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?Deriving Action-based Semantics from LearningRepositoriesOlivier Constant*º, Christine Mingins*, Annya Réquilé -Romanczuk*º and Brian Yap*,http://www.csse.monash.edu.au/projects/LEOPARDrepositories of learning objects with gusto. InAustralia,* School of Computer Science and Software Engineering,Monash University, PO Box 197 Caulfield East, Victoria 3145, Australiaº Ecole des Mines de Nantes, La Chantrerie, 4 rue Alfred Kastler, BP 20722,44307 Nantes Cedex 3, FranceAbstract-- Motivated by recent developments in Category theory,we have designed a generic virtual layer that overlaysrepositories of learning objects. Agents embedded in this layerobserve traversals from both the repository and the userperspective, and support the inference of dynamic semanticsbased on actual usage. We will experiment with the dynamicallygenerated metadata with the goal of enhancing users’navigation and discovery experiences.for example, “… all States, Territories and the Commonwealthof Australia are collaborating in this Initiative-The Le@rningFederation-to generate, over time, online curriculum contentfor Australian schools. “ Our concern is that coded metadata,hard indexes and search mechanisms will provide insufficientsupport for content users to explore and discover usefulmaterials in very richIndex Terms--Multi-Agent, Learning Objects, Intelligentenvironment, User pathway, Recommender Systems.TI. INTRODUCTION AND MOTIVATIONhe learning community has adopted the idea ofOlivier Constant is a student in the EMOOSE Master of Science atEcole des Mine de Nantes in France, currently completing his MastersThesis at Monash University. (e-mail: Olivier.Constant@eleve.emn.fr ).Christine Mingins is Associate Professor in the School of ComputerScience at Monash University (e-mail: cmingins@csse.monash.edu.au ).Annya Réquilé-Romanczuk, Ecole des Mines de Nantes, France, iscurrently on sabbatical in the School of Computer Science and SoftwareEngineering at Monash University (e-mail: arequile@csse.monash.edu.au).Brian Yap is a research student at Monash University. (e-mail:brian.yap@csse.monash.edu.au)ICITA2002 ISBN: 1-86467-114-9and complex repositories. We have designed a generic,virtual layer that sits over repositories and collects informationabout users’ traversals. Inspired by the Prototype Categorytheories of Elinor Rosch [11] and George Lakoff [12], we intendto experiment with the information derived from thesetraversals to attempt to infer action-based semantics about therepository. For example, we may ‘discover’ communities ofusers and categories of content that are not explicit in theindexes; We will use this derived metadata to inform userprofiles and more generally attempt to enhance the experienceof the users, content providers and site managers innavigating, discovering and managing the material in therepository.This paper describes the architecture of this ‘businessintelligence layer’.II. A CASE STUDY WITH LEARNING OBJECTSThe multi-agent architecture presented in this paper isdesigned to address the problem of making effective use ofMSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 60
very large repositories of learning objects. In this section wedescribe the case study in which this problem appears.Technological advances in the past few years, particularly inthe area of online delivery and e-learning, have inspiredchanges in the way educational materials are designed,developed, and delivered to teachers and students. A majorshift in educational materials development has occurred wherethere is a move away from the traditional method of developingcourses in an integrated way to accomplish a learningobjective to one that is based on the use of individual buildingblocks or bite-sized “learning objects” [8]. This approachresembles what Wayne Hodgins, Director of WorldwideLearning Solutions, has called the Legos TM approach. In thesame way that Legos TM building blocks can be used to build avariety of structures, so too learning objects can be used bylecturers, teachers and others in creative ways to build courseswhich meet different learning outcomes. The theoreticalunderpinning is that instead of thousands of people wastingtime “re-inventing the wheel”, a learning object onceconstructed can be re-used and shared. Steven Downs [9] hasargued that the economics of sharing learning objects arerelentless.This new approach has resulted in the establishment of alarge number of learning object repositories both withinAustralia and overseas.While theoretical underpinnings of learning objectrepositories are difficult to challenge, a number of issuesrelating to their establishment still need to be researched. Ourproject will address major problems/issues, such as the lack ofconsultation/analysis of user needs in the creation ofrepositories and inadequate resource dis covery tools. It hasbeen reported that many of the learning repositories aredifficult to use.We will try to address these problems:- by undertaking user needs and usages analysis, and byusing the data collected by the multi-agent component(see fig.1);- by facilitating access to the plethora of contentrepositories and to address the problems of locating,exploring and manipulating learning resources expertlyand creatively;- by using the Learning Object Exploration System, crossdomainsearching software and profiling systems toautomatically match the needs of users with theappropriate learning objects;- by using “intelligent” agents to “remember” frequentlyused and relevant resources and to inform usersthrough presenting more intelligently guided pathwayswithin that virtual environment.The project will deliver an intelligent Learning ObjectExploration System capable of identifying the needs ofteachers, lecturers and course builders. This “intelligence” willbe based on “profiling”, extensive user analysis and resourceassessment, and the construction of an “intelligent” agents toprovide appropriate feedback.The development of an intelligent learning architectureincorporates:- the ability to actively collect and access a wide rangeof content repositories- substantial improvements in the usefulness oflearning objects within any given repository leadingto enhanced exploitation of learning materials .