Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. There are a number of methodologies have been proposed for multiagent engineering process such as Multi-agent System Engineering (MaSE). I have used the MaSE engineering process for the development of my ontology based multiagent system for the Academic Institute. In the era of Semantic Web, the ontology has established as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous distributed systems. Both ontology and agent technologies are central to the semantic web, and their combined use will enable the sharing of heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. Ontology is used throughout the multi-agent system to assist the interactions among different agents as well as to improve the quality of the service provided by each agent. This paper focuses on the utilization of combining both Ontology and Multi-Agent System (MAS) structure towards system integration for University teaching environment. In this paper, I include system prototype for ontology based multi-agent system. I have used Web Ontology Language (OWL) for the development of domain ontology for the Academic Institute and KQML as an agent communication language. Finally I have developed ontology-based multiagent system for the university teaching environment by taking the benefits of both renowned technologies.

For generating comprehensive and precise analysis, Decision Tree technique is found as most adequate technique. Usually decision trees are used in data mining to study historical data and on the basis of the data analysis and its rules, one can predict the result. Most of the higher education institutions are suffering from low percentage of result, placement and interest of the students. To address this issue, we have suggested one Decision Support System using decision tree which predicts the post graduation stream for the students on the basis of their past academic performance. Prediction of students' performance is a great concern to the higher education institutions. So, this paper covers all the parameters which have some influence in student's performance. In this investigation, a survey cum experimental methodology is adopted to generate the data store. Paper also discusses use of decision tree for the prediction. Decision tree algorithms are applied on Post Graduate students who are either pursuing or have completed. Academic history and social data are collected and used to design the model. This model is used for the prediction of students' performance.

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Evaluation Roles and Means are very broad. It concerns with n number of attributes. This paper discussed the relationship between evaluation & research, philosophy of evaluation, physiology of evaluation, Evaluation in applied psychology. In the above context, how the basic logic of evaluation is set with evaluation fields and the phases of evaluation process