Adaptive and Intelligent MOOCs: How They Contribute to the Improvement of the MOOCs’ Effectiveness


Abstract

Several traditional MOOCs have been developed utilizing particular traditional approaches for distance learning. The main objective of this article is to examine numerous studies and research about the provision of adaptive and intelligent MOOCs to address issues, such as dropout rate, for improving their efficiency compared to conventional MOOCs. Important issues that have been the essential study interests of MOOC scholars in recent years, including dropout rate, completion rate, loneliness, and other topics, were studied. Finally, the research questions posed on the effectiveness of Adaptive and Intelligent MOOCs, the learner’s characteristics used for adaptation, the adaptive and intelligent methods and techniques used, and the improvements they bring to traditional MOOCs as a compass for designing Adaptive and Intelligent MOOCs in the coming years, are discussed.


Keywords

distance education; adaptive and intelligent MOOCs; personalized learning; MOOC challenges

Aleven, V., Sewall, J., Popescu, O., Ringenberg, M., van Velsen, M., & Demi, S. (2016). Embedding Intelligent Tutoring Systems in MOOCs and e-Learning Platforms. In: A. Micarelli, J. Stamper, K. and Panourgia (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science, 9684, Cham: Springer. https://doi.org/10.1007/978-3-319-39583-8_49

Amarasinghe, I., Hernández-Leo, D., Manathunga, K., & Jonsson, A. (2018). Sustaining Continuous Collaborative Learning Flows in MOOCs: Orchestration Agent Approach, Journal of Universal Computer Science, 24(8), 1034–1051. https://doi.org/10.3217/jucs-024-08-1034.

Anders, A. (2015). Theories and applications of massive online open courses (MOOCs): The case for hybrid design. International Review of Research in Open and Distance Learning, 16(6). https://doi.org/10.19173/irrodl.v 16i6. 2185

Ardchir, S., Talhaoui M.A., & Azzouazi, M. (2017). Towards an Adaptive Learning Framework for MOOCs, Proceedings of the International Conference on E-Technologies, (pp. 236–251). https://doi.org/10.1007/978-3-319-59041-7_15

Assaf, N., Ramírez-Hernández, D., & Glasserman, L. (2018). Effective Completion Rate Design for MOOCs of the Binational Laboratory for Intelligent Energy Sustainability Management and Technological Training, Proceedings of the International Academy of Technology, Education and Development (EDULEARN18) (pp. 234–244). https://doi.org/10.21125/edulearn.2018.0125.

Birari, N. (2014). Intelligent Tutoring System using Computerized Adaptive testing and interaction logs for MOOCs (Doctoral dissertation), Indian Institute of Technology, Bombay.

Blanco, A.F., García-Peñalvo F.J., & Sein-Echaluce, M. (2013). A methodology proposal for developing adaptive cMOOC, Proceedings of the 2013 ACM International Conference on Technological Ecosystem for Enhancing Multiculturality, TEEM (pp. 553–558). https://doi.org/10.1145/2536536.2536621.

Bonomo, V. (2017). Brain-Based Learning Theory. Journal of Education and Human Development, 6(1), 27–43. https://doi.org/10.15640/jehd.v6n1a3.

Boulton, C.A., Hughes, E., Kent C., Smith, J.R., Williams, H.T.P. (2019). Student engagement and wellbeing over time at a higher education institution. PLoS ONE 14(11), e0225770. https://doi.org/10.1371/journal.pone.0225770.

Braidic, S. (2011). Brain Based Learning Environments in an Online Setting. In Tomei, L. (Ed.), Online Courses and ICT in Education: Emerging Practices and Applications. (pp. 90–98). https://doi.org/10.4018/978-1-60960-150-8.ch007.

Brusilovsky, P., & Miller, P., (2001). Course Delivery Systems for the Virtual University. In T. Tschang, & T. Della Senta (Eds.) Access to Knowledge: New Information Technologies and the Emergence of the Virtual University, Amsterdam: Elsevier Science, (pp. 167–206).

Brusilovsky, P., & Peylo, C. (2003). Adaptive and Intelligent Web-based Educational Systems, International Journal of Artificial Intelligence in Education, 13(2), 156–169.

Caballé, S., & Conesa, J. (2018). Conversational Agents in Support for Collaborative Learning in MOOCs: An Analytical Review, International Conference on Intelligent Networking and Collaborative Systems (pp. 384–394), Bratislava, Slovakia, Cham: Springer. https://doi.org/10.1007/978-3-319-98557-2_35.

