2.9 Research Directions
3.1.5 Agent Technology for e-Learning System Administration
The administration environment provides access for the management of all environ-ments, system components and support layers. The possibilities are ranging from sim-ple observation to the integration of new components or the update of existing ones. The access to components and the provided functionalities is limited by the access restriction of a particular user.
The most extensive access is possible for the administrators. All other user groups have access to their specific objects and to the adjustment capabilities of the environ-ments where they have access to.
A very important example of needed accessibility is the manageability of the user model for the depicted learner. If it is available and manageable for individuals it gives learner control and responsibility [Kernchen and Dumke, 2007]. Thereby it supports meta-learning activities like the monitoring of learning, the setting of personal learning goals; it is the basis for planning goals and supports the reflection about and the tracing of the learning progress by the comparison of set goals. As presented in figure 3.16, the AE needs connections to all other environments.
Regarding functionalities we grouped in the user, institutional and technical area.
Within the user area all aspects are pooled that are related to specific user tasks. Thereby not only learners, but all possible users have access to administration functionalities that are targeted to them, their tasks or resources. Institutional management facilities pro-vide access to services, functionalities and resources that are related to the management of meta-activities within the specific institution as e.g. user management, course man-agement, class manman-agement, study specification management and certification manage-ment. Management capabilities for the classic administrator role are pooled within the technical area.
Administration Environment
User Area Institutional Area Technical Area
User
Figure 3.16:The Administration Environment (cp. [Mencke and Dumke, 2007a])
In the following, chosen approaches for the usage of agent technology within the domain of e-Learning system administration are sketched.
3.1.5.1 Multi-Agent System for e-Learning and Skill Management
The Multi-Agent System for e-Learning and Skill Management (MASEL) presented e.g. in [Garro and Palopoli, 2002], [Garro and Palopoli, 2003] and [Garro et al., 2003]
targets the automatisation of certain tasks within the context of skill management for employees. That includes for example the individuation of student learning objectives, the evaluation of his competence gaps, the control of his improvements and the creation of the bridge between his individual learning objectives and the ones of the organisation in which he is integrated. The system’s architecture is presented in figure 3.17.
In MASEL agents are mainly used for communication between distributed compo-nents, for monitoring the environment, for autonomous operations, reasoning and to perform compley message-based operations. Therefore this system was implemented in JADE (cp. 1.2.6.1) making extended usage of XML for ontology representation and handling and for communication. The created MAS itself contains seven agent types and consists of at least one CLO Assistant Agent, one Skill Manager Agent, one Content Agent, one Learning Paths Agent, one CCO Assistant Agent, one User Profile Agent and n Student Assistant Agents, that are described below.
The CLO Assistant Agent (CLO) supports the Chief Learning Officer in defining a learning strategy for the intented user in terms of roles and required competencies based on the organisation’s learning objectives. Therefore the CLO supports the management of roles and competencies, the management of potential learners, the suggestion of pos-sible suitable employees for certain roles, the definition of priorities and constraints as
122 3 Agent-Supported e-Learning
SAAi
LPA
CCO
UPA
COA
SMA CLO
User profile database
Competence database
LOM & LO database
Figure 3.17:Architecture of the MASEL system (cp. [Garro et al., 2003])
well as the presentation of individual learning activities, based on historical data of the employees.
TheSkill Manager Agent (SMA) manages general skill information of the organisa-tion. Therefore all related data, including the ones processed by the CLO, are stored in an XML document. Additional data are the individual roles and competencies of em-ployees. This agent provides services to insert, delete and update individual and organi-sational role and competency information and functionality to query the data structures for certain reasons.
TheLearning Path Agent(LPA) tries to create learning paths to fill identified compe-tency gaps of the employees. It is related to the Student Assistant Agent (SAA) and it is used to create test to identify and evaluate the missing skills, to enrich and modify the learning path and to inform the CCA Assistant Agent for missing learning objects.
The already mentionedStudent Assistant Agent(SAA) is associated to an individual student and its task is supportive to fill his competency gap for a certain role. Therefore it presents information about the identifies competency gaps, presents the test created by the LPA, modifies the course based on user feedback and manages information about the learning progress.
TheContent Agent (COA) manages the database consisting the learning objects and thereby provides the content needed by the LPA and SAA to adapt a course. This agents inserts, deletes, modifies and queries the stored learning objects.
TheCCO Assistant Agent (CCO) supports the Chief Content Officer in dealing with the learning object database. Therefore it cooperates with the COA and can present the learning history of employees.
The last implemented agent type is theUser Profile Agent(UPA) for the storage of
and updates his competency levels (together with the SMA and SAA).
SAAi
LPA
SMA
Competence database
COA
LOM & LO database
Figure 3.18:Personalised learning path in the MASEL system (cp. [Garro et al., 2003])
To create individual learning paths with the implemented agents (cp. figure 3.18) different learning strategies can be applied, e.g. time minimization and knowledge maximization. The construction process is semi-automatic, three-tier and stops with a complete learning path, reaching the learning objective. Didactics is applied in terms of prerequirements that need to be fulfilled.
◦ Step 1:creation of a set of learning objects based on learning objectives
◦ Step 2:presentation of this set to the user
◦ Step 3:manual choice of appropriate learning objects as a subset