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Intelligent e-learning system based on fuzzy logic

  • Soft Computing Techniques: Applications and Challenges
  • Published:
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Abstract

In this technological world demanding latest updations in the domain knowledge, it is no surprise that e-learning has become a more viable option to a range of people from beginners to get knowledged and the experts to get updated in a particular domain. Nevertheless, the evolution of e-learning systems is yet to provide full adaptability to the e-learners due to several weaknesses in the systems. Normally, e-learners have varying degrees of progress in their respective learning methodology. Over a period of time, this affects the e-learners performance while providing the same course to all e-learner. Hence, there is a need to create the adaptive e-learning environment to offer the appropriate e-learning contents to all the e-learners. This proposed work puts forth a fuzzy-based novel, intelligent and adaptive e-learning context for a programming language and offers appropriate domain contents to the e-learners which shall update the e-learners better than the previous works. The dependency relation among the concepts in the programming language is provided using fuzzy cognitive map which in turn paves the way for the development of the existing e-learning system. The fuzzy sets and the fuzzy rules represent the e-learners knowledge level and help in providing appropriate recommendations for the previous and subsequent related concepts in a fuzzy cognitive map. Evaluations of the proposed intelligent e-learning system provide promising results in the precise categorization of e-learners and to find their true knowledge.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University College of Engineering Tindivanam, Melpakkam, Tamilnadu, 604001, India

    R. Karthika, L. Jegatha Deborah & P. Vijayakumar

Authors
  1. R. Karthika
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  2. L. Jegatha Deborah
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  3. P. Vijayakumar
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Correspondence to R. Karthika.

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Appendix

Appendix

Rule1:

If α (βi) = Unknown and α (βj) = Learned, then α (βj) = Unknown

µUn (βj) = µUn (βi) * µγ (βi, βj)

Rule2:

If α (βi) = Unknown and α (βj) = Known, then α (βj) = Unknown

µUn (βj) = µUn (βi) * µγ (βi, βj)

Rule3:

If α (βi) = Unknown and α (βj) = Insufficiently known, then α (βj) = Unknown

µUn (βj) = µUn (βi) * µγ (βi, βj)

Rule4:

If α (βi) = Insufficiently known and α (βj) = learned, then α (βj) = Insufficiently known

µInk (βj) = µInk (βi) * µγ (βi, βj)

Rule5:

If α (βi) = Insufficiently known and α (βj) = Known, then α (βj) = Insufficiently known

µInk (βj) = µInk (βi) * µγ (βi, βj)

Rule6:

If α (βi) = Insufficiently known and α (βj) = Unknown, then α (βj) = Insufficiently known

µInk (βj) = µInk (βi) * µγ (βi, βj)

Rule7:

If α (βi) = Known and α (βj) = Learned, then α (βj) = Known

µK (βj) = µK (βi) * µγ (βi, βj)

Rule8:

If α (βi) = Known and α (βj) = Insufficiently known, then α (βj) = Known

µK (βj) = µK (βi) * µγ (βi, βj)

Rule9:

If α (βi) = Known and α (βj) = Unknown, then α (βj) = Known

µK (βj) = µK (βi) * µγ (βi, βj)

Rule10:

If α (βi) = Learned and α (βj) = Known, then α (βj) = Learned

µL (βj) = µL (βi) * µγ (βi, βj)

Rule11:

If α (βi) = Learned and α (βj) = Insufficiently known, then α (βj) = Learned

µL (βj) = µL (βi) * µγ (βi, βj)

Rule12:

If α (βi) = Learned and α (βj) = Unknown, then α (βj) = Learned

µL (βj) = µL (βi) * µγ (βi, βj)

Rule13:

If α (βi) = Assimilated and α (βj) = Known, then α (βj) = Assimilated

µA (βj) = µA (βi) * µγ (βi, βj)

Rule14:

If α (βi) = Assimilated and α (βj) = Unknown, then α (βj) = Assimilated

µA (βj) = µA (βi) * µγ (βi, βj)

Rule15:

If α (βi) = Assimilated and α (βj) = Learned, then α (βj) = Assimilated

µA (βj) = µA (βi) * µγ (βi, βj)

Rule16:

If α (βi) = Assimilated and α (βj) = Insufficiently known, then α (βj) = Assimilated

µA (βj) = µA (βi) * µγ (βi, βj)

Rule17:

If α (βi) = learned and α (βj) = unknown, then α (βi) = Unknown

µUn = µUn (βi) * µγ (βj, βi)

Rule18:

If α (βi) = Assimilated and α (βj) = Unknown, then α (βi) = Unknown

µUn = µUn (βi) * µγ (βj, βi)

Rule19:

If α (βi) = Learned and α (βj) = Insufficiently known, then α (βi) = Insufficiently known

µInk (Ci) = µInk (βi) * µγ (βj, βi)

Rule20:

If α (βi) = Assimilated and α (βj) = Insufficiently known, then α (βi) = Insufficiently known

µInk (Ci) = µInk (βi) * µγ (βj, βi)

Rule21:

If α (βi) = Learned and α (βj) = Known, then α (βi) = Known

µK (Ci) = µK (βi) * µγ (βj, βi)

Rule22:

If α (βi) = Assimilated and α (βj) = Known, then α (βi) = Known

µK (βi) = µK (βi) * µγ (βj, βi)

Rule23:

If α (βi) = Learned and α (βj) = Learned, then α (βi) = Learned

µL (βi) = µL (βi) * µγ (βj, βi)

Rule24:

If α (βi) = Assimilated and α (βj) = Learned, then α (βi) = Learned

µL (βi) = µL (βi) * µγ (βj, βi)

Rule25:

If α (βi) = Learned and α (βj) = Assimilated, then α (βi) = Assimilated

µA (βi) = µA (βi) * µγ (βj, βi)

Rule26:

If α (βi) = Assimilated and α (βj) = Assimilated, then α (βi) = Assimilated

µA (βi) = µA (βi) * µγ (βj, βi)

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Karthika, R., Jegatha Deborah, L. & Vijayakumar, P. Intelligent e-learning system based on fuzzy logic. Neural Comput & Applic 32, 7661–7670 (2020). https://doi.org/10.1007/s00521-019-04087-y

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  • DOI: https://doi.org/10.1007/s00521-019-04087-y

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