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Teaching assistance and automatic difficulty estimation in converting first order logic to clause form

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Abstract

In this paper, two tools for helping tutors in teaching the conversion of first order logic (FOL) formulas into Clause Form (CF), in the context of an interactive web-based system, are presented. The first is a tutoring managing tool that assists the tutor in managing the teaching material and helps him/her in monitoring the students’ learning progress. The second tool is an expert system that aims at helping the tutor in determining the difficulty level of a formula’s conversion process. To this end, it combines two different approaches, one based on formula’s structure and the other on the conversion process steps. Experimental results show that the difficulty estimation systems perform very successfully.

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References

  • Barwise J, Etchemendy J (2002) Language, proof and logic. Center for the Study of Language and Information

  • Boda K, Ma J, Sinnadurai G, Summers A (2007) Pandora: a reasoning toolbox using natural deduction style. Log J IGPL 15(4):293–304

    Article  Google Scholar 

  • Brachman RJ, Levesque HJ (2004) Knowledge representation and reasoning. Elsevier, Amsterdam

    Google Scholar 

  • Cheng I, Vicent L, Basu A, Goebel R (2010) Multimedia in education: adaptive learning and testing. World Scientific, Singapore

    Book  Google Scholar 

  • Cheng I, Shen R, Basu A (2008) An algorithm for automatic difficulty level estimation of multimedia mathematical test items. In: Proceedings of the 8th IEEE international conference on advanced learning technologies. IEEE Computer Society, Los Alamitos, CA, pp 175–179

  • Coq Development Team (2010) The Coq proof assistant user’s guide. Version 8.3

  • Friedman-Hill E (2003) Jess in action: rule-based systems in java. Manning Publications, Greenwich

    Google Scholar 

  • Grivokostopoulou F, Perikos I, Hatzilygeroudis I (2012a) Assistant tools for teaching FOL to CF conversion. In: Proceedings of 8th artificial intelligence applications and innovations (AIAI), vol 381, pp 306–315

  • Grivokostopoulou F, Perikos I, Hatzilygeroudis I (2012b) A web-based interactive system for learning FOL to CF conversion. In: Proceedings of the IADIS international conference e-learning 2012, pp 287–294

  • Hatzilygeroudis I, Giannoulis C, Koutsojannis C (2004) A web based education system for predicate logic. In: Proceedings of the IEEE international conference on advanced learning technologies (ICALT ’04), pp 106–110

  • Hatzilygeroudis I (2007) Teaching NL to FOL and FOL to CF conversions. In: Proceedings of the 20th international FLAIRS conference, key west, FL. AAAI Press, Menlo Park, pp 309–314

  • Hendriks M, Kaliszyk C, Van Raamsdonk F, Wiedijk F (2010) Teaching logic using a state-of-the-art proof assistant. Acta Didact Napocensia 3(2):35–48

    Google Scholar 

  • Koutsojannis C, Beligiannis G, Hatzilygeroudis I, Papavlasopoulos C, Prentzas J (2007) Using a hybrid Al approach for exercise difficulty level adaptation. Int J Continuing Eng Educ Life Long Learn 17(4–5): 256–272

    Google Scholar 

  • Lukins S, Levicki A, Burg J (2002) A tutorial program for propositional logic with human/computer interactive learning. In: SIGCSE 2002. ACM, New York, pp 381–385

  • Perikos I, Grivokostopoulou F, Hatzilygeroudis I, Kovas K (2011) Difficulty estimator for translating natural language into first order logic. In: Proceedings of the third international conference on intelligent decision techologies (KES-IDT 2011), vol 10, Part I, pp 135–144

  • Regueras LM, Verdú E, Muñoz MF, Pérez MA, de Castro JP, Verdú MJ (2009) Effects of competitive e-learning tools on higher education students: acase study. IEEE Trans Educ 52(2):279–285

    Article  Google Scholar 

  • Sieg W (2007) The AProS project: strategic thinking & computational logic. Log J IGPL 15(4):359–368

    Article  MATH  Google Scholar 

  • Spence R (2001) Information visualization. Addison-Wesley, Boston

    Google Scholar 

  • Verdú E, Verdú MJ, Regueras LM, De Castro JP, García R (2012) A genetic fuzzy expert system for automatic question classification in a competitive learning environment. Exp Syst Appl 39(8):7471–7478

    Article  Google Scholar 

  • Yacef K (2005) The logic-ITA in the classroom: a medium scale experiment. Int J Artif Intell Educ 15(1):41–62

    Google Scholar 

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Acknowledgments

This work was supported by the Research Committee of the University of Patras, Greece, Program “Karatheodoris”, project No C901

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Correspondence to Ioannis Hatzilygeroudis.

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Grivokostopoulou, F., Hatzilygeroudis, I. & Perikos, I. Teaching assistance and automatic difficulty estimation in converting first order logic to clause form. Artif Intell Rev 42, 347–367 (2014). https://doi.org/10.1007/s10462-013-9417-8

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