Skip to main content
SpringerLink
Log in

Review Study on the Adaptive and Personalized Intelligent Tutoring Systems in E-learning

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023) (AI2SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 931))

  • 145 Accesses

Abstract

E-learning has been widely acknowledged as a “game-changer” in the global education system, and the COVID-19 pandemic crisis further demonstrated its value. Consequently, educational institutions worldwide have reconsidered the significance of this crucial learning method by adopting remote learning and teaching through various e-learning platforms. However, deploying virtual classrooms is challenging, including time consumption, technological costs, a lack of learner interactivity, and the mismatch between learners’ cognitive abilities and learning styles. Addressing these concerns, understanding learners’ styles, and providing tailored and personalized content have become primary objectives for many contemporary e-learning systems. In order to achieve adaptive and personalized learning (PL), a multitude of Intelligent Tutoring Systems (ITSs) have been developed. Our literature review explicitly explores the advantages of ITSs in personalized and adaptive learning, as well as identifying some ITSs that integrate these features. Furthermore, our study aims to offer a comprehensive perspective on how and where ITSs can be beneficial for both personalized and adaptive learning (AL).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dhawan, S.: Online learning: a panacea in the time of COVID-19 crisis. J. Educ. Technol. Syst. 49, 5–22 (2020). https://doi.org/10.1177/0047239520934018

  2. Mcandrew, P.: Review of “game changers: education and information technologies” (by Diana G. Oblinger). J. Interact. Media Educ. 5 (2013). https://doi.org/10.5334/2013-05

  3. Coman, C., Țîru, L.G., Meseșan-Schmitz, L., Stanciu, C., Bularca, M.C.: Online teaching and learning in higher education during the coronavirus pandemic: students’ perspective. Sustainability 12 (2020). https://doi.org/10.3390/su122410367

  4. Caner, M.: The definition of blended learning in higher education. In: Blended Learning Environments for Adults: Evaluations and Frameworks, pp. 19–34 (2012)

    Google Scholar 

  5. Adedoyin, O.B., Soykan, E.: Covid-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ., 1–13 (2020). https://doi.org/10.1080/10494820.2020.1813180

  6. Kasim, N., Khalid, F.: Choosing the right learning management system (LMS) for the higher education institution context: a systematic review. Int. J. Emerg. Technol. Learn. (iJET) 11, 55 (2016). https://doi.org/10.3991/ijet.v11i06.5644

  7. Irlbeck, S., Mowat, J.: Learning content management system (LCMS) (2007)

    Google Scholar 

  8. Keller, C.: Virtual learning environments: three implementation perspectives. Learn. Media Technol. 30, 299–311 (2005). https://doi.org/10.1080/17439880500250527

    Article  Google Scholar 

  9. Yan, S., Song, R.: Virtual classroom and traditional classroom (2013). https://doi.org/10.2991/icetms.2013.16

  10. Milrad, M.: Designing an interactive learning environment to support children’s understanding in complex domains (1999)

    Google Scholar 

  11. Aeiad, E., Meziane, F.: An adaptable and personalised E-learning system applied to computer science programmes design. Edu. Inf. Technol. 24 (2019). https://doi.org/10.1007/s10639-018-9836-x

  12. Akyuz, Y.: Effects of intelligent tutoring systems (ITS) on personalized learning (PL). Creat. Educ. 11, 953–978 (2020). https://doi.org/10.4236/ce.2020.116069

    Article  Google Scholar 

  13. Kurniawan, M., Suharjito, S., Diana, Witjaksono, G.: Human anatomy learning systems using augmented reality on mobile application. Procedia Comput. Sci. 135, 80–88 (2018). https://doi.org/10.1016/j.procs.2018.08.152

  14. Ghali, M.J.A., Ayyad, A., Abu-Naser, S.S., Laban, M.A.: An intelligent tutoring system for teaching English grammar. Int. J. Eng. 2, 1–6 (2018)

    Google Scholar 

  15. Mayring, P.: Qualitative content analysis. Forum Qual. Sozialforschung 1 (2000)

    Google Scholar 

  16. Castro-Schez, J.J., González-Morcillo, C., Albusac, J., Vallejo-Fernandez, D.: An intelligent tutoring system for supporting active learning: a case study on predictive parsing learning. Inf. Sci. 544, 446–468 (2020)

