Online Staff and Student Interaction

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 ABSTRACT

Grid computing promises a standard, ‘complete’ set of distributed computing capabilities. In “Online Staff Student Interaction Project” we must provide basic functions such as resource discovery and information collection & publishing, data management on and between resources, process management on and between resources, common security mechanism underlying the above, process and session recording/accounting.

Main advantage of this project is, a network of distributed resources including computers, peripherals, switches, instruments, and data. Each user should have a single login account to access all resources.

We start by analyzing the nature of Grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the Grid.

INTRODUCTION

In this project, we examine the semantic aspects of e-learning from both pedagogical and technological points of view. We suggest that if semantics are to fulfill their potential in the learning domain then a paradigm shift in perspective is necessary, from information-based content delivery to knowledge-based collaborative learning services. We propose a semantics driven Knowledge Life Cycle that characterizes the key phases in managing semantics and knowledge, show how this can be applied to the learning domain and demonstrate the value of semantics via an example of knowledge reuse in learning assessment management.

As e-learning applications become more integrated and e-learning systems more distributed, there is an increased need to manage their software and data components . There is a trend in the distributed systems and middleware areas of computing towards Service-Oriented Architectures (SOA), these emphasize loosely coupled components that interoperate by providing distinct services through standardized interfaces. In particular the grid is evolving as an SOA for securely orchestrating and sharing stateful services and resources across distributed or virtual organizations .

Both web and grid service architectures have been applied to the e-learning domain , the argument is that they are advantageous as they are modular and extensible and offer increased interoperability to software producers. While grid services were originally conceived as a method of distributing high performance computation, they also offer benefits in distributed knowledge and information management, offering a guaranteed level of security that is essential for serious e-learning applications .

We believe that the semantic aspects of learning content are the key to facilitating large-scale collaboration of e-learning activities over service-oriented infrastructures. To use explicit and accurate semantics, a consensus in the domain at the conceptual level is necessary, so that computer and human participants can understand and communicate. An ontology is the best vehicle in this context to formally hold a pacification (of the conceptualization) that can be shared within the community to describe semantics accurately and consistently. An ontology explicitly defines the domain concepts and their relationships and is similar to a dictionary or glossary, but with richer structure, relationship and axioms that describe a domain of interest more precisely.

These rich semantics offer both teachers and learners new opportunities for locating and reusing resources. But defining the correct semantics for a learning application is difficult and maintaining ontologies can be problematic (akin to managing the evolution of a complex graph).

We propose a Knowledge Life Cycle for learning, to help define and maintain evolving semantics . Our intention is not to develop a definitive ontology or to promote a particular architecture, but to demonstrate how a semantic-driven Knowledge Life Cycle model can be applied to the learning domain.

In this paper, we present an overview of the semantics involved in learning, present the Knowledge Life Cycle and show the advantages of rich semantics via a demonstration of knowledge reuse.

Connecting communities: services can put people in contact with other people who are experts or learners with similar interests

 Personalized content: intelligent tutoring systems have for some time being delivering content that was personalized for the user, based on an understanding of their goals and previous knowledge

 Personalized sequencing: Adaptive Hypertext Systems attempt to provide pathways through materials by matching domain ontologies with dynamically evolving user models

Adaptive assessment: systems may choose questions for the learner at the boundary of their understanding; thus, improving the efficiency of assessment and providing feedback that provides detail in critical areas]

Feedback agents: intelligent agents that observe student behavior (e.g. assessment results, interactions with a virtual experiment, etc.) can attempt to provide feedback and links to suitable material to assist the learner

Recommender agents: the system could recommend alternative resources based on user searching and studying patterns. In a formal setting, it could query the syllabus and timetable to recommend a plan of study

Annotation tools: users could annotate information themselves, providing useful information for others and allowing both readers and other services the opportunity to process the information in alternative ways

Search engines: when resources have been semantically enriched, then search engines can be much more powerful. Where services are semantically enriched, search engines can choose suitable services to manage the query

Analytic tools: the e-science community is leading the way in the production of tools that harvest, store and analyze data from a range of sources.

How semantic enrichment can improve the management of learning

E-learning practitioners often comment that they believe they spend as much time

Organizing materials as they spend on teaching and the production of materials.

We believe that semantics may ease this problem in a number of ways:

Production of materials: production of teaching materials is a notoriously time-consuming task and the ability to locate and to reuse existing materials is a primary motivation for providing metadata for learning resources. The next stage is to provide services to assist in the location of suitable materials from heterogeneous sources.

Student management: an understanding of the roles of the actors (teachers, students, experts, assessors, etc.) makes the production of services for assigning students to the correct classes, discussion groups, experimental teams, etc., possible.

Timetable management: an important task for teachers of online tasks is the timing of events, such as the release of some new materials, the closing date of some assessment, the exact time of a synchronous group chat session, etc. These events can be made to happen automatically when a course is described in some language such as IMS Learning Design

Record keeping: record keeping and quality assurance can be the bane of a teacher’s life, requiring them to spend much time ensuring that all the results are kept in the correct places such as institutional enterprise systems, student portfolios as well as made available for QA purposes by whatever external authorities might be involved. All of this work is an obvious target for automation by services that understand the goals.

Quality assurance: quality assurance often involves the maintenance of sample work and feedback/reflections, as well as ensuring that new programmers, courses and assessments have been through appropriate validation. Again, this is a task that could be assisted by intelligent services, which could guide such tasks through the set of other services involved. 

EXISTING SYSTEM

Knowledge management has six problems in knowledge life cycle. That is acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. Grid are how to acquire, formally model, explicitly  represent, store, maintain, and update them, and  to use them to support seamless resource sharing and interoperability, so as to achieve a high degree of automation.

PROPOSED SYSTEM

We analyze the nature of Grid computing and identify its requirements for knowledge management. We further argue that an innovative and systematic approach to knowledge management on the Grid is required in order to help achieve the goal of the Grid.

Our contributions are three folds: First, we propose the Semantic Web-based approach to managing heterogeneous, distributed Grid resources for Grid applications. Second, we design architecture to realize the proposed approach and conceive a methodology which addresses the complete life cycle of knowledge management. Third, we apply the approach, concepts, and methodology to a real-world Grid application.

LIST OF MODULES

n  User Interaction

n   Admin

n  Staff

n  Student

MODULE DESCRIPTION

UserInteraction

In this module the admin, staff and students can have the rights to logon the system.

Admin:

In this module the admin can logon and view all the process which is done in the management. The admin can view the details of staffs and students. He also can view the material details, work which is assigned to the staffs and view the test results which is conducted by the staffs.

Staffs:

In this module the staff can maintain students’ details, materials, course details and the test which is given by them. In material details they prepare materials for students in required courses. In course details, what are all the courses which is available in e-learning system. In test, the staffs are conducting test to the students.

Student:

In student module, the students can search the course materials, download, upload and viewing the searching materials. The students can also view the course and material details and also view the result of test which was conducted by the staffs.

HARDWARE REQUIREMENTS

SYSTEM                          : Pentium IV 2.4 GHz

HARD DISK                    : 40 GB

RAM                                 : 1 GB

SOFTWARE REQUIREMENTS

Operating system           : Windows XP Professional

Front End                        : Microsoft Visual Studio .Net 2008

Coding Language         : Visual C# .Net

Web Technology            :ASP.Net

Back End                       : SQL Server 2005

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