Carneiro, R. (2013). Living by learning, learning by living: the quest for meaning. International Review of Education, 59, 353–372. https://doi.org/10.1007/s11159-013-9355-3.

Chiappe, A., & Castillo, B. (2020). Retention in MOOCS: some key factors, Essay Evaluation and Public Policies in Education, 28, 1–23. https://doi.org/10.1590/S0104-40362020002802667

Daniel, J., Cano, E.V., & Cervera, M.G. (2015). The Future of MOOCs: Adaptive Learning or Business Model? International Journal Educational Technology Higher Education, 12, 64–73. https://doi.org/10.7238/rusc.v12i1.2475

Dillenbourg, P., Fox, A., Kirchner, C., Mitchell J., & Wirsing M. (Eds), (2014). Massive open online courses: current state and perspectives, Dagstuhl Manifestos, 4(1), 1–27. https://doi.org/10.4230/DagMan.4.1.1.

El Emrani, S.E., Merzouqi, A.E., & Khaldi M. (2021). An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement. International Journal of Emerging Technologies in Learning (iJET), 16(13), 82–93. https://doi.org/10.3991/ijet.v16i13.22261.

El Emrani, S.Ε., Palomo-Duarte, M., Mota, J.M., & Dodero, J.M. (2022). E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research? EAI Endorsed Transactions on Scalable Information Systems. 9(6), e10, https://doi.org/10.4108/eetsis.v9i6.2713.

Ewais, Α., & Samara, D.A. (2020). Adaptive MOOCs Based on Intended Learning Outcomes Using Naïve Bayesian Technique, International Journal of Emerging Technologies in Learning (iJET), 15(4), 4–21. https://doi.org/10.3991/ijet.v15i04.11420.

Ferschke, O., Yang, D., Tomar G., & Rosé, C.P. (2015). Positive Impact of Collaborative Chat Participation in an edX MOOC, Proceedings of the International Conference on Artificial Intelligence in Education (pp. 115–124). Madrid, Spain, Cham: Springer. https://doi.org/10.1007/978-3-319-19773-9_12.

Goldberg, B., Hoffman, M. & Tarr, R. (2015). Authoring Instructional Management Logic in GIFT Using the Engine for Management of Adaptive Pedagogy (EMAP). In R. Sottilare, A. Graesser, X. Hu & K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Volume 3). U.S. Army Research Laboratory.

González-Castro, N. Muñoz-Merino, P.J. Alario-Hoyos, C. & Delgado-Kloos, C. (2021). Adaptive learning module for a conversational agent to support MOOC learners. Australasian Journal of Educational Technology, 37(2), 24–44. https://doi.org/10.14742/ajet.6646.

Gynther, K. (2016). Design Framework for an Adaptive MOOC Enhanced by Blended Learning: Supplementary Training and Personalized Learning for Teacher Professional Development, The Electronic Journal of eLearning, 14(1), 15–30, ISSN 1479-4403.

Hamal, O., & El Faddouli, N. (2022). Intelligent System Using Deep Learning for Answering Learner Questions in a MOOC, International Journal of Emerging Technologies in Learning (iJET), 17(2), 32–42. https://doi.org/10.3991/ijet.v17i02.26605.

Hasmaini, H., Salam, S., Nurul, S., & Syafiatun, N. (2018). The Designing of Adaptive Self-Assessment Activities in Second Language Learning using Massive Open Online Courses (MOOCs), International Journal of Advanced Computer Science and Applications (IJACSA), 9(9), 276–282. https://doi.org/10.14569/IJACSA.2018.090937.

Hew, K. F., & Cheung, W. S. (2014). Students’ and Instructors’ Use of Massive Open Online Courses (MOOCs): Motivations and Challenges, Educational Research Review, 12, 45–58. http://dx.doi.org/10.1016/j.edurev.2014.05.001.

Illich, I. (1971). Deschooling Society. NY: Harper & Row.

Itani, A, Brisson, L., & Garlatti, S. (2018). Understanding Learner’s Drop-Out in MOOCs, Proceedings of 19th International Conference of IDEAL 2018 – Part I, Madrid, Spain. https://doi.org/10.1007/978-3-030-03493-1_25.

