An improved constrained learning path adaptation problem based on genetic algorithm

References

• Aguilar, J., Jerez, M., & Rodríguez, T. (2018). CAMeonto: Context awareness meta ontology modeling. Applied Computing and Informatics, 14(2), 202–213. https://doi.org/10.1016/j.aci.2017.08.001
   Google Scholar
• Al-Muhaideb, S., & Menai, M. E. B. (2011). Evolutionary computation approaches to the curriculum sequencing problem. Natural Computing, 10(2), 891–920. https://doi.org/10.1007/s11047-010-9246-5
   Web of Science ®Google Scholar
• Benabdellah, N C, Gharbi, M, & Bellafkih, M. (2017). Europe and MENA Cooperation Advances in Information and Communication Technologies (pp. 149–158). Springer.
   Google Scholar
• Benabdellah, N. C., Gharbi, M., & Bellafkih, M. (2015). Toward E-Content adaptation: Units’ sequence and adapted Ant Colony algorithm. Information, 6(3), 564–575. https://doi.org/10.3390/info6030564
   Google Scholar
• Benmesbah, O., Lamia, M., Hafidi, M., & Zouaghi, I. (2019, September). Towards a reference context model for adaptive learning. In 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (pp. 1–7). IEEE.
   Google Scholar
• Bouihi, B., & Bahaj, M. (2019). Ontology and rule-based recommender system for E-learning applications. International Journal of Emerging Technologies in Learning (iJET), 14(15), 4–13. https://doi.org/10.3991/ijet.v14i15.10566
   Web of Science ®Google Scholar
• Chambers, L. D. (2019). Practical handbook of genetic algorithms: Complex coding systems (Vol. 3). CRC press.
   Google Scholar
• Chang, T. Y., & Ke, Y. R. (2013). A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system. Journal of Network and Computer Applications, 36(1), 533–542. https://doi.org/10.1016/j.jnca.2012.04.002
   Web of Science ®Google Scholar
• Christudas, B. C. L., Kirubakaran, E., & Thangaiah, P. R. J. (2018). An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials. Telematics and Informatics, 35(3), 520–533. https://doi.org/10.1016/j.tele.2017.02.004
   Web of Science ®Google Scholar
• Chu, C. P., Chang, Y. C., & Tsai, C. C. (2011). PC 2 PSO: Personalized e-course composition based on Particle Swarm optimization. Applied Intelligence, 34(1), 141–154. https://doi.org/10.1007/s10489-009-0186-7
   Web of Science ®Google Scholar
• Cun-Ling, B. I. A. N., De-Liang, W. A. N. G., Shi-Yu, L. I. U., Wei-Gang, L. U., & Jun-Yu, D. O. N. G. (2019). Adaptive learning path recommendation based on graph theory and an improved immune algorithm. KSII Transactions on Internet & Information Systems, 13(5), 2277–2298.
   Web of Science ®Google Scholar
• de Marcos, L., Martínez, J. J., & Gutiérrez, J. A. (2008, July). Swarm intelligence in e-learning: a learning object sequencing agent based on competencies. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (pp. 17–24).
   Google Scholar
• Dey, A. K., Abowd, G. D., & Salber, D. (2001). A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human–Computer Interaction, 16(2–4), 97–166. https://doi.org/10.1207/S15327051HCI16234_02
   Web of Science ®Google Scholar
• Dharshini, A. P., Chandrakumarmangalam, S., & Arthi, G. (2015). Ant colony optimization for competency based learning objects sequencing in e-learning. Applied Mathematics and Computation, 263, 332–341. https://doi.org/10.1016/j.amc.2015.04.067
   Web of Science ®Google Scholar
• Duan, X. (2019). Automatic generation and evolution of personalized curriculum based on genetic algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(12), 15–28. https://doi.org/10.3991/ijet.v14i12.10812
   Web of Science ®Google Scholar
• Economides, A. A. (2009). Adaptive context-aware pervasive and ubiquitous learning. International Journal of Technology Enhanced Learning, 1(3), 169–192. https://doi.org/10.1504/IJTEL.2009.024865
   Google Scholar
• Islam, A. N. (2016). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 33(1), 48–55. https://doi.org/10.1016/j.tele.2015.06.010
   Web of Science ®Google Scholar
• Lin, Y. S., Chang, Y. C., & Chu, C. P. (2016). An innovative approach to scheme learning map considering tradeoff multiple objectives. Journal of Educational Technology & Society, 19(1), 142–157.
   Web of Science ®Google Scholar
• Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., & Shen, J. (2016, May). Learning path adaptation in online learning systems. In 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 421–426). IEEE.
   Google Scholar
• Nabizadeh, A. H., Mário Jorge, A., & Paulo Leal, J. (2017, July). Rutico: Recommending successful learning paths under time constraints. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 153–158).
   Google Scholar
• Premlatha, K. R., & Geetha, T. V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: A survey. Artificial Intelligence Review, 44(4), 443–465. https://doi.org/10.1007/s10462-015-9432-z
   Web of Science ®Google Scholar
• Shmelev, V., Karpova, M., & Dukhanov, A. (2015). An approach of learning path sequencing based on revised bloom's taxonomy and domain ontologies with the use of genetic algorithms. Procedia Computer Science, 66, 711–719. https://doi.org/10.1016/j.procs.2015.11.081
   Google Scholar
• Tan, X. H., Shen, R. M., & Wang, Y. (2012). Personalized course generation and evolution based on genetic algorithms. Journal of Zhejiang University Science C, 13(12), 909–917. https://doi.org/10.1631/jzus.C1200174
   Web of Science ®Google Scholar
• Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 318–335. https://doi.org/10.1109/TLT.2012.11
   Web of Science ®Google Scholar
• Wan, S., & Niu, Z. (2014, October). Adaptive learning objects assembly with compound constraints. In 2014 IEEE Computers, Communications and IT Applications Conference. (pp. 34–39).
   Google Scholar
• Wan, S., & Niu, Z. (2016). A learner oriented learning recommendation approach based on mixed concept mapping and immune algorithm. Knowledge-Based Systems, 103, 28–40. doi:10.1016/j.knosys.2016.03.022
   Web of Science ®Google Scholar