Fuzzy Logic Modelling Of A Performance
Evaluation System For Academic Programmes
In Nigeria Higher Education
Author(s)
E. M. Igbape & E. A. Onibere
Abstract
Over the past few decades, computer applications have extended beyond the
traditional areas to non-traditional ones. Notably, real life applications often
present vague, ambiguous and imprecise data. Previous investigations have
shown that most current efforts to deploy ICT tools for schools management in
Nigeria have resulted in multiplicity of ad-hoc incompatible information systems
consisting of fragmented mixtures of single function systems that neither share
data nor communicate effectively. Also, data acquired from numerous academic
processes have varying degrees of imprecision, incompleteness, ambiguity,
vagueness and subjectivity. Analysing the data to generate various reports for
effective school administration and quality assurance presents challenges not
easy to resolve with conventional information processing systems built on
normal relational databases. Based on the premise that enough literature exists
on systems integration standards and best practices, this study proposes a model
for building a decision support system for monitoring and evaluating the
performance of academic programmes in Nigerian Schools using fuzzy logic.
This is achieved by using the approach proposed by Daniel Fasel to introduce
fuzzy concepts into the derived multi-dimensional data schema and adding a
meta table structure for classification, without affecting the core of the crisp data.
This will eliminate the possibility of parallel data structures for different subsystems
and allow the use of both sharp and fuzzy queries simultaneously.
Specific implementation issues are examined with a case study of the University
Programmes Accreditation Status Monitoring System in Nigeria.
Keywords: fuzzy relational databases, data integration, integrated fuzzy data
repository, membership function, fuzzy classification, quality assurance.
Keywords
Keywords: fuzzy relational databases, data integration, integrated fuzzy data
repository, membership function, fuzzy classification, quality assurance.
Related Book
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