International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.10, No.9, Sep. 2018

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

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Kennedy E Ehimwenma, Paul Crowther, Martin Beer

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


[1]T. R. Razak, M. A. Hashim, N. M. Noor., I. H. A. Halim, and N. F. F. Shamsul, “Career path recommendation system for UiTM Perlis students using fuzzy logic,” in IEEE 5th International Conference on Intelligent and Advanced Systems (ICIAS), 2014, pp. 1-5.

[2]D. Bañeres and J. Conesa, “A life-long learning recommender system to promote employability,” International Journal of Emerging Technologies in Learning, vol. 12(6), 2017.

[3]G. J. Nalepa and S. Bobek, “Rule-based solution for context-aware reasoning on mobile devices,” Computer Science and Information Systems, vol. 11(1), pp. 171-193, 2014.

[4]R, P. Bringula, A. D. Aviles, M. Y. C. Batalla and M. T. F. Borebor, “Factors affecting failing the programming skill examination of computing students,” International Journal of Modern Education and Computer science (IJMECS), 5, 1-8, 2017, doi: 10.5815/ijmecs.2017.05.01.

[5]T. Anderson, “The theory and practice of online learning,” Athabasca University Press, 2008.  (accessed: 06.06.2017).

[6]S. B. Kotsiantis, I. Zaharakis and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerging Artificial Intelligence Applications in Computer Engineering, I. Maglogianis et al, Eds. IOS Press, 2007, pp. 3-24.

[7]R. Rifkin and A. Klautau, “In defense of one-vs-all classification,” Journal of machine learning research, 5(Jan), pp. 101-141, 2004.

[8]S. Marsland, “Machine learning: an algorithmic perspective,” vol. 10, no. 1, CRC press, 2014.

[9]K. Adhatrao, A. Gaykar, A. Dhawan, R. Jha and V. Honrao, “Predicting students' performance using ID3 and C4. 5 classification algorithms,” International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.3, No.5, September 2013, pp. 39-52, arXiv preprint arXiv:1310.2071.

[10]M. H. Dunham, “Data Mining: Introductory and Advanced Topics,” Pearson Education Inc., Indian, 2003.

[11]N. Chanamarn & K. Tamee, “Enhancing Efficient Study Plan for Student with Machine Learning Techniques,” International Journal of Modern Education and Computer Science, vol. 9(3), 1, 2017.

[12]C. González, J. C. Burguillo and M. Llamas, “Case-Based student modeling in multi-agent learning environment,” in Multi-Agent Systems and Applications IV, Springer Berlin Heidelberg, 2005, pp. 72-81.

[13]R. L. de Mantaras, “Case-based reasoning,” in Machine Learning and Its Applications, Springer Berlin Heidelberg, 2001, pp. 127-145.

[14]D. Patterson, “Introduction to artificial intelligence and expert systems,” Prentice-Hall, Inc. 1990.

[15]A. Hutchinson, “Algorithmic learning,” Oxford University Press, Inc. 1994.

[16]A. Abelló, M. E. Rodríguez, T. Urpí, X. Burgués, M. J. Casany, C. Martín and C. Quer, “Learn-sql: automatic assessment of sql based on IMS QTI specification,” in Eighth IEEE International Conference on Advanced Learning Technologies ICALT'08, pp. 592-593, 2008.

[17]C. Kenny and C, Pahl, “Automated tutoring for a database skills training environment,” ACM, vol. 37, No. 1, pp. 58-62, 2005.

[18]A. Mitrovic, “Learning sql with a computerized tutor,” in SIGCSE’98, 1998, pp. 307 – 311.

[19]J. C. Prior, “Online assessment of SQL query formulation skills,” in Proceedings of the fifth Australasian conference on Computing education, Australian Computer Society, Inc vol. 20, pp. 247-256, January 2003.

[20]S. Dekeyser, M. de Raadt and T. Y. Lee, “Computer assisted assessment of SQL query skills,” in Proceedings of the eighteenth conference on Australasian database, Australian Computer Society, Inc., Vol. 63, pp. 53-62, March 2007.

[21]Ö. Korkmaz, “The effects of scratch-based game activities on students' attitudes, self-efficacy and academic achievement,” International Journal of Modern Education and Computer Science (IJMECS), vol. 8(1), 16, 2016.

[22]C. Rogerson and E. Scott, “The fear factor: how it affects students learning to program in a tertiary environment,” Journal of Information Technology Education, vol. 9, 2010.

[23]J. C. Prior and R. Lister, “The backwash effect on SQL skills grading,” ACM SIGCSE Bulletin, vol. 36(3), pp. 32-36, 2004.

