1. Adedoyin, O.B., Soykan, E.: Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learn. Environ.1–13 (2020)

   Google Scholar 

2. Albacete, P., Jordan, P., Katz, S., Chounta, I.-A., McLaren, B.M.: The impact of student model updates on contingent scaffolding in a natural-language tutoring system. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 37–47. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-23204-7_4

   Chapter  Google Scholar 

3. Barokas, J., Ketterl, M., Brooks, C., Greer, J.: Lecture capture: student perceptions, expectations, and behaviors. In: Sanchez, J., Zhang, K. (eds.) World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Educatio, pp. 424–431 (2010)

   Google Scholar 

4. Basilaia, G., Kvavadze, D.: Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Res.5(4) (2020)

   Google Scholar 

5. Brown, G.T.L., Harris, L.R.: Student self-assessment. In: McMillan, J.H. (ed.) The SAGE Handbook of Research on Classroom Assessment, pp. 367–393. Sage, Thousand Oaks (2013)

   Google Scholar 

6. Brown, G.T., Andrade, H.L., Chen, F.: Accuracy in student self-assessment: directions and cautions for research. Assess. Educ.: Principles, Policy Practice22(4), 444–457 (2015)

   Article  Google Scholar 

7. Büdenbender, J., Frischauf, A., Goguadze, G., Melis, E., Libbrecht, P., Ullrich, C.: Using computer algebra systems as cognitive tools. In: International Conference on Intelligent Tutoring Systems, pp. 802–810 (2002)

   Google Scholar 

8. Chi, M., Koedinger, K., Gordon, G., Jordan, P., Vanlehn, K.: Instructional factors analysis: a cognitive model for multiple instructional interventions. In: EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, pp. 61–70 (2011)

   Google Scholar 

9. Coffield, F.J., Moseley, D.V., Hall, E., Ecclestone, K.: Learning Styles for Post 16 Learners: What Do We Know?. Learning and Skills Research Centre/University of Newcastle upon Tyne, London (2004)

   Google Scholar 

10. Crowell, T.L.: Student self grading: perception vs. reality. Am. J. Educ. Res.3(4), 450–455 (2015).https://doi.org/10.12691/education-3-4-10

11. Demmans Epp, C., Phirangee, K., Hewitt, J., Perfetti, C.A.: Learning management system and course influences on student actions and learning experiences. Educ. Tech. Research Dev.68(6), 3263–3297 (2020).https://doi.org/10.1007/s11423-020-09821-1

    Article  Google Scholar 

12. Fossati, D., Di Eugenio, B., Ohlsson, S., Brown, C., Chen, L.: Data driven automatic feedback generation in the iList intelligent tutoring system. Technol. Instr. Cogn. Learn.10(1), 5–26 (2015)

    Google Scholar 

13. Graesser, A., VanLehn, K., Rose, C., Jordan, P., Harter, D.: Intelligent tutoring systems with conversational dialogue. AI Mag.22(4), 39–51 (2001)

    Google Scholar 

14. Honey, P., Mumford, A.: The Manual of Learning Styles. Peter Honey Publications, Maidenhead (1992)

    Google Scholar 

15. Hrastinski, S., Stenbom, S., Benjaminsson, S., Jansson, M.: Identifying and exploring the effects of different types of tutor questions in individual online synchronous tutoring in mathematics. Interactive Learn. Environ.1–13 (2019)

    Google Scholar 

16. Kashihara, A., Hasegawa, S.: A model of meta-learning for web-based navigational learning. Int. J. Adv. Technol. Learn.2(4), 198–206 (2005)

    Google Scholar 

17. Koedinger, K., Corbett, A.: Cognitive tutors: technology bringing learning sciences to the classroom. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 61–78. Cambridge University Press, New York (2006)

    Google Scholar 

18. Kolb, D.A.: Experiential Learning Experience as the Source of Learning and Development. Prentice Hall, New Jersey (1984)

    Google Scholar 

19. Kumar, A.N.: Results from the evaluation of the effectiveness of an online tutor on expression evaluation. ACM SIGCSE Bull.37(1), 216–220 (2005)

    Article  Google Scholar 

20. Kumar, A.N.: Generation of problems, answers, grade, and feedback–case study of a fully automated tutor. J. Educ. Resourc. Comput. (JERIC)5(3), 3-es (2005)

    Google Scholar 

21. Lin, C.F., Yeh, Y.C., Hung, Y.H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput. Educ.68, 199–210 (2013).https://doi.org/10.1016/j.compedu.2013.05.009

    Article  Google Scholar 

22. Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. (2014)

    Google Scholar 

23. Mark, M.A., Greer, J.E.: Evaluation methodologies for intelligent tutoring systems. J. Artif. Intell. Educ.4, 129–129 (1993)