Kentnor, Η. (2013). Distance Education and the Evolution of Online Learning in the United States, Curriculum and Teaching Dialogue, 17(1–2), 21–34.

Lafifi, Y., Boudria, A., Lafifi A., & Cheraitia, M. (2020). Intelligent Tutoring of Learners In E-learning Systems and Massive Open Online Courses. In S. Gülseçen, S.K. Sharma, and E. Akadal. (eds), Who Runs the World:Data. Istanbul University Press (pp. 177–192). https://doi.org/ 10.26650/B/ET06.2020.011.10.

Lall’e, S., & Conati, C. (2021). A Framework to Counteract Suboptimal User-Behaviors in Exploratory Learning Environments: An Application to MOOCs. arXiv, 2106.07555. https://doi.org/10.48550/arXiv.2106.07555.

Li, C., & Zhou, H. (2018). Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability, Sustainability, 10(2), 468. https://doi.org/10.3390/su10020468.

Lin, Y., Feng, S., Lin, F. Zeng, W., Liu, Y., & Wu, P. (2021). Adaptive course recommendation in MOOCs, Knowledge-Based Systems, 224. https://doi.org/10.1016/j.knosys.2021.107085.

Lucas, R.W. (2010). The creative training idea book: Inspired tips and techniques for engaging and effective learning. New York: AMACOM.

Miloud, S.E., Soukaina, S., Salma, A., & El Hassan, C. (2020). An Adaptive Learning Approach for Better Retention of Learners in MOOCs, Proceedings of 3rd International Conference on Networking, Information Systems and Security, 26, 1–5. https://doi.org/10.1145/3386723. 3387845.

Nicholas, J.S., & Francis, F.S. (2017). Adaptive MOOCs to Foster Personalized Learning, International Journal of Computational Engineering Research (IJCER), 7(9), 12–18, ISSN (e): 2250–3005.

Niman, N.B. (2014). The gamification of higher education: Developing a game-based business strategy in a disrupted marketplace, NY: Palgrave Macmillan.

Pang, Y., Liu, W., Jin, Y., Peng, H., Xia, T., & Wu, Y. (2018). Adaptive recommendation for MOOC with collaborative filtering and time series, Computer Applications in Engineering Education, 26(6), 2071–2083. https://doi.org/10.1002/cae.21995.

Parfenov, D.I., & Zaporozhko, V. (2018). Developing SMART educational cloud environment on the basis of adaptive massive open online courses. Proceedings of the Workshop on Internationalization of Education in Applied Mathematics and Informatics for HighTech Applications (EMIT 2018) 2093, 35–41.

Pham, P., & Wang, J. (2016). Adaptive Review for Mobile MOOC Learning via Implicit Physiological Signal Sensing, Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI ‘16), (pp. 63–72). Tokyo, Japan. https://doi.org/10.1145/3242969.3243002.

Qaffas, A., Kaabi, K., Shadiev, R., & Essalmi, F. (2020). Towards an optimal personalization strategy in MOOCs, Smart Learning Environments, 7(14), 1–18. https://doi.org/10.1186/s40561-020-0117-y.

Roblyer, M.D., & Wiencke, W.R. (2003). Design and use of a rubric to assess and encourage interactive qualities in distance courses, The American Journal of Distance Education, 17(2), 77–98. https://doi.org/10.1207/S15389286AJDE1702_2.

Rosen, Y. Rushkin, I. Federicks, C. Tingley, D., & Blink, M.J. (2017). Designing Adaptive Assessments in MOOCs, Proceedings of the 4th ACM Conference on Learning @ Scale (pp. 233–236).

Rossano, V., Pesare, E., & Roselli, T. (2017). Are Computer Adaptive Tests suitable for assessment in MOOCs?, Journal of e-Learning and Knowledge Society, 13(3), 71-81. https://doi.org/10.20368/1971-8829/1393.

Sanchez-Gordon, S., & Luján-Mora, S. (2015). Adaptive Content Presentation Extension for Open edX. Enhancing MOOCs Accessibility for Users with Disabilities, Proceedings of the 8th International Conference on Advances in Computer-Human Interactions (pp. 181–183).

Sein-Echaluce, M.L., Fidalgo-Blanco, Á., & García-Peñalvo, F.J. (2017). The adaptive and cooperative model of knowledge management in MOOCs, Proceedings of the 4th International Conference, LCT 2017 (pp. 273–284). https://doi.org/10.1007/978-3-319-58509-3_22.

