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Learning path recommendation based on modified variable length genetic algorithm

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Abstract

With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing is considered as an important concern for developing more efficient personalized e-learning systems. A more effective personalized e-learning recommender system should recommend a sequence of learning materials called learning path, in an appropriate order with a starting and ending point, rather than a sequence of unordered learning materials. Further the recommended sequence should also match the learner preferences for enhancing their learning capabilities. Moreover, the length of recommended sequence cannot be fixed for each learner because these learners differ from one another in their preferences such as knowledge levels, learning styles, emotions, etc. In this paper, we present an effective learning path recommendation system (LPRS) for e-learners through a variable length genetic algorithm (VLGA) by considering learners’ learning styles and knowledge levels. Experimental results are presented to demonstrate the effectiveness of the proposed LPRS in e-learning environment.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India

    Pragya Dwivedi

  2. Computer Science and Engineering Department, The LNM Institute of Information Technology, Jaipur, India

    Vibhor Kant

  3. School of Computer and Systems Sciences, Jawaharlal Nehru University New Delhi, New Delhi, India

    Kamal K. Bharadwaj

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  1. Pragya Dwivedi
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  2. Vibhor Kant
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  3. Kamal K. Bharadwaj
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Correspondence to Pragya Dwivedi.

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Dwivedi, P., Kant, V. & Bharadwaj, K.K. Learning path recommendation based on modified variable length genetic algorithm. Educ Inf Technol 23, 819–836 (2018). https://doi.org/10.1007/s10639-017-9637-7

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