The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.
 L. A. James, Evaluation of an Adaptive Learning Technology as a Predictor of Student Performance in Undergraduate Biology, (Master’s thesis), Appalachian State University, North Carolina, USA, May 2012.
 A.Ohle, N. McElvany, Teachers’ diagnostic competences and their practical relevance. Special Issue Editorial, Journal for Educational Research Online, vol. 7, no. 2, 2015.
 B. Bloom, The 2 sigma problem: The search for methods of group instruction as effective as one-toone tutoring, Educ. Res., vol. 13, pp. 4-16, 1984.
 T. Kidd, Online Education and Adult Learning. New York: Hershey, 2010.
 M. Vandewaetere, P. Desmet, and G. Clarebout, The contribution of learner characteristics in the development of computer-based adaptive learning environments, Computers in Human Behavior, vol. 27, No. 1, pp. 118-130, 2011.
 C. Zhao, and L. Wan, A shortest learning path selection algorithm in e-learning, in Proc. 6th IEEE Int’l. Conf. on Advanced Learning Technologies (ICALT), 94-95, 2006.
 I. E. Allen, and J. Seaman, Staying the Course: Online Education in the United States. Sloan-C, Needham, MA: Sloan Consortium, 2008.
 S. Adkins, The Worldwide Market for Self-paced eLearning Products and Services: 2011-2016 Forecast and Analysis, Ambient Insight Premium Report, 2013.
 D. Ryan, E - learning Modules: DLR Associates Series, Author House, 2012
 R.C. Clark and R.E. Mayer, E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, 3rd ed., San Francisco, USA: JohnWiley & Sons, 2011.
 H. Beetham and R.Sharpe, Rethinking pedagogy for a digital age:Designing for 21st century learning, New York, NY:Routledge, 2013.
 S.Selvakumarasamy and D.Dekson, “Architecture of Adaptive E-Learning Ecosystem, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2013.
 B. Ciloglugil, and M. Inceoglu, User modeling for adaptive e-learning systems, Computational Science and Its Applications (ICCSA 2012), vol. 7335, pp. 5561, 2012.
 F. Essalmi, L. J. B. Ayed, M. Jemni, Kinshuk, and S. Graf, A fully personalization strategy of Elearning scenarios, Computers in Human Behavior, vol. 26, no. 4, pp. 581-591, 2010.
 Image from http://languageteachingtips.files.wordpress.com/2013/03/learningdeliverycontinuum.jpg, Retrieved Feburary 8, 2016
 V. J. Shute, and D. Zapata-Rivera, Adaptive educational systems, in P. Durlach (Ed.), Adaptive Technologies for Training and Education (pp. 7-27). New York: Cambridge University Press, 2012.
 S. Oxman, and W. Wong, White paper: Adaptive Learning Systems, A white paper from DVX/ De- Vry Education Group and Integrated Education Solutions, 2014.
 I. Adaptive Learning,White paper based upon the Speak Up 2011 national findings, Leveraging Intelligent Adaptive Learning to Personalize Education, Intelligent Adaptive Learning: Speak Up Reports, 2012.
 C. Martins, L. Faria, and E. Carrapatoso, An adaptive educational system for higher education, Proceedings of the 14th EUNIS 08 International Conference of European University Information Systems, Denmark, 2008.
 S. Haggard, The maturing of the MOOC: Literature review of Massive Open Online Courses and other forms of Online Distance Learning (BIS Research Paper Number 130), Department for Business, Innovation and Skills, london uk, Research Papers Research paper number 130, 2013.
 X. Chen, D. Barnett, and C. Stephens, Fad or future: The advantages and challenges of massive open online courses (MOOCs), In Research-to Practice Conference in Adult and Higher Education, pp. 20-21, 2013.
 D. Onah, J. Sinclair, R. Boyatt (2014), Dropout rates of massive open online courses: behavioural patterns. Proceedings of the 6th International Conference on Education and New Learning Technologies, pp. 5825-5834, 2014.