    Article  Google Scholar 

  17. Vermote, B., Aelterman, N., Beyers, W., Aper, L., Buysschaert, F., Vansteenkiste, M.: The role of teachers’ motivation and mindsets in predicting a (de)motivating teaching style in higher education: a circumplex approach. Motiv. Emot. 44 (2020). https://doi.org/10.1007/s11031-020-09827-5

  18. Izard, C.E.: Emotion theory and research: highlights, unanswered questions, and emerging issues. Annu. Rev. Psychol. 60, 1–25 (2009). https://doi.org/10.1146/annurev.psych.60.110707.16353

    Article  Google Scholar 

  19. Rosati-Peterson, G.L., Piro, J.S., Straub, C., O’Callaghan, C.: A nonverbal immediacy treatment with pre-service teachers using mixed reality simulations. Cogent Educ. 8, 1882114 (2021). https://doi.org/10.1080/2331186X.2021.1882114

    Article  Google Scholar 

  20. Shekhar, R.: Development of Intelligent Tutoring System Framework: Using Guided Discovery Learning (2012). https://www.it.iitb.ac.in/~sri/students/rajashekhar-thesis.pdf

  21. Al Ghamdi, A.: Influence of lecturer immediacy on students’ learning outcomes: evidence from a distance education program at a university in Saudi Arabia. Int. J. Inf. Educ. Technol. 7, 35–39 (2015). https://doi.org/10.18178/ijiet.2017.7.1.838

  22. Hachem, E.-K.: Artificial Intelligence in E-Learning, p. 145. Taylor & Francis Group (2016, 2017)

    Google Scholar 

  23. Kularbphettong, K., Kedsiribut, P., Roonrakwit, P.: Developing an adaptive web-based intelligent tutoring system using mastery learning technique. Procedia Soc. Behav. Sci. 191, 686–691 (2015). https://doi.org/10.1016/j.sbspro.2015.04.619

    Article  Google Scholar 

  24. Bailón, A., Fajardo, W., Molina-Solana, M.: Intelligent tutoring system, based on video e-learning, for teaching artificial intelligence. In: Bajo, J., et al. (eds.) Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability. AISC, vol. 372, pp. 215–224. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19629-9_24

  25. Hooshyar, D., Ahmad, R.B., Yousefi, M., Fathi, M., Horng, S.J., Lim, H.: Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Comput. Educ. 94, 18–36 (2016). https://doi.org/10.1016/j.compedu.2015.10.013

    Article  Google Scholar 

  26. Cole, R., et al.: One-on-one and small group conversations with an intelligent virtual science tutor. Comput. Speech Lang. 50 (2018). https://doi.org/10.1016/j.csl.2018.01.002

  27. Treceño-Fernández, D., Calabia-del-Campo, J., Bote-Lorenzo, M., Gómez-Sánchez, E., Luis-García, R., Alberola-López, C.: Integration of an intelligent tutoring system in a magnetic resonance simulator for education: technical feasibility and user experience. Comput. Methods Programs Biomed. 195, 105634 (2020). https://doi.org/10.1016/j.cmpb.2020.105634

    Article  Google Scholar 

  28. Khalfallah, J., Ben Hadj Slama, J.: Facial expression recognition for intelligent tutoring systems in remote laboratories platform. Procedia Comput. Sci. 73 (2015). https://doi.org/10.1016/j.procs.2015.12.030

  29. Muangprathub, J., Boonjing, V., Chamnongthai, K.: Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon 6, e05227 (2020). https://doi.org/10.1016/j.heliyon.2020.e05227

    Article  Google Scholar 

  30. Yu, S.-J., Hsueh, Y.-L., Sun, J.C.-Y., Liu, H.-Z.: Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing. Comput. Educ. Artif. Intell. 2, 100030 (2021). https://doi.org/10.1016/j.caeai.2021.100030

    Article  Google Scholar 

  31. Lafraya, S.: Intercultural learning in non-formal education: theoretical frameworks and starting points (2011)

    Google Scholar 

  32. Fajardo, W., Gibaja, E., Caballero, E.: An Intelligent tutoring system for a virtual e-learning center, January 2006

    Google Scholar 

  33. Zhang, D., Zhao, J.L., Zhou, L., Nunamaker, J.F.: Can e-learning replace classroom learning? Commun. ACM 47, 75–79 (2004)