[24], “SQL tutorial,” (Accessed: 2nd June 2018).

[25]Beginner SQL Tutorial, “Learn sql programming,” (Accessed: 2nd July 2017).

[26], “Interactive online sql training,” (Accessed: 2nd June 2018)

[27]SQLzoo, “Sql zoo,” (Accessed: 22nd May 2018)

[28]R. S. Michalski, J. G. Carbonell and T. M. Mitchell, “A comparative review of selected methods for learning from examples,”in “Machine learning: An artificial intelligence approach”, R. S. Michalski, J. G. Carbonell and T. M. Mitchell, Eds. Springer Science & Business Media, pp. 4-81, 2013.

[29]N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel and R. Koper, “Recommender systems in technology enhanced learning,” Recommender systems handbook, 2011, pp. 387-415.

[30]D. Bañeres, “A personalized summative model based on learner’s effort,” International Journal of Emerging Technologies in Learning (iJET), vol. 12(06), pp. 4-21, 2017.

[31]M. El Mabrouk, S. Gaou and M. K. Rtili, “Towards an intelligent hybrid recommendation system for e-learning platforms using data mining,” International Journal of Emerging Technologies in Learning (iJET), vol. 12(06), pp. 52-76, 2017.

[32]S. Ritter, J. Anderson, M. Cytrynowicz and O. Medvedeva, “Authoring content in the published pat algebra tutor,” Journal of Interactive Media in Education, no. 2, 1988,

[33]P. Dell'Acqua, F. Sadri, F. Toni and F. S. F. Toni,  “Communicating agents,” Citeseer, 1999.

[34]L. De Silva, “Planning in BDI agent systems,” PhD Thesis RMIT University, Australia, 2009.

[35]A. K. Dey, “Providing architectural support for building context-aware applications,” Ph.D. thesis, Atlanta, GA, USA, 2000.

[36]A. Ranganathan and R. H. Campbell, “An infrastructure for context-awareness based on first order logic,” Personal and Ubiquitous Computing, vol. 7(6), pp. 353-364, 2003.

[37]F. Yu, Q. Zhou, X. Lu and S. Zhao, “A first-order logic framework of major choosing decision making with an uncertain reasoning function,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016.

[38]P. T. Baffes, “Learning to model students: using theory refinement to detect misconceptions,” Technical Report, Artificial Intelligence, University of Texas at Austin, 1994.

[39]I. Padayachee, “Intelligent tutoring systems: architecture and characteristics,” in Proceedings of the 32nd Annual SACLA Conference, 2002, pp. 1-8.

[40]A. Ricci, M. Piunti and M. Viroli, “Environment programming in multi-agent systems: an artifact-based perspective,” Autonomous Agents and Multi-Agent Systems, vol. 23(2), pp. 158-192, 2011.

[41]F. Wang, “POMDP framework for building an intelligent tutoring system,” Computer Supported Education (CSEDU2014), SCITEPRESS, pp. 233-240, 2014. 

[42]K. E. Ehimwenma, M. Beer and P. Crowther, “Adaptive multiagent system for learning gap identification through semantic communication and classified rules learning,” 7th International Conference on Computer Supported Education (CSEDU), Doctoral Consortium, SCITEPRESS, pp. 33-38, 2015.

[43]K. E. Ehimwenma, M. Beer, and P. Crowther, "Student modelling and classification rules learning for educational resource prediction in a multiagent system," in IEEE 7th Computer Science and Electronic Engineering Conference (CEEC), 2015, pp. 59-64.

[44]X. Chen, A. Mitrovic and M. Mathews, “Does adaptive provision of learning activities improve learning in sql-tutor?,” in International Conference on Artificial Intelligence in Education, Springer, Cham, June 2017, pp. 476-479.

[45]D. Lavbič, T. Matek and A. Zrnec, “Recommender system for learning SQL using hints,” Interactive Learning Environments, vol. 25(8), pp. 1048-1064, 2017.

[46]C. Casteel, “Effects of chunked reading among learning disabled students: an experimental comparison of computer and traditional chunked passages,” Journal of Educational Technology Systems, vol. 17(2), pp.115-21, 1988.

[47]K. E. Ehimwenma, P. Crowther and M. Beer, “A system of serial computation for classified rules prediction in non-regular ontology trees,” International Journal of Artificial Intelligence and Applications (IJAIA), vol. 7(2), pp.21-33, 2016.

[48]R. H. Bordini, J. F. Hübner and M. Wooldridge, “Programming multi-agent systems in AgentSpeak using Jason, John Wiley & Sons, UK, Vol. 8, 2007.