 H. Fournier, R. Kop, and H. Sitlia, The Value of Learning Analytics to Networked Learning on a Personal Learning Environment, in Proceedings of the 1st International Conference on Learning Analytics and Knowledge, New York, NY, USA, 2011, pp. 104-109.
 S. Fauvel and H. Yu, A Survey on Artificial Intelligence and Data Mining for MOOCs, SciRate, 2016.
 L. C. Lin and . A. J. Prez, Educational Data Mining and Learning Analytics: differences, similarities, and time evolution,” Rev. Univ. Soc. Conoc., vol. 12, no. 3, pp. 98-112, 2015.
 G. Siemens and R. S. J. d. Baker, Learning Analytics and Educational Data Mining: Towards Communication and Collaboration, in Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge, New York, NY, USA, 2012, pp. 252-254.
 T. Daradoumis Haralabus, J. Faulin, . A. Juan Prez, F. J. Martnez, I. Rodrguez-Ardura, and F. Xhafa, Customer Relationship Management applied to higher education: developing an e-monitoring system to improve relationships in electronic learning environments, International Journal of Services Technology and Management, vol.14(1), pp. 103-125, 2010.
 T. Daradoumis, A. A. Juan, F. Lera-Lpez, and J. Faulin, Using Collaboration Strategies to Support the Monitoring of Online Collaborative Learning Activity, in Technology Enhanced Learning. Quality of Teaching and Educational Reform, M. D. Lytras, P. O. D. Pablos, D. Avison, J. Sipior, Q. Jin,W. Leal, L. Uden, M. Thomas, S. Cervai, and D. Horner, Eds. Springer Berlin Heidelberg, 2010, pp. 271-277.
 A. A. Juan, T. Daradoumis, J. Faulin, and F. Xhafa, A data analysis model based on control charts to monitor online learning processes, Int. J. Bus. Intell. Data Min., vol. 4, no. 2, pp. 159-174, 2009.
 B. R. Prakash, D. M. Hanumanthappa, and V. Kavitha, Big Data in Educational Data Mining and Learning Analytics, Int. J. Innov. Res. Comput. Commun. Eng, Vol. 2, Issue 12, 2014
 C. Romero and S. Ventura, Data Mining in Education, Wiley Int Rev Data Min Knowl Disc, vol. 3, no. 1, pp. 12-27, 2013.
 C. Romero and S. Ventura, Educational data mining: A survey from 1995 to 2005, Expert Syst. Appl., vol. 33, no. 1, pp. 135-146, 2007.
 C. Romero and S. Ventura, Educational Data Mining: A Review of the State of the Art, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 40, no. 6, pp. 601-618, 2010.
 A. Pea-Ayala, Review: Educational Data Mining: A Survey and a Data Mining-based Analysis of Recent Works, Expert Syst Appl, vol. 41, no. 4, pp. 1432-1462, 2014.
 A. M and Z. R. A. M. J. Md, A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance, 2015, pp. 59-88
 R. Baker, Data Mining for Education, In: McGaw B, Peterson P, Baker E(eds) International Encyclopedia of Education (3rd edition), 7:112-118. Oxford, UK: Elsevier, 2010.
 R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules between Sets of Items in Large Databases, in: Proceedings Of The 1993 Acm Sigmod International Conference On Management Of Data, Washington DC (USA, 1993, pp. 207-216.
 E. Garca, C. Romero, S. Ventura, and T. Calders, Drawbacks and solutions of applying association rule mining in learning management systems, 2007, pp. 15-25.
 C. Romero, S. Ventura, M. Pechenizkiy, and R. S. J. d Baker, Handbook of Educational Data Mining. CRC Press, 2010
 B. K. Baradwaj and S. Pal, Mining Educational Data to Analyze Students’ Performance, ArXiv12013417 Cs, Jan. 2012.
 E. Frias-Martinez, G. Magoulas, S. Chen, and R Macredie, Recent soft computing approaches to user modeling in adaptive hypermedia, In Adaptive Hypermedia and AdaptiveWeb-Based Systems, the series Lecture Notes in Computer Science, vol. 3137, pp. 104-114, Springer Berlin Heidelberg, 2004.