    Article  Google Scholar 

  34. Ruiz, J., Mintzer, M., Leipzig, R., Portal, A.: The impact of E-learning in medical education. Acad. Med. (2006). https://doi.org/10.1097/00001888-200603000-00002

    Article  Google Scholar 

  35. Brooks, C., Greer, J., Melis, E., Ullrich, C.: Combining ITS and elearning technologies: opportunities and challenges. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds.) ITS 2006. LNCS, vol. 4053, pp. 278–287. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_28

  36. Koutsojannis, C., Prentzas, J., Hatzilygeroudis, I.: A web-based intelligent tutoring system teaching nursing students fundamental aspects of biomedical technology. In: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 4024–4027 (2001)

    Google Scholar 

  37. Oliveira Neto, J., Nascimento, E.: Intelligent tutoring system for distance education. JISTEM J. Inf. Syst. Technol. Manag. 9, 109–122 (2012). https://doi.org/10.4301/S1807-17752012000100006

    Article  Google Scholar 

  38. Web-Based Virtual Learning Environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Q. 25, 401–426 (2001)

    Google Scholar 

  39. Sottilare, R., Graesser, A., Hu, X., Olney, A., Nye, B., Sinatra, A.: Introduction to domain modeling and GIFT, July 2016

    Google Scholar 

  40. Tomlinson, C., et al.: Differentiating instruction in response to student readiness, interest, and learning profile in academically diverse classrooms: a review of literature. J. Educ. Gifted 27, 119–145 (2003). https://doi.org/10.1177/016235320302700203

    Article  Google Scholar 

  41. Feng, M., Heffernan, N., Koedinger, K.: Student modeling in an intelligent tutoring system. In: Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation and Evaluation (2010). https://doi.org/10.4018/978-1-61692-008-1.ch011

  42. Andrade, M.S.: Effective eLearning and eTeaching — a theoretical model. In: Gradinarova, B. (ed.) E-Learning. IntechOpen, Rijeka (2015)

    Google Scholar 

  43. Piñeres, M., Josyula, D., Jiménez-Builes, J.: Multi-level pedagogical model for the personalization of pedagogical strategies in intelligent tutoring systems. Dyna (Medellin, Colombia) 82, 185–193 (2015). https://doi.org/10.15446/dyna.v82n194.49279

  44. Ma, W., Adesope, O., Nesbit, J., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106 (2014). https://doi.org/10.1037/a0037123

  45. Lameras, P., Arnab, S.: Power to the Teachers: an exploratory review on artificial intelligence in education. Information 13 (2022). https://doi.org/10.3390/info13010014

  46. Soloman, B., Felder, R.: Index of learning styles questionnaire. Learning (1999)

    Google Scholar 

  47. Amamou, S., Cheniti-Belcadhi, L.: Tutoring In project-based learning. Procedia Comput. Sci. 126, 176–185 (2018). https://doi.org/10.1016/j.procs.2018.07.221

    Article  Google Scholar 

  48. Almerich, G., Orellana, N., Suárez-Rodríguez, J., Díaz-García, I.: Teachers’ information and communication technology competences: a structural approach. Comput. Educ. 100, 110–125 (2016). https://doi.org/10.1016/j.compedu.2016.05.002

    Article  Google Scholar 

  49. Ghadirli, H.M., Rastgarpour, M.: A web-based adaptive and intelligent tutor by expert systems. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds.) Advances in Computing and Information Technology. AISC, vol. 177, pp. 87–95. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-31552-7_10

  50. Oulhaci, M.A., Tranvouez, E., Fournier, S., Espinasse, B.: Un système tutoriel intelligent pour simfor: Un jeu sérieux pour la gestion des risques. Aix-Marseille Université (2012)

    Google Scholar 

  51. Malik, M., Fatima, G., Ch, A.H., Sarwar, A.: E-Learning: students’ perspectives about asynchronous and synchronous resources at higher education level (2017)

    Google Scholar 

  52. Northey, G., Bucic, T., Chylinski, M., Govind, R.: Increasing student engagement using asynchronous learning. J. Mark. Educ. 37 (2015). https://doi.org/10.1177/0273475315589814

