The educational process at universities affected many of the physical ,human and
administrative factors ,and to determine the extent of the impact of each of these factors in
the educational process to reflect the efficiency, It is considered a ver
y difficult process .In
order to achieve output of higher education , we have to stand at the level of the
educational process efficiency and performance in universities, because it is affected by
many of the physical , human and administrative factors . which invited us to devise a
quantitative method for evaluating and measuring the educational process efficiency and
performance in school undergraduate courses. By arranging different success rates, as well
as through special test success rates in the decision, and the results that were obtained:
1-Devise mathematical relationship to measure the educational process efficiency
and performance through the installation of the proportion of applicants and the proportion
of the general success and the success rate for in decision.
2-Devise mathematical relationship to measure the educational process efficiency
and performance in courses through the test of the success rate for in decision.
3-The crisis experienced by the country has reversed dramatically and negatively
educational process efficiency and performance in courses in different years, and according
to the scientific departments.
We offered, in a previous paper, an ontology-based approach to recognize
constraints in free-form service requests and provide services for users. Our
system handles a service request by finding, from among many ontologies, the
domain ontology tha
t best matches the request and then uses the matched
ontology to generate the service request constraints. Although our system is
powerful in recognizing constraints and therefore servicing requests, the
recognition process is a bottleneck due to the number of the tested ontologies
and the amount of computations involved. This paper provides a novel
approach to speed up the recognition process by (1) using ontology indexing
and (2) excluding inapplicable regular expressions early in the process and thus
reducing the number of applied regular expressions. Our experiments show
that our techniques are effective in significantly reducing the amount of
computations and therefore speeding up the recognition process.
