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2021 - Volume 4 - Number 2

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Dan Oyuga Anne * danoyuga@gmail.com * ORCID: 0000-0003-2103-2928
Kenyatta University, School of Engineering, Nairobi, KENYA

Elizaphan Maina * maina.elizaphan@ku.ac.ke
Kenyatta University, School of Engineering, Nairobi, KENYA

Open Journal for Information Technology, 2021, 4(2), 55-76 * https://doi.org/10.32591/coas.ojit.0402.03055o
Received: 20 August 2021 ▪ Accepted: 9 November 2021 ▪ Published Online: 8 December 2021

LICENCE: Creative Commons Attribution 4.0 International License.

ARTICLE (Full Text - PDF)

ABSTRACT:
We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics.  We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

KEY WORDS: reinforcement learning, adaptive learning, learner characteristics.

CORRESPONDING AUTHOR:
Dan Oyuga Anne, Kenyatta University, School of Engineering, Nairobi, KENYA. E-mail: danoyuga@gmail.com.

REFERENCES:

Aajli, A., & Afdel, K. (2017). Generation of an adaptive e-learning domain model based on a fuzzy logic approach. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 1-8. https://doi.org/10.1109/AICCSA.2016.7945708

Almohammadi, K., & Hagras, H. (2013a). An adaptive fuzzy logic based system for improved knowledge delivery within intelligent E-learning platforms. IEEE International Conference on Fuzzy Systems, 1-8. https://doi.org/10.1109/FUZZ-IEEE.2013.6622350

Almohammadi, K., & Hagras, H. (2013b). An Interval Type-2 Fuzzy Logic Based System for Customised Knowledge Delivery within Pervasive E- Learning Platforms. 2878-2885. https://doi.org/10.1109/SMC.2013.490

Alshammari, M., Anane, R., & Hendley, R. J. (2014). Adaptivity in e-learning systems. Proceedings - 2014 8th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2014, 79-86. https://doi.org/10.1109/CISIS.2014.12

Alshammari, M., Anane, R., & Hendley, R. J. (2015). Students’ satisfaction in learning style-based adaptation. Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015, 2018(July), 55-57. https://doi.org/10.1109/ICALT.2015.56

Balasubramanian, V., Anouneia, S. M., & Abraham, G. (2013). Reinforcement learning approach for adaptive e-learning systems using learning styles. Information Technology Journal, 12,2306-2314.

Chen, C. M., & Li, Y. L. (2010). Personalised context-aware ubiquitous learning system for supporting effective english vocabulary learning. Interactive Learning Environments, 18(4), 341-364. https://doi.org/10.1080/10494820802602329

Chen, S. Y., Huang, P. R., Shih, Y. C., & Chang, L. P. (2016). Investigation of multiple human factors in personalized learning. Interactive Learning Environments, 24(1), 119-141. https://doi.org/10.1080/10494820.2013.825809

Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715-4729. https://doi.org/10.1016/j.eswa.2013.02.007

Colchester, K., Hagras, H., & Alghazzawi, D. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47-64. https://doi.org/10.1515/jaiscr-2017-0004

Dalgarno, B. (2001). Interpretations of constructivism and consequences for Computer Assisted Learning. British Journal of Educational Technology, 32(2), 183-194. https://doi.org/10.1111/1467-8535.00189

Deeb, B., Hassan, Z., & Beseiso, M. (2014). An adaptive HMM based approach for improving e-Learning methods. 2014 World Congress on Computer Applications and Information Systems, WCCAIS 2014. https://doi.org/10.1109/WCCAIS.2014.6916638

Ding, W., Zhu, Z., & Guo, Q. (2018). A new learner model in adaptive learning system. 2018 3rd International Conference on Computer and Communication Systems (ICCCS), 1, 440-443.