  53. Rouse, M.: Fallsimulationen und automatisches adaptives Feedback mittels Künstlicher Intelligenz in digitalen Lernumgebungen

    Google Scholar 

  54. Paassen, B., Hammer, B., Price, T.W., Barnes, T., Gross, S., Pinkwart, N.: The continuous hint factory - providing hints in vast and sparsely populated edit distance spaces. J. Educ. Data Min. 10, 1–35 (2018). https://doi.org/10.5281/zenodo.3554697

    Article  Google Scholar 

  55. Lau, R., Yen, N., Li, F., Wah, B.: Recent development in multimedia e-learning technologies. World Wide Web 17 (2014). https://doi.org/10.1007/s11280-013-0206-8

  56. Lodge, J.M., Kennedy, G., Lockyer, L., Arguel, A., Pachman, M.: Understanding difficulties and resulting confusion in learning: an integrative review. Front. Educ. 3 (2018). https://doi.org/10.3389/feduc.2018.00049

  57. Lane, J.M., Fetherston, T.: Beyond U-tube: an innovative use of online digital video analysis in teacher education (2008)

    Google Scholar 

  58. Pane, J., Steiner, E., Baird, M., Hamilton, L.: Continued progress: promising evidence on personalized learning (2015)

    Google Scholar 

  59. Zwartjes, L., et al.: A literature review of personalized learning and the Cloud (2015)

    Google Scholar 

  60. Basham, J., Hall, T., Carter Jr., R., Stahl, W.: An operationalized understanding of personalized learning. J. Spec. Educ. Technol. 31 (2016). https://doi.org/10.1177/0162643416660835

  61. Melzer, P.: A Conceptual Framework for Personalised Learning - Influence Factors, Design, and Support Potentials. Springer, Wiesbaden (2018). https://doi.org/10.1007/978-3-658-23095-1

  62. Michele, M.: Gardner’s Theory of Multiple Intelligences. https://www.simplypsychology.org/multiple-intelligences.html

  63. Walkington, C., Bernacki, M.L.: Appraising research on personalized learning: definitions, theoretical alignment, advancements, and future directions. J. Res. Technol. Educ. 52, 235–252 (2020). https://doi.org/10.1080/15391523.2020.1747757

    Article  Google Scholar 

  64. Shemshack, A., Spector, J.: A systematic literature review of personalized learning terms. Smart Learn. Environ. 7 (2020). https://doi.org/10.1186/s40561-020-00140-9

  65. Yang, D.: Instructional strategies and course design for teaching statistics online: perspectives from online students. Int. J. STEM Educ. 4 (2017). https://doi.org/10.1186/s40594-017-0096-x

  66. Mousavinasab, E., Zarifsanaiey, N., Kalhori, S.R.N., Rakhshan, M., Keikha, L., Saeedi, M.G.: Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interact. Learn. Environ. 29, 142–163 (2021). https://doi.org/10.1080/10494820.2018.1558257

    Article  Google Scholar 

  67. Morgan, B., Hogan, M., Hampton, A., Lippert, A., Graesser, A.: The need for personalized learning and the potential of intelligent tutoring systems, March 2020

    Google Scholar 

  68. Patel, A., Scott, B., Kinshuk, D.: Intelligent tutoring: from SAKI to Byzantium. Kybernetes 30, 807–819 (2001). https://doi.org/10.1108/03684920110392075

    Article  Google Scholar 

  69. Serdyukov, P.: Innovation in education: what works, what doesn’t, and what to do about it. J. Res. Innov. Teach. Learn. 10, 4–33 (2017). https://doi.org/10.1108/JRIT-10-2016-0007

    Article  Google Scholar 

  70. Fan, O., Pengcheng, J.: Artificial intelligence in education: the three paradigms. Comput. Educ. Artif. Intell. 2, 100020 (2021). https://doi.org/10.1016/j.caeai.2021.100020

    Article  Google Scholar 

  71. Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., Fischer, F.: Simulation-based learning in higher education: a meta-analysis. Rev. Educ. Res. 90, 499–541 (2020). https://doi.org/10.3102/0034654320933544

    Article  Google Scholar 

  72. Vandewaetere, M., Desmet, P., Clarebout, G.: The contribution of learner characteristics in the development of computer-based adaptive learning environments. Comput. Hum. Behav. 27, 118–130 (2011). https://doi.org/10.1016/j.chb.2010.07.038

