Formalizing Logic Based Rules for Skills Classification and Recommendation of Learning Materials

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Kennedy E Ehimwenma 1,* Paul Crowther 1 Martin Beer 1

1. Sheffield Hallam University, Department of Computing, Sheffield, S1 1WB, United Kingdom

* Corresponding author.


Received: 14 Sep. 2017 / Revised: 17 Feb. 2018 / Accepted: 7 Aug. 2018 / Published: 8 Sep. 2018

Index Terms

First order logic, skills classification rules, recommender systems, multi-agent systems, pre-learning assessment decisions, formative, education


First-order logic based data structure have knowledge representations in Prolog-like syntax. In an agent based system where beliefs or knowledge are in FOL ground fact notation, such representation can form the basis of agent beliefs and inter-agent communication. This paper presents a formal model of classification rules in first-order logic syntax. In the paper, we show how the conjunction of boolean [Passed, Failed] decision predicates are modelled as Passed(N) or Failed(N) formulas as well as their implementation as knowledge in agent oriented programming for the classification of students’ skills and recommendation of learning materials. The paper emphasizes logic based contextual reasoning for accurate diagnosis of students’ skills after a number of prior skills assessment. The essence is to ensure that students attain requisite skill competences before progressing to a higher level of learning.

Cite This Paper

Kennedy E Ehimwenma, Paul Crowther, Martin Beer, "Formalizing Logic Based Rules for Skills Classification and Recommendation of Learning Materials", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.9, pp.1-12, 2018. DOI:10.5815/ijitcs.2018.09.01


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