considering network delivery conditions along user personal characteristics in the content selection process. The POAA and the simulation setup are presented in the following section.

2 Performance Oriented Adaptation Agent

OAEHS selection of LOs should be based not only on learner’s personal characteris-tics but also on network connectivity properties in order to allow for performance-efficient delivery. To address this issue, a Performance Oriented Adaptation Agent (POAA) is proposed. POAA calculates a network performance rating, which is subse-quently factored into the process of selecting the LOs to be delivered.

In a typical example of an existing OAEHS, the learner requests content on the cli-ent side. The OAEHS front-end server processes the request, selects appropriate LOs from various sources, builds a presentation suitable for the learner and delivers it. This relies on the fact that different sources may contain LOs with the same learning objectives.

We extend this architecture by deploying a POAA on the OAEHS front-end server. The POAA monitors network conditions between the OAEHS front-end server and DER servers. Network parameters considered are related to the delivery performance, such as download time, loss, delay and jitter. The values of these parameters are col-lected and stored separately for each DER in a sliding window-like structure. The POAA uses this data to calculate performance ratings. Every time a LO is selected and new performance information is acquired, the relevant DERs sliding window is updated.

The three-step LOs selection process is depicted in Fig. 1. The first step is rele-vance selection where the OAEHS identifies the learning outcome and selects LOs providing that learning outcome. These LOs may be distributed across several DER servers. The second step is personalization selection where the OAEHS shortlists a number of suitable LOs based on the user’s profile. Each LO is assigned a suitability rating for that learner. The third step is performance selection where POAA agent estimates performance rating for each suitable object. An LO is assigned a perform-ance rating based on the average computed on the performance values collected for the DER on which it resides. Suitability and performance ratings are combined. The LO with the highest cumulative rating is delivered to the learner. Some adaptive sys-tems, such as SASY [10] inform the user on omitted content. In the proposed system the learner cannot directly interfere in the selection process.

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Performance Enhancement for Open Corpus Adaptive Hypermedia Systems 465

PerformanceSelection

PerformanceRating

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Objective

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Personalisation PerformanceSelection

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Fig. 1. POAA-based learning objects selection process

Preliminary tests are currently performed using Network Simulator version 2.27 – NS2 [11]. NS2 is a discrete event simulator, with substantial support for simulation of protocols at various levels of the TCP/IP networking model over wired and wireless networks. The test setup is presented in Fig. 2. Clients (C1, C2, …, CN) and DER serv-ers (S1, S2, …, SM) are connected to a OAHES server (P) on which POAA was de-ployed. The initial simulations focus on the connections between the server and the DER servers (P-Si) consequently the network links between the clients and the server (Ci-P) are over-provisioned such that no loss or significant delays are expected. The network connections (P-Si) differ in terms of bandwidth and propagation delay. Communication between S1 and C1 benefits from POAA deployment, whereas that between Si-Ci, i>1 consists of background traffic.

Clients

S1

SM

C1

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Links of different bandwidth and delay

OAEHS serverPOAA

S2 Clients

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Fig. 2. Simulation topology

We assume that the system would be aware of servers that contain different LOs. The aim of these tests is to compare the delivery performance in terms of download time for a system that deploys the proposed POAA against those measured for a sys-tem that does not employ any intelligent selection of the content based on perform-ance. Results are expected to indicate a significant improvement in performance when using the POAA-based system in comparison with the classic system.

3 Conclusion and Future Work

This paper proposes a Performance Oriented Adaptation Agent (POAA) for Open corpus Adaptive Educational Hypermedia Systems (OAEHS). POAA enhances the

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existing selection process of learning objects (LOs) by taking into consideration not only the user personal characteristics but also network delivery conditions. The use of POAA for OAEHS brings significant delivery performance improvements and is expected to improve overall learners’ satisfaction as well as their learning outcomes.

The proposed agent could be used with existing OAEHS such as Knowledge Tree [12] to augment the current adaptation process.

The proposed approach will be more extensively tested with a higher number of clients, in different network topologies, against background traffic that varies in type, shape and size.

References

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9. Muntean, C.H. and McManis, J.: End-User Quality of Experience Layer for Adaptive Hy-permedia Systems. 3rd International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, Eindhoven, The Netherlands (2004), 87-96.

10. Czarkowski. M., Kay, J., Potts, S.: Web Framework for Scrutable Adaptation, Proceedings of Workshop on Learner Modelling for Reflection, International Conference on Artificial Intelligence in Education (2005), 11-18.

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L. Rovcanin, C. Muntean, and G.-M. Muntean Hava