    Article  Google Scholar 

  73. Verbert, K., et al.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5, 318–335 (2012). https://doi.org/10.1109/TLT.2012.11

    Article  Google Scholar 

  74. Al-Hmouz, A., Shen, J., Yan, J., Al-Hmouz, R.: Enhanced learner model for adaptive mobile learning, New York, NY, USA (2010)

    Google Scholar 

  75. Holden, O.L., Norris, M.E., Kuhlmeier, V.A.: Academic integrity in online assessment: a research review. Front. Educ. 6 (2021). https://doi.org/10.3389/feduc.2021.639814

  76. Tuomi, I.: The Impact of Artificial Intelligence on Learning, Teaching, and Education: Policies for the Future (2018). https://doi.org/10.2760/12297

  77. Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl. Based Syst. 22, 261–265 (2009). https://doi.org/10.1016/j.knosys.2009.01.008

    Article  Google Scholar 

  78. Deng, C., Ji, X., Rainey, C., Zhang, J., Lu, W.: Integrating machine learning with human knowledge. iScience 23, 101656 (2020). https://doi.org/10.1016/j.isci.2020.101656

  79. SenthilKumaran, V., Annamalai, S.: Recommendation system for adaptive e-learning using semantic net. Int. J. Comput. Appl. 63, 19–24 (2013). https://doi.org/10.5120/10478-5210

    Article  Google Scholar 

  80. Hwang, G.-J., Yang, L.-H., Wang, S.-Y.: A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Comput. Educ. 69, 121–130 (2013)

    Article  Google Scholar 

  81. Nafea, S.M., Siewe, F., He, Y.: On recommendation of learning objects using Felder-Silverman learning style model. IEEE Access 7, 163034–163048 (2019)

    Article  Google Scholar 

  82. Krauss, C.: Smart learning: time-dependent context-aware learning object recommendations (2016)

    Google Scholar 

  83. Martin, A., Collie, R., Nagy, R.: Adaptability and high school students’ online learning during COVID-19. Front. Psychol. (2021). https://doi.org/10.3389/fpsyg.2021.702163

    Article  Google Scholar 

  84. Dockterman, D.: Insights from 200+ years of personalized learning. npj Sci. Learn. 3 (2018). https://doi.org/10.1038/s41539-018-0033-x

  85. Gomede, E., de Barros, R.M., de Souza Mendes, L.: Deep auto encoders to adaptive E-learning recommender system. Comput. Educ. Artif. Intell. 2, 100009 (2021). https://doi.org/10.1016/j.caeai.2021.100009

  86. Guo, L., Wang, D., Gu, F., Li, Y., Yezhu, W., Rongting, Z.: Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view. Asia Pac. Educ. Rev. 22, 1–21 (2021). https://doi.org/10.1007/s12564-021-09697-7

    Article  Google Scholar 

  87. Ennouamani, S., Mahani, Z.: An overview of adaptive e-learning systems, December 2017

    Google Scholar 

  88. Dayagbil, F.T., Palompon, D.R., Garcia, L.L., Olvido, M.M.J.: Teaching and learning continuity amid and beyond the pandemic. Front. Educ. 6 (2021). https://doi.org/10.3389/feduc.2021.678692

  89. Oates, S.: The importance of autonomous, self-regulated learning in primary initial teacher training. Front. Educ. 4 (2019). https://doi.org/10.3389/feduc.2019.00102

  90. Uhomoibhi, J., et al.: Development of E-learning in higher education and future directions, May 2011

    Google Scholar 

  91. Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R.: AutoTutor: a simulation of a human tutor. Cognit. Syst. Res. 1, 35–51 (1999). https://doi.org/10.1016/S1389-0417(99)00005-4

    Article  Google Scholar 

  92. Mitrovic, A.: An intelligent SQL tutor on the web. Int. J. Artif. Intell. Educ. 13, 173–197 (2003)

    Google Scholar 

  93. Suraweera, P., Mitrovic, A.: An intelligent tutoring system for entity relationship modelling. Int. J. Artif. Intell. Educ. 14, 375–417 (2004)

    Google Scholar 

  94. Melis, E., Siekmann, J.: ACTIVEMATH: an intelligent tutoring system for mathematics. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS, vol. 3070, pp. 91–101. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_12