- the provision of enhanced feedback for the bettermanagement of content repositories.III. MULTI-AGENT ARCHITECTUREIn this section we describe this architecture. An overall viewof the architecture is depicted in Fig.1.userInteracts withLearning Objects ExplorationSystemWebBrowserCommunicates withWebServerGives access toLearningObjectRepositoryLearningObjectProvides exploration assistancePlugs web contentsWritesLogLearningObjectReadsBusiness IntelligenceComponentArchitectureAccessesExplorationSystem InterfaceFig. 1. Overview of the whole systemAccessesStandard UserProfilesReadsAgents ComponentUser AgentsComponentNode AgentsComponentThe prototype is based on an agent infrastructure and agentframework developed in C#. The infrastructure supports multihostsdistribution via .Net’s remoting and MSMQ. PersistentMSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 61
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Vrije Universiteit Brussel - Belgiu
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ContentsAbstract 1Acknowledgements
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(4) Table UP_SUP (matches User Prof
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content providers’ interpretation
Page 11 and 12: - Active profiling consists in aski
Page 13 and 14: 2 Agents2.1 Overview2.1.1 Definitio
Page 15 and 16: (1) each agent has incomplete infor
Page 17 and 18: MASs need an organization for speci
Page 19 and 20: However, the usefulness of personal
Page 21 and 22: they often provide implementation o
Page 23 and 24: 2.5 Design2.5.1 The DIMA designIn [
Page 25 and 26: 3 MetadataDuring the earlier invest
Page 27 and 28: This requires some agreement and st
Page 29 and 30: 4 PerspectivesRecommender systems m
Page 31 and 32: way Lego blocks should be assembled
Page 33 and 34: usiness intelligence is generated.
Page 35 and 36: important than memory space optimiz
Page 37 and 38: The graph is then formatted to be v
Page 39 and 40: Chapter 4: Implementation1 Implemen
Page 41 and 42: (1) A Name Server (NS) maps every a
Page 43 and 44: the agent terminates, the thread is
Page 45 and 46: 4 Application level4.1 OverviewThe
Page 47 and 48: 4.3 User Agent MakerThis agent has
Page 49 and 50: Thus the profile, node and link cla
Page 51 and 52: BIProviderR : Provide_Business_Inte
Page 53 and 54: to enable users to explicitly speci
Page 55 and 56: ConclusionA “minimalist” approa
Page 57 and 58: Appendix A: Addendum onIndustrial I
Page 59 and 60: Appendix B: SQL Tables Design1 Tabl
Page 61: Appendix C: Conference PaperA paper
Page 65 and 66: popularity of the link (the bigger
Page 67 and 68: Appendix D: Source Code1 Agent infr
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Page 71 and 72: string path = MakePath(".", Middlew
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Page 75 and 76: public static NameServer GetNS(){if
Page 77 and 78: }}}table[agentId] = address;}}/// /
Page 79 and 80: ****** NON-STATIC PART ******// Tab
Page 82 and 83: }}}return (AgentId) first;using Sys
Page 85 and 86: {}return Subject.GetHashCode() + Na
Page 87 and 88: name in order to allow for the retr
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Page 91 and 92: }DirectoryFacilitator.GetDF().Unreg
Page 93 and 94: {/// /// Allows for specific conver
Page 95 and 96: }Monitor.Exit(aLock);public overrid
Page 97 and 98: Allows for synchronized Message exc
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Page 101 and 102: }}private void RunTerminationExamin
Page 103 and 104: }private Message RequestUACreation(
Page 105 and 106: }msgQueue.AddFilters(new MessageCat
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Page 109 and 110: 19. Class Architecture.DBManagerAg
Page 111 and 112: }}connection.Close();/// /// For sa
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using System.Collections;namespace
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namespace Architecture.Profiles{usi
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private UP(DataRow row) : this(newU
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}DataRow[] rows = Row.GetChildRows(
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}adapter.Update(Data);//***********
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internal static readonly string LAS
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}}Row[OCCURRENCES] = Occurrences +
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}get{}return Data.Rows.Find(_name);
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private static SqlDataAdapter adapt
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}foreach (ILink link in links){if (
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}}}private DataRow Row{get{return D
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18. N. Good, et al. Combining colla
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49. D. Derbyshire, et al., Agent-Ba
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83. Council for Higher Education Ac
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