 FM. Cin, and AF. Baba, Assessment of English proficiency by fuzzy logic approach, In International Educational Technology Conference, pp. 355-359. 2008.
 H. Gamboa, Designing Intelligent Tutoring Systems : A Bayesian Approach, Proc. of Ana Fred 3rd International Conference on Enterprise Information Systems (ICEIS’2001), 2001, pp. 452-458.
 A. Gertner, and K.VanLehn, Andes: A coached problem solving environment for physics, In Intelligent Tutoring Systems, vol.1839, pp. 133-142, Springer Berlin Heidelberg, 2000.
 J. Ma and D. N. Zhou, Fuzzy set approach to the assessment of student centered learning, IEEE Trans. Educ., vol. 33, pp. 237-241, 2000.
 H. Seridi-Bouchelaghem, T. Sari, and M. Sellami, A Neural Network for Generating Adaptive Lessons, Journal of Computer Science 1, no. 2, pp. 232-243, 2005.
 R. Sripan and B. Suksawat, Propose of Fuzzy Logic-Based Students’ Learning Assessment, Proceedings in the International Conference on Control, Automation and Systems, pp. 414-417, Gyeonggido, Korea, 2010.
 S. Venkatesan, and S. Fragomeni, Evaluating learning outcomes in PBL using fuzzy logic techniques, 19th Annual Conference of the Australasian Association for Engineering Education: To Industry and Beyond; Proceedings of the. Institution of Engineers, Australia, 2008.
 H. Cha, Y. Kim, S. Park, T. Yoon, Y. Jung, and J. Lee, Learning Style Diagnosis Based on User Interface Behavior for the Customization of Learning Interfaces in an Intelligent Tutoring System, Proceedings of the 8th International Conference on Intelligent Tutoring Systems, the series Lecture Notes in Computer Science, vol. 4053, pp. 513-524, Springer Berlin Heidelberg, 2006.
 N. Idris, N. Yusof, and P. Saad, Adaptive course sequencing for personalization of learning path using neural network, International Journal of Advanced Soft Computing and Its Applications, vol. 1, no.1, pp. 49-61, 2009.
 S. Gutierrez-Santos, J. Mayor-Berzal, C. Fernandez-Panadero, and CR. Kloos, Authoring of Probabilistic Sequencing in Adaptive Hypermedia with Bayesian Networks, Journal of Universal Computer Science, vol.16, no. 19, pp. 2801-2820, 2010.
 F. Moreno, A. Carreras, M. Moreno and E. R. Royo, Using Bayesian Networks in the Global Adaptive E-learning Process, EUNIS 2005, Manchester, pp. 1-4, 2005.
 D. Xu, H. Wang and K. Su, Intelligent student profiling with fuzzy models, in Proceedings of the 35th Hawaii International Conference on System Science (HICSS 2002), Hawaii, U.S.A, 2002.
 P. Brusilovsky, and E. Milln. User models for adaptive hypermedia and adaptive educational systems, The AdaptiveWeb, the series Lecture Notes in Computer Science, vol. 4321, pp. 3-53, Springer Berlin Heidelberg, 2007.
 A. Ahmad, O. Basir, and K. Hassanein, Adaptive user interfaces for intelligent e-Learning: issues and trends, in Proceedings of the Fourth International Conference on Electronic Business (ICEB2004), Xiyuan Hotel, Beijing, China, pp. 925-934, 2004.
 L. A. Zadeh, Fuzzy sets, Inf. Control, vol. 8, pp. 338-353, 1965.
 J. Bih, Paradigm shift -an introduction to fuzzy logic, IEEE Potentials, vol. 25, no. 1, pp. 6-21, 2006.
 J. Mendel, Fuzzy logic system for engineering: A tutorial, Proceedings of the IEEE, 1995, vol. 83, no. 3, pp. 345-374.
 J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems, Prentice Hall 2001.