Shpolianskaya, I., & Seredkina, (2020). Intelligent Support System for Personalized Online Learning, Broad Research in Artificial Intelligence and Neuroscience, 11(3), 29–35. https://doi.org/10.18662/brain/11.3/107.

Siemens, G. (2005). Connectivism: A learning theory for the digital age, International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.

Slavkin, M.L. (2004). Authentic Learning: How Learning about the Brain Can Shape the Development of Students. R&L Education.

Smaili, E.M., Khoudda, C., Sraidi, S., Azzouzi S., & Charaf, M.E.H. (2022). An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study, Statistics, Optimization & Information Computing, 10(1), 45–58. https://doi.org/10.19139/soic-2310-5070-1206

Sonwalkar, N. (2012). The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture–Part I, MOOCs Forum, 1, 22–29. https://doi.org/10.1089/mooc.2013.0007.

Sprenger, M.B. (2010). Brain-Based Teaching in the Digital Age. 1st Edition. ASCD.

Sun, G., Cui, T. Li, K., Xu, D., Chen, S. Shen, J., & Guo, W. (2015). Towards bringing adaptive microlearning into MOOC courses, Proceedings of the IEEE International Conference on Advanced Learning Technologies (pp. 462–463), USA:IEEE. http://dx.doi.org/10.1109/ ICALT.2015.26.

Sun, G., Cui, T., Yong, J., Shen, J., & Chen, S. (2018). MLaaS: A Cloud-Based System for Delivering Adaptive Micro-Learning in Mobile MOOC Learning, IEEE Transactions on Services Computing, 11(2), 292–305. http://dx.doi.org/10.1109/TSC.2015.2473854.

Sun, Y., Guo, Y., & Zhao, Y. (2020). Understanding the determinants of learner engagement in MOOCs: An adaptive structuration perspective, Computers & Education, 157, 103963. https://doi.org/10.1016/j.compedu.2020.103963.

Tegos, S., Mavridis, A., & Demetriadis, S. (2021). Agent-Supported Peer Collaboration in MOOCs, Frontier Artificial Intelligence, 4, 710856. https://doi.org/10.3389/frai.2021.710856.

Teixeira, A., Garcia-Cabot, A., García-Lopéz, E., Mota J., & de-Marcos, L. (2016). A new competence-based approach for personalizing MOOCs in a mobile collaborative and networked environment, Ibero-American Journal of Distance Education, 19(1), 143–160. https://doi.org/10.5944/ried.19.1.14578.

Tomar, G.S., Sankaranarayanan, S., Wang, X., & Rosé, C.P. (2017). Coordinating Collaborative Chat in Massive Open Online Courses, in Transforming Learning, Empowering Learners, Singapore: International Society of the Learning Sciences, 1, 607–614.

Trirat, P., Noree, S, & Yi, M.Y. (2020). IntelliMOOC: Intelligent Online Learning Framework for MOOC Platforms, Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020) (pp. 682–685).

Tzeng, J.W., Lee, C.A., Huang, N.F., Huang H.H., & Lai, C.F. (2022). MOOC Evaluation System Based on Deep Learning, The International Review of Research in Open and Distributed Learning, 23(1), 21–40. https://doi.org/10.19173/irrodl.v22i4.5417.

Yilmaz, R., Yurdugül, H., Yilmaz, F.G.K., Şahi̇n, M., Sulak, S., Aydin, F., Tepgeç, M., Müftüoğlu, C.T., & Oral, Ö. (2022). Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal, Computers and Education: Artificial Intelligence, 3, 100092. https://doi.org/10.1016/j.caeai.2022.100092.

Yu, H., Miao, C., Leung, C., & White, T. J. (2017). Towards AI-powered personalization in MOOC learning, npj Science Learn, 2(15), 1–5. https://doi.org/10.1038/s41539-017-0016-3.

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Published : 2023-04-13


PapadimitriouA. (2023). Adaptive and Intelligent MOOCs: How They Contribute to the Improvement of the MOOCs’ Effectiveness. International Journal of Research in E-Learning, 9(1), 1-21. https://doi.org/10.31261/IJREL.2023.9.1.01

Alexandros Papadimitriou 
School of Humanities, Hellenic Open University, Greece  Greece
https://orcid.org/0000-0002-7661-731X




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