ABSTRACT
In the context of e-learning, learners often struggle to make informed decisions about what and how to learn when they have access to a vast array of learning resources. To address this issue, several approaches have been proposed from different perspectives, aiming to generate personalized learning paths for e-learners. These approaches include learner-based, knowledge-based, and hybrid recommendation methods. Among them, hybrid methods have emerged as a promising solution for personalized learning path recommendations, as they combine the strengths of both learner-based and knowledge-based approaches. However, existing hybrid methods typically employ exhaustive techniques to determine optimal paths by extracting all possible learning paths. This approach can be time-consuming and computationally expensive. To overcome this challenge, we propose a novel two-stage personalized learning path recommendation approach that integrates concept map and an improved genetic algorithm. We conducted computational experiments using diverse simulation datasets to assess the effectiveness of our proposed method. The experimental results indicate that our approach surpasses other competing methods in terms of both performance and robustness.
- Maiti Monica, and Priyaadharshini Manickavasagam. 2022. Recommender System for Low Achievers in Higher Education. International Journal of Information and Education Technology, 12, 12(DEC. 2022),1390-1398. https://doi.org/10.18178/ijiet.2022.12.12.1763Google ScholarCross Ref
- Sarwar Sohail, García-Castro Raúl, Qayyum Zia Ul, Safyan Muhammad, Munir Faisal, and Iqbal Muddesar. 2018. Ontology Based e-Learning Systems: A Step towards Adaptive Content Recommendation. International Journal of Information and Education Technology, 8, 10(OCT. 2018) ,700-705. https://doi.org/10.18178/IJIET.2018.8.10.1125Google ScholarCross Ref
- Kaewkiriya Thongchai, Utakrit Nattavee, and Tiantong Monchai. 2016. The Design of a Rule Base for an e-Learning Recommendation System Base on Multiple Intelligences. International Journal of Information and Education Technology, 6, 3(MAR. 2016),206-210. https://doi.org/10.7763/IJIET.2016.V6.685Google ScholarCross Ref
- Liu Gang, and Hao Tianyong. 2012. User-based Question Recommendation for Question Answering System. International Journal of Information and Education Technology, 2, 3(JUN. 2012), 243-246. https://doi.org/10.7763/ijiet.2012.v2.120Google ScholarCross Ref
- Jafarkarimi1 Hosein, Hiang Sim Alex Tze, and Saadatdoost Robab. 2012. A Naïve Recommendation Model for Large Databases. International Journal of Information and Education Technology, 2, 3(JUN. 2012), 216-219. https://doi.org/10.7763/IJIET.2012.V2.113Google ScholarCross Ref
- Long III* Robert W., and Watanabe Hiroaki. 2023. Teacher Recommendations for Writing Programs in Japanese Universities. International Journal of Information and Education Technology, 13, 4(APR. 2023), 650-657. https://doi.org/10.18178/ijiet.2023.13.4.1849Google ScholarCross Ref
- Marcelo de Oliveira Costa Machado, Natalie Ferraz Silva Bravo, Andre Ferreira Martins, Heder Soares Bernardino, Eduardo Barrere, and Jairo Francisco de Souza. 2021. Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artif Intell Rev, 54, 1(JAN. 2021), 711-754. https://doi.org/10.1007/s10462-020-09864-zGoogle ScholarDigital Library
- Amir Hossein Nabizadeh, Jose Paulo Leal, Hamed N. Rafsanjani, and Rajiv Ratn Shah. 2020. Learning path personalization and recommendation methods: A survey of the state-of-the-art. Expert Systems with Applications, 159(NOV. 2020). https://doi.org/ 10.1016/j.eswa.2020.113596Google ScholarCross Ref
- Eugenijus Kurilovas, Inga Zilinskiene, and Valentina Dagiene. 2015. Recommending suitable learning paths according to learners’ preferences: Experimental research results. Computers in Human Behavior, 51(OCT. 2015), 945-951. https://doi.org/10.1016/j.chb.2014.10.027Google ScholarDigital Library