 J. Mendel, H. Hagras, W. Tan, W. Melek, and H. Ying, Introduction to Type-2 Fuzzy Logic Control, John Wiley and IEEE Press,Hoboken, NJ, 2014
 A. Jameson, Numerical uncertainty management in user and student modeling: An overview of systems and issues, Use Modeling and User-adapted Interaction, vol. 5(3-4), pp. 103-251, 1996.
 A. Kavi, R. Pedraza-Jimnez, H. Molina-Bulla, F.J. Valverde-Albacete, J. Cid-Sueiro, and A. Navia- Vzquez, Student Modelling Based on Fuzzy Inference Mechanisms, Proceedings of the IEEE Region 8 EUROCON 2003, Computer as a Tool, Ljubljana, Slovenia, 2003.
 D. Chang, and C. Sun (1993), Fuzzy Assessment Learning Performance of Junior High School Students, Proceedings of the 1993 First National Symposium on Fuzzy Theory and Applications, Hsinchu, Taiwan, Republic of China, pp. 1-10, 1993.
 S. Chen, and C. Lee, New methods for students’ evaluating using fuzzy sets, Fuzzy Sets and Systems, vol.104, pp. 209-218, 1999.
 O. Nyknen, Inducing fuzzy models for student classification, Journal of Educational Technology and Society, vol.9, pp. 223-234,2006.
 S. Weon, J. Kim, Learning achievement evaluation strategy using fuzzy membership function, In Proceedings of the 31st ASEE/IEEE Frontiers In Education Conference,Reno, NV (pp. 19-24),2001.
 A. Kavi, Fuzzy user modeling for adaptation in educational hypermedia, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 34, no. 4, pp. 439-449, 2004.
 K. Almohammadi and H. Hagras, An Interval Type-2 Fuzzy Logic Based System for Customised Knowledge Delivery within Pervasive E-Learning Platforms, Proceeings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2872-2879, 2013.
 K. Almohammadi and H. Hagras, An adaptive fuzzy logic based system for improved knowledge delivery within intelligent E-Learning platforms, Proccedings of the the 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, 2013.
 K. Almohammadi, B. Yao, and H. Hagras, An interval type-2 fuzzy logic based system with user engagement feedback for customized knowledge delivery within intelligent E-learning platforms, Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 808-817, 2014.
 K. Almohammadi, H. Hagras, B. Yao, A. Alzahrani, D.Alghazzawi, and G. Aldabbagh, A Type-2 Fuzzy Logic Recommendation System for Adaptive Teaching, Journal of Soft Computing, 2015.
 T. M. Lakshmi, A. Martin, R. M. Begum, and V. P. Venkatesan, An Analysis on Performance of Decision Tree Algorithms using Student’s Qualitative Data, I.J.Modern Education and Computer Science, vol. 5, pp. 18-27, 2013.
 Margret H. Dunham, Data Mining: Introductory and advance topic, Pearson Education India, 2006.
 J.R.Quinlan, Induction of Decision Tree, Journal of Machine learning, Morgan Kaufmann Vol.1, 1986, pp. 81-106.
 C.-M. Chen, Intelligent Web-based Learning System with Personalized Learning Path Guidance, Comput. Educ., vol. 51, no. 2, pp. 787-814, 2008.
 C. F. Lin, Y.-C. Yeh, Y. H. Hung, and R. I. Chang, Data Mining for Providing a Personalized Learning Path in Creativity: An Application of Decision Trees, Comput Educ, vol. 68, pp. 199-210, 2013.
 B. Kumar Baradwaj, S. Pal, Mining Educational Data to Analyze Students’ Performance, International Journal of Advanced Computer Science and Applications, Volume 2, No. 6, 2011, pp 63-69.
 Adhatrao K, Gaykar A, Dhawan A, Jha R, Honrao V, Predicting Students’ Performance using ID3 and C4.5 classification algorithms,International Journal of Data Mining & Knowledge Management Process, Volume 3, No. 5, pp 39-52, 2013, DOI :10.5121/ijdkp.2013.3504.