El Aissaoui, O., El Madani El Alami, Y., Oughdir, L., & El Allioui, Y. (2018). Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach. 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), 1-6. https://doi.org/10.1109/ISACV.2018.8354021

Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Icicis, 342-347. https://doi.org/10.1109/INTELCIS.2017.8260060

Fenza, G., Orciuoli, F., & Sampson, D. G. (2017). Building adaptive tutoring model using artificial neural networks and reinforcement learning. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), 460-462. https://doi.org/10.1109/ICALT.2017.124

Firte, A. A., Bratu, C. V., & Cenan, C. (2009). Intelligent component for adaptive e-learning systems. Proceedings – 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, ICCP 2009, 35-38. https://doi.org/10.1109/ICCP.2009.5284788

Guan, M., Jia, J., Yang, Y., & Chen, Q. (2013). Research on adaptive e-Learning system using technology of learning navigation. Proceedings of the 8th International Conference on Computer Science and Education, ICCSE 2013, Iccse, 24-29. https://doi.org/10.1109/ICCSE.2013.6553877

Hadullo, K., Oboko, R., & Omwenga, E. (2017). A model for evaluating e-learning systems quality in higher education in developing countries. International Journal of Education and Development Using Information and Communication Technology, 13(2), 185-204. https://doi.org/https://0-search-proquest-com.oasis.unisa.ac.za/docview/1952423514?accountid=14648

Hadullo, K., Oboko, R., & Onwenga, E. (2018). Status of e-learning quality in Kenya: Case of Jomo Kenyatta University of Agriculture and Technology postgraduate students. International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.3322

Hammad, J., Hariadi, M., Purnomo, M. H., & Jabari, N. (2018). E-learning and adaptive e-learning review. IJCSNS International Journal of Computer Science and Network Security, 18(2), 48-55.

Havard, B., East, M. L., Prayaga, L., & Whiteside, A. (2016). Adaptable learning theory framework for technology-enhanced learning. Leadership and Personnel Management, February, 384-406. https://doi.org/10.4018/978-1-4666-9624-2.ch018

Huang, S., Yin, B., & Liu, M. (2017). Research on individualized learner model based on. https://doi.org/10.1109/ISET.2017.45

Hwang, A. G., Sung, H., Hung, C., & Huang, I. (2013). International forum of educational technology & society a learning style perspective to investigate the necessity of developing adaptive learning systems source. Journal of Educational Technology & Society, 16(2)

Jang, B., Kim, M., Harerimana, G., & Kim, J. W. (2019). Q-learning algorithms: A comprehensive classification and applications. IEEE Access, 7, 133653-133667. https://doi.org/10.1109/ACCESS.2019.2941229

Kanimozhi, A. (n.d.). An adaptive e-learning environment centered on learner’ s emotional behaviour.

Khalil, M. K., & Elkhider, I. A. (2020). Applying learning theories and instructional design models for effective instruction. 29605, 147-156. https://doi.org/10.1152/advan.00138.2015

Kolekar, S. V., Sanjeevi, S. G., & Bormane, D. S. (2010). Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. 2010 IEEE International Conference on Computational Intelligence and Computing Research, March 2016, 1-5. https://doi.org/10.1109/ICCIC.2010.5705768

Linecar, P., & Marchbank, P. (2020). INSPIRE XXV e-learning as a solution during unprecedented times in the 21st century. https://www.researchgate.net/publication/348169543_INSPIRE_XXV_e-Learning_as_a_Solution_during_Unprecedented_Times_in_the_21st_CenturyPROCEEDINGS.

Liu, Z., Wang, Z., & Fang, Z. (2006). An agent-based e-learning assessing and instructing system. Proceedings - 2006 10th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2006, 1414-1419. https://doi.org/10.1109/CSCWD.2006.253194

Malpani, A. (2011). Personalized intelligent tutoring system using reinforcement learning. Siam Journal on Control, 561-562.

Mejia, C., Gomez, S., Mancera, L., & Taveneau, S. (2017). Inclusive learner model for adaptive recommendations in virtual education. 0-1. https://doi.org/10.1109/ICALT.2017.101

Premlatha, K. R., & Geetha, T. V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artif Intell.

Rabat, I. (2016). Learning Environment. 1–5.