- Pragya Dwivedi, Vibhor Kant, and Kamal K. Bharadwaj. 2018. Learning path recommendation based on modified variable length genetic algorithm. Education and information technologies, 23, 2(MAR. 2018), 819-836. https://doi.org/10.1007/s10639-017-9637-7Google ScholarDigital Library
- Yuwen Zhou, Changqin Huang, Qintai Hu, Jia Zhu, and Yong Tang. 2018. Personalized learning full-path recommendation model based on LSTM neural networks. Information sciences, 444(MAY. 2018),135-152. https://doi.org/10.1016/j.ins.2018.02.053Google ScholarCross Ref
- Guillaume Durand, Nabil Belacel, and Francois LaPlante. 2013. Graph theory based model for learning path recommendation. Information Sciences, 251(DEC. 2013), 10-21. https://doi.org/ 10.1016/j.ins.2013.04.017Google ScholarCross Ref
- Yanxia Pang, Na Wang, Ying Zhang, Yuanyuan Jin, Wendi Ji, and Wenan Tan. 2019. Prerequisite-related MOOC recommendation on learning path locating. Computational Social Networks, 6(AUG. 2019), 1-16. https://doi.org/10.1186/s40649-019-0065-2Google ScholarCross Ref
- Jiaqi Gao, Qianhui Liu, and Win-Bin Huang. 2021. Learning Path Generator Based on Knowledge Graph. 12th International Conference on E-Education, E-Business, E-Management, and E-Learning, 27-33. https://doi.org/10.1145/3450148.3450155Google ScholarDigital Library
- Haiping Zhu, Feng Tian, Ke Wu, Nazaraf Shah, Yan Chen, Yifu Ni, Xinhui Zhang, Kuo-Ming Chao, and Qinghua Zheng. 2018. A multi-constraint learning path recommendation algorithm based on knowledge map. Knowledge-Based Systems, 143(MAR. 2018), 102-114. https://doi.org/10.1016/j.knosys.2017.12.011Google ScholarCross Ref
- Daqian Shi, Ting Wang, Hao Xing, and Hao Xu. 2020. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowledge-Based Systems, 195(MAY. 2020). https://doi.org/10.1016/j.knosys.2020.105618Google ScholarCross Ref
- Amir Hossein Nabizadeh, Danie Goncalves, Sandra Gama, Joaquim Jorge, and Hamed N. Rafsanjani. 2020. Adaptive learning path recommender approach using auxiliary learning objects. Computers & Education, 147(APR. 2020). https://doi.org/10.1016/j.compedu.2019.103777Google ScholarDigital Library
- Fernando Gaxiola, Patricia Melin, Fevrier Valdez, Juan R. Castro, and Oscar Castillo. 2016. Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO. Applied Soft Computing, 38(JAN. 2016), 860-871. https://doi.org/10.1016/j.asoc.2015.10.027Google ScholarDigital Library
- Hwai-En Tseng, Chien-Cheng Chang, Shih-Chen Lee, and Yu-Ming Huang. 2018. A block-based genetic algorithm for disassembly sequence planning. Expert Systems with Applications, 96(APR. 2018), 492-505. https://doi.org/10.1016/j.eswa.2017.11.004Google ScholarDigital Library
- Pankaj Gupta, Masahiro Inuiguchi, Mukesh Kumar Mehlawat, and Mittal, Garima. 2013. Multiobjective credibilistic portfolio selection model with fuzzy chance-constraints. Information Sciences, 229(APR. 2013), 1-17. https://doi.org/10.1016/j.ins.2012.12.011Google ScholarDigital Library
Index Terms
- A Novel Two-Stage Personalized Learning Path Recommendation Approach for E-learning
Recommendations
Learning path recommendation based on modified variable length genetic algorithm
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 ...
Intelligent web-based learning system with personalized learning path guidance
Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized ...
A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels
Artificial Intelligence in EducationAbstractE-learning has resulted in the proliferation of educational resources, but challenges remain in providing personalized learning materials to learners amidst an abundance of resources. Previous personalized learning path recommendation (LPR) ...
Comments