 M. Pandey, V. Kumar Sharma, A Decision Tree Algorithm Pertaining to the Student Performance Analaysis and Prediction, International Journal of Computer Applications, volume 61, No. 13, Jan 2013, pp 1-5, DOI 10.5120/9985-4822
 B. K. Baradwaj and S. Pal, Mining Educational Data to Analyze Students’ Performance, ArXiv12013417 Cs, 2012.
 M. Huang, H. Huang, and M. Chen, Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach, Expert Syst. Appl., vol. 33, no. 3, pp. 551-564, Oct. 2007.
 L. Fausett, Ed., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Upper Saddle River, NJ, USA, Prentice-Hall, Inc., 1994.
 S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1998.
 A. S. Drigas, K. Argyri, and J. Vrettaros, Decade Review, Artificial Intelligence Techniques in Student Modeling, in Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All, vol. 49, M. D. Lytras, P. Ordonez de Pablos, E. Damiani, D. Avison, A. Naeve, and D. G. Horner, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 552-564.
 B. Naik and S. Ragothaman, Using Neural Networks to Predict MBA Student Success, Coll. Stud. J., vol. 38, no. 1, p. 143, 2004.
 Z. Ibrahim and D. Rusli, Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression, in 21st Annual SAS Malaysia Forum, 2007.
 C. Gonzalez, J. Burguillo, and M. Llamas, A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems, 2006, pp. 13-18.
 Martn, J., VanLehn, K. OLAE: Progress toward a multi-activity,Bayesian student modeler. Proceedings of the World Conference on Artificial Intelligence in Education. 1993, pp. 410-417.
 P. Garca, A. Amandi, S. Schiaffino, and M. Campo, Using Bayesian networks to detect students’ learning styles in a web-based education system, Proc ASAI Rosario, pp. 115-126, 2005.
 C. Conati, A. Gertner, and K. Vanlehn, Using Bayesian networks to manage uncertainty in student modeling, User Model. User-Adapt. Interact., vol. 12, no. 4, pp. 371-417, 2002.
 V. J. Shute, E. G. Hansen, and R. G. Almond, You Can’t Fatten A Hog by Weighing It - Or Can You? Evaluating an Assessment for Learning System Called ACED, Int. J. Artif. Intell. Educ., vol. 18, no. 4, pp. 289-316, 2008.
 K. Vanlehn, C. Lynch, K. Schulze, J. A. Shapiro, R. Shelby, L. Taylor, D. Treacy, A. Weinstein, and M. Wintersgill, The Andes Physics Tutoring System: Lessons Learned,Int. J. Artif. Intell. Ed, vol. 15, no. 3, pp. 147-204, 2005.
 C. J. Burke and M. Rosenblatt. A markovian function of a markov chain.The Annals of Mathematical Statistics, Vol. 29, No. 4, pp. 1112-1122, 1958.
 B. Shih, K. R. Koedinger, and R. Scheines, Discovery of Student Strategies using Hidden Markov Model Clustering, the Proceedings of the 6th International Conference on Educational Data Mining. 2010.
 L. Rabiner and B. Juang, An introduction to hidden Markov models, IEEE ASSP Mag., vol. 3, no. 1, pp. 4-16, 1986
 T. Doleck, R. B. Basnet, E. G. Poitras, and S. P. Lajoie, Mining learner-system interaction data: implications for modeling learner behaviors and improving overlay models, J. Comput. Educ., vol. 2, no. 4, pp. 421-447, Aug. 2015.
 Morteza.S. Anari, Maryam. S. Anari, Intelligent ELearning Systems Using Student Behavior Prediction, J. Basic. Appl. Sci. Res., 2(12)12017-12023, 2012.
 X. Huang, J. Yong, J. Li, J. Gao, Prediction of student actions using weighted Markov models, IT in Medicine and Education, IEEE International Symposium on Digital Object Identifier, 2008.
 S. Azough and M. B. E. H. Bouyakhf, Adaptive E-learning using Genetic Algorithms, IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 10, no. 7, pp. 237-277, 2010.