Journal Article10.1007/S10639-017-9637-7
Learning path recommendation based on modified variable length genetic algorithm
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TL;DR: This paper presents 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 and demonstrates the effectiveness of the proposed LPRS in e-learning environment.
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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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Citations
A systematic review: machine learning based recommendation systems for e-learning
TL;DR: A taxonomy that accounts for components required to develop an effective recommendation system was developed and it was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components.
267
A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning
TL;DR: A learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner’s target learning object.
216
Genetic algorithms: theory, genetic operators, solutions, and applications
Bushra Alhijawi,Arafat Awajan +1 more
TL;DR: This article aims to review and summarize the recent contributions to the GA research field, and surveys the real-life applications and roles of GA.
154
A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020
Nisha S. Raj,V. G. Renumol +1 more
- 11 Aug 2021
TL;DR: In this article, a systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020, which resulted in 52 publications.
Learning path personalization and recommendation methods: A survey of the state-of-the-art
TL;DR: The most significant challenges of the methods that are applied to personalize learning paths need to be tackled in order to enhance the quality of the personalization.
134
References
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