  95. Baghaei, N., Mitrovic, A., Irwin, W.: Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. Int. J. Comput. Support. Collab. Learn. 2, 159–190 (2007)

    Article  Google Scholar 

  96. Cheung, B., Hui, L., Zhang, J., Yiu, S.M.: SmartTutor: an intelligent tutoring system in web-based adult education. J. Syst. Softw. 68, 11–25 (2003). https://doi.org/10.1016/S0164-1212(02)00133-4

    Article  Google Scholar 

  97. Heffernan, N., Turner, T., Lourenco, A., Macasek, M., Nuzzo-Jones, G., Koedinger, K.: The ASSISTment builder: towards an analysis of cost effectiveness of ITS creation, January 2006

    Google Scholar 

  98. Woolf, B.: Reasoning about teaching and learning, January 2003

    Google Scholar 

  99. Schiaffino, S., Garcia, P., Amandi, A.: ETeacher: providing personalized assistance to e-learning students. Comput. Educ. 51, 1744–1754 (2008). https://doi.org/10.1016/j.compedu.2008.05.008

    Article  Google Scholar 

  100. Keleş, A., Ocak, R., Keleş, A., Gülcü, A.: ZOSMAT: web-based intelligent tutoring system for teaching–learning process. Expert Syst. Appl. 36, 1229–1239 (2009). https://doi.org/10.1016/j.eswa.2007.11.064

    Article  Google Scholar 

  101. McLaren, B.M., Adams, D., Mayer, R.E.: Delayed learning effects with erroneous examples: a study of learning decimals with a web-based tutor. Int. J. Artif. Intell. Educ. 25, 520–542 (2015)

    Article  Google Scholar 

  102. Adams, D.M., McLaren, B.M., Mayer, R.E., Goguadze, G., Isotani, S.: Erroneous examples as desirable difficulty. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 803–806. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_117

  103. Oproiu, G.: A study about using e-learning platform (moodle) in university teaching process. Procedia Soc. Behav. Sci. 180, 426–432 (2015). https://doi.org/10.1016/j.sbspro.2015.02.140

    Article  Google Scholar 

  104. Weber, G., Brusilovsky, P.: ELM-ART – an interactive and intelligent web-based electronic textbook. Int. J. Artif. Intell. Educ. 26, (2015). https://doi.org/10.1007/s40593-015-0066-8

  105. Palaigeorgiou, G., Chloptsidou, I., Lemonidis, C.: Computational estimation in the classroom with tablets, interactive selfie video and self-regulated learning. In: Auer, M., Tsiatsos, T. (eds.) IMCL 2017. AISC, vol. 725, pp. 860–871. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75175-7_84

  106. Nacher, M.J., Badenes-Ribera, L., Torrijos, C., Ballesteros, M., Cebadera, E.: The effectiveness of the GoKoan e-learning platform in improving university students’ academic performance. Stud. Educ. Eval. 70, 101026 (2021). https://doi.org/10.1016/j.stueduc.2021.101026

    Article  Google Scholar 

  107. Okide, C., et al.: e-learning platforms for community development: a tool for prospective students. J. Eng. Appl. Sci. 14, 7420–7425 (2019). https://doi.org/10.36478/jeasci.2019.7420.7425

  108. mine, A. of: A Customizable eLearning platform built for your organization. https://www.academyofmine.com/

  109. Md. Ngadiran, N., Alias, N., Anuar, N.: M-learning module for self-directed language learning; a study of students’ needs analysis (2021). https://doi.org/10.30880/dils.2021.01.01.001

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences, Moulay Ismail University, Meknes, Morocco

    Atmane El Hadbi, El Kaber Hachem, Hamid Bourray, Mohammed Hatim Rziki & Houda Oubalahcen

Authors
  1. Atmane El Hadbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. El Kaber Hachem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Bourray
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed Hatim Rziki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Houda Oubalahcen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atmane El Hadbi .

Editor information

Editors and Affiliations

  1. Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Hadbi, A., Hachem, E.K., Bourray, H., Rziki, M.H., Oubalahcen, H. (2024). Review Study on the Adaptive and Personalized Intelligent Tutoring Systems in E-learning. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-031-54288-6_5

Download citation

Publish with us

Policies and ethics

Search

Navigation