Raj, N. S., & Renumol, V. G. (2021). A rule-based approach for adaptive content recommendation in a personalized learning environment: An experimental analysis. October 2018. https://doi.org/10.1109/T4E.2019.00033

Rajendran, R., Iyer, S., & Murthy, S. (2018). Personalized affective feedback to address students frustration in ITS. IEEE Transactions on Learning Technologies, XX(c), 1-12. https://doi.org/10.1109/TLT.2018.2807447

Rani, M., Vyas, R., & Vyas, O. P. (2017). OPAESFH: Ontology-based personalized adaptive e-learning system using FPN and HMM. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017-Decem, 2441-2446. https://doi.org/10.1109/TENCON.2017.8228271

Sabourin, J., Mott, B., & J., L. (2011). Modeling learner affect with theoretically grounded dynamic Bayesian networks. Proceedings of the Fourth International Conference on Affective Computing and Intelligent Interaction, 286-295. https://doi.org/10.1007/978-3-642-24600-5_32

Schott, F. (2015). University of Freiburg Department of Education. July 2018, 0-22. https://doi.org/10.1016/B978-0-08-097086-8.92032-4

Sethi, M. A., Lomte, S. S., & Shinde, U. B. (2017). Multimodal approach to identify learning strategies of visual and verbal learners. International Journal of Emerging Technologies in Learning, 12(10), 76-94. https://doi.org/10.3991/ijet.v12i10.6935

Sethi, M. A., & S Lomte, S. (2017). An interactive dynamic elearning framework for visual and verbal learners. International Journal of Computer Science and Engineering, 4(7), 3-13. https://doi.org/10.14445/23488387/ijcse-v4i7p102

Steinbacher, H.-P., & Hoffmann, K. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. Proceedings of the 8th IADIS International Conference Information Systems 2015, IS 2015, 2(August), 227-231. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84944055689&partnerID=40&md5=435fa9ccaccc7717986ba5820ce3a3be.

Sundayana, R., Herman, T., Dahlan, J. A., & Prahmana, R. C. I. (2017). Using ASSURE learning design to develop students’ mathematical communication ability Using ASSURE learning design to develop students’ mathematical communication ability. October.
Sutton, R. S., & Barto, E. G. (2018). Reinforcement learning: An introduction. https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf.

Tadlaoui, M. A., Carvalho, R. N., & Khaldi, M. (2018). A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems. 8(37).

Ueno, M., & Okamoto, T. (2007). Bayesian agent in e-learning. Proceedings – The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007, 1, 282-284. https://doi.org/10.1109/ICALT.2007.82

van Riesen, S. A. N., Gijlers, H., Anjewierden, A., & de Jong, T. (2018). The influence of prior knowledge on experiment design guidance in a science inquiry context. International Journal of Science Education, 40(11), 1327-1344. https://doi.org/10.1080/09500693.2018.1477263

Watkins, C. J. C. H., & Dayan, P. (1992) Q-Learning. Machine Learning, 8, 279-292.
http://dx.doi.org/10.1007/BF00992698

Whitehill, J., & Movellan, J. (2018). Approximately optimal teaching of approximately optimal learners. IEEE Transactions on Learning Technologies, 11(2), 152-164. https://doi.org/10.1109/TLT.2017.2692761

Wu, C. H., Chen, T. C., Yan, Y. H., & Lee, C. F. (2017). Developing an adaptive e-learning system for learning excel. Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017, 1973-1975. https://doi.org/10.1109/ICASI.2017.7988583

Wu, D., Lu, J., & Zhang, G. (2015). A fuzzy tree matching-based personalized e-learning recommender system. IEEE Transactions on Fuzzy Systems, 23(6), 2412-2426. https://doi.org/10.1109/TFUZZ.2015.2426201

Yang, A. T., Hwang, G., Yang, S. J., Yang, T., Hwang, G., & Yang, S. J. (2016). International Forum of Educational Technology & Society Development of an Adaptive Learning System with Multiple Perspectives based on Students ’ Learning Styles and Cognitive Styles. Journal of Educational Technology & Society, 16(4).