2016
DOI: 10.3991/ijet.v11i01.5001
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Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Abstract: Abstract!Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10], [11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm in the estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system e… Show more

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Cited by 3 publications
(7 citation statements)
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“…In practical systems this implies: on or off, good or bad, working or notWorking, found or notFound, present or notPresent, etc. The investigation of relational-concept ontology in [16], [17] for the pre-assessment of students from node-to-node in a regular ontology: ontology with equal number of leaf-nodes across all parent nodes (Fig.1) used the boolean parameter T = 2 torepresent either a pass or a fail for measurement of pre-assessment outcomes. The parameters T and leaf-nodes N of a given parent class node then formed the basis of agent classification learning.…”
Section: Discrete Parameters and Classificationmentioning
confidence: 99%
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“…In practical systems this implies: on or off, good or bad, working or notWorking, found or notFound, present or notPresent, etc. The investigation of relational-concept ontology in [16], [17] for the pre-assessment of students from node-to-node in a regular ontology: ontology with equal number of leaf-nodes across all parent nodes (Fig.1) used the boolean parameter T = 2 torepresent either a pass or a fail for measurement of pre-assessment outcomes. The parameters T and leaf-nodes N of a given parent class node then formed the basis of agent classification learning.…”
Section: Discrete Parameters and Classificationmentioning
confidence: 99%
“…To estimate and predict the number of classified induction learning rules R, [16], [17] as stated earlier projected the Initialisation algorithm R = CT N + 1 eq.1…”
Section: The Initialisation Algorithmmentioning
confidence: 99%
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