    Google Scholar 

24. Melis, E., Siekmann, J.: ActiveMath: an intelligent tutoring system for mathematics. Artif. Intell. Soft Comput.91–101 (2004)

    Google Scholar 

25. Munshi, A., Biswas, G.: Personalization in OELEs: developing a data-driven framework to model and scaffold SRL processes. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11626, pp. 354–358. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-23207-8_65

    Chapter  Google Scholar 

26. Narciss, S., et al.: Exploring feedback and student characteristics relevant for personalizing feedback strategies. Comput. Educ.71, 56–76 (2014)

    Article  Google Scholar 

27. Nye, B.D., Graesser, A.C., Hu, X.: AutoTutor and family: a review of 17 years of natural language tutoring. Int. J. Artif. Intell. Educ.24(4), 427–469 (2014)

    Article  Google Scholar 

28. Onyema, E.M., et al.: Impact of Coronavirus pandemic on education. J. Educ. Pract.11(13), 108–121 (2020)

    Google Scholar 

29. Penstein, C.R., Moore, J., VanLehn, K., Allbritton, D.: A comparative evaluation of socratic versus didactic tutoring. Ann. Meeting Cogn. Sci. Soc.23 (2001)

    Google Scholar 

30. Psotka, J., Massey, D., Mutter, S.: Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates, Hillsdale (1988)

    Google Scholar 

31. Rus, V., Stefanescu, D., Baggett, W., Niraula, N., Franceschetti, D., Graesser, A.C.: Macro-adaptation in conversational intelligent tutoring matters. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 242–247. Springer, Cham (2014).https://doi.org/10.1007/978-3-319-07221-0_29

    Chapter  Google Scholar 

32. Rus, V., Stefanescu, D., Niraula, N., Graesser, A.C.: DeepTutor: towards macro-and micro-adaptive conversational intelligent tutoring at scale. In: Proceedings of the first ACM Conference on Learning@ Scale Conference, pp. 209–210 (2014)

    Google Scholar 

33. Sampson, D., Karagiannidis, C.: Personalised learning: educational, technological and standarisation perspective. Digit. Educ. Rev.4, 24–39 (2002)

    Google Scholar 

34. Serban, I.V., et al.: A large-scale, open-domain, mixed-interface dialogue-based ITS for STEM. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 387–392. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-52240-7_70

    Chapter  Google Scholar 

35. Kochmar, E., Vu, D.D., Belfer, R., Gupta, V., Serban, I.V., Pineau, J.: Automated personalized feedback improves learning gains in an intelligent tutoring system. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 140–146. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-52240-7_26

    Chapter  Google Scholar 

36. Grenander, M., Belfer, R., Kochmar, E., Serban, I.V., St-Hilaire, F., Cheung, J.C.: Deep discourse analysis for generating personalized feedback in intelligent tutor systems. In: The 11th Symposium on Educational Advances in Artificial Intelligence (2021)

    Google Scholar 

37. Stash, N.V., Cristea, A.I., De Bra, P.M.: Authoring of learning styles in adaptive hypermedia: problems and solutions. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 114–123 (2004)

    Google Scholar 

38. Tan, Y., Quintana, R.M.: What can we learn about learner interaction when one course is hosted on two MOOC platforms? In: Companion Proceedings to the International Conference on Learning Analytics and Knowledge (LAK), pp. 155–156 (2019)

    Google Scholar 

39. Tomkins, S., Ramesh, A., Getoor, L.: Predicting Post-Test Performance from Online Student Behavior: A High School MOOC Case Study. International Educational Data Mining Society (2016)

    Google Scholar 

40. VanLehn, K., Graesser, A.C., Jackson, G., Jordan, P., Olney, A., Rose, C.P.: When are tutorial dialogues more effective than reading? Cogn. Sci.31(1), 3–62 (2007)

    Article  Google Scholar 

41. VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. (2011)

    Google Scholar 

42. Wang, Y., Paquette, L., Baker, R.: A longitudinal study on learner career advancement in MOOCs. J. Learn. Anal.1(3), 203–206 (2014)

    Article  Google Scholar 

43. Wood, D., Wood, H.: Vygotsky, tutoring and learning. Oxf. Rev. Educ.22(1), 5–16 (1996)

    Article  Google Scholar 

44. Woolf, B.: Building Intelligent Interactive Tutors. Morgan Kaufmann Publishers, Burlington (2009)

    Google Scholar 

45. Yin, B., Patikorn, T., Botelho, A.F., Heffernan, N.T.: Observing personalizations in learning: identifying heterogeneous treatment effects using causal trees. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 299–302 (2017)

    Google Scholar 

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