Skip to main content

A Review of Intelligent Tutoring in Secondary Education

  • Conference paper
  • First Online:
Computer Science – CACIC 2022 (CACIC 2022)

Abstract

The use of intelligent tutoring systems may be useful to support the teaching and learning process, especially in the area of Math, in order to contribute to improve the students’ academic level.

The selection strategies and evaluation criteria were defined in a previous work about Intelligent Tutoring in Teaching [11]. The present paper delves into the analysis of the selected intelligent tutoring systems taking their functional characteristics into consideration.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

References

  1. Aleks.https://www.aleks.com/. Último acceso: 5/02/2023

  2. Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward Tutoring Help Seeking. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30139-4_22

    Chapter  Google Scholar 

  3. Arroyo, I., Cooper, D., Burleson, W., Woolf, B.P.: Bayesian networks and linear regression models of students’ goals, moods, and emotions. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R. (eds.) Handbook of Educational Data Mining, pp. 323–338. CRC Press, Boca Raton (2010)

    Google Scholar 

  4. Arroyo, I., Woolf, B.P., Burelson, W., et al.: A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. Int. J. Artif. Intell. Educ. 24(387–426) (2014). https://doi.org/10.1007/s40593-014-0023-y. Últimoacceso 7 Feb 2023

  5. Springer link. https://link.springer.com/.Último acceso: febrero 2023

  6. Beal, C.R., Walles, R., Arroyo, I., Woolf, B.P.: On-line tutoring for math achievement testing: a controlled evaluation. J. Interact. Online Learn. 6(1), 43–55 (2007)

    Google Scholar 

  7. IEEE Xplore. https://www.ieee.org/.Último acceso: febrero 2023

  8. Wongwatkit, C., Srisawasdi, N., Hwang, G.-J., Panjaburee, P.: Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions. Interact. Learn. Environ. 25(7), 889–903 (2017)

    Article  Google Scholar 

  9. PREBI - SEDICI (UNLP).http://sedici.unlp.edu.ar/.Último aceso: febrero 2023

  10. Casanovas, I.: La didáctica en el diseño de simuladores digitales para la formación universitaria en la toma de decisiones. Tesis de Magíster en Docencia Universitaria, UTN-FRBA (2005)

    Google Scholar 

  11. Pezzini C, Thomas P, Tutores inteligentes en la enseñanza: Una revision y análisis en la educación secundaria. XXVIII Congreso Argentino de Ciencias de la Computación - CACIC 2022. Editorial de la Universidad Nacional de La Rioja (EUDELAR). ISBN: 978–987–1364–31–2

    Google Scholar 

  12. Chen, C.-M., Lee, H.-M., Chen, Y.-H.: Personalized e-learning system using item response theory. Comput. Educ. 44(3), 237–255 (2005)

    Article  Google Scholar 

  13. Collins, A., Brown, J.S., Newman, S.E.: Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In: Resnick, L.B. (ed.) Knowing, Learning, and Instruction: essays in honor of robert glaser, pp. 453–494. Lawrence Erlbaum Associates, Hillsdale (1989)

    Google Scholar 

  14. ACM, Association for computing Machinery. https://www.acm.org/. Último acceso: febrero 2023

  15. Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Model. User Adapted Interact. 4, 253–278 (1995)

    Article  Google Scholar 

  16. Craig, S.D., Graesser, A.C., Perez, R.S.: Advances from the Office of Naval Research STEM Grand Challenge: expanding the boundaries of intelligent tutoring systems. IJ STEM Ed 5, 11 (2018). https://doi.org/10.1186/s40594-018-0111-x. Últimoacceso 5 Feb 2023

  17. D’Mello, S.K., Graesser, A.C.: AutoTutor and affective AutoTutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intel. Syst. 2, 1–39 (2012)

    Article  Google Scholar 

  18. Falmagne, J. C., Albert, D., Doble, C., Eppstein, D., & Hu, X. (Eds.). (2013). Knowledge spaces: Applications in education. Springer Science & Business Media

    Google Scholar 

  19. Gan, W., Sun, Y., Ye, S., Fan, Y., Sun, Y.: AI-Tutor: Generating Tailored Remedial Questions and Answers Based on Cognitive Diagnostic Assessment. In: 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), Beijing, China, 2019, pp. 1–6 https://doi.org/10.1109/BESC48373.2019.8963236. Último Acceso: 5/02/2023

  20. Wen, Z.A., Silverstein, E., Zhao, Y., Amog, A.L., Garnett, K., Azenkot, S.: Teacher Views of Math E-learning Tools for Students with Specific Learning Disabilities. In The 22nd International ACM SIGACCESS Conference on Computers and Accessibility, Association for computing Machinery (ACM), New York, NY, USA, 1–13 (2020)

    Google Scholar 

  21. Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R.: TRG AutoTutor: a simulation of a human tutor.J. Cognit. Syst. Res. 1, 35–51 (1999)

    Google Scholar 

  22. Graesser, A.C., Chipman, P., Haynes, B.C., Olney, A.: AutoTutor: an intelligent tutoring system with mixed-initiative dialogue. IEEE Trans. Educ. 48(4), 612–618 (2005)

    Article  Google Scholar 

  23. Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H., Ventura, M., Olney, A., et al.: AutoTutor: a tutor with dialogue in natural language. Behav. Res. Meth. Instruments Comput. 36, 180–193 (2004)

    Article  Google Scholar 

  24. Graesser, A.C., Jeon, M., Dufty, D.: Agent technologies designed to facilitate interactive knowledge construction. Discourse Process. 45, 298–322 (2008)

    Article  Google Scholar 

  25. Heffernan, N.T., Heffernan, C.L.: The ASSISTments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24(4), 470–497 (2014). https://doi.org/10.1007/s40593-014-0024-x

    Article  MathSciNet  Google Scholar 

  26. Samantha, J., Reyes, J.-R., Víctor, C., Alan, R.N.: An affective learning ontology for educational systems (2016)

    Google Scholar 

  27. Mayer, R.E.: Multimedia Learning. Cambridge University Press, New York (2001)

    Book  Google Scholar 

  28. Ministerio de Educación de la República Argentina. https://www.argentina.gob.ar/educacion/evaluacion-e-informacion-educativa. Último acceso: 8/02/2023

  29. Murray, T., Arroyo, I.: Towards measuring and maintