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
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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).
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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).
Google Scholar

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.
Google Scholar

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).
Google Scholar

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.
Google Scholar

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.
Google Scholar

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

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

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
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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).
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar