Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12540/448
Title: Formalizing logic based rules for skills classification and recommendation of learning materials
Authors: Ehimwenma, Kennedy E. 
Crowther, Paul 
Beer, Martin 
Issue Date: 2018
Publisher: Modern Education and Computer Science Press
Source: Ehimwenma, K. E., Crowther, P., & Beer, M. (2018). Formalizing logic based rules for skills classification and recommendation of learning materials. International Journal of Information Technology and Computer Science, 10(9), 1-12.
Journal: International Journal of Information Technology and Computer Science 
Abstract: 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.
URI: https://hdl.handle.net/20.500.12540/448
DOI: 10.1007/s42979-020-00338-1
Appears in Collections:Scholarly Publications

Files in This Item:
File Description SizeFormat 
wku_schlrs_publcn_000151.pdf814.99 kBAdobe PDFThumbnail
View/Open
Show full item record

Page view(s)

711
checked on Sep 19, 2021

Download(s)

115
checked on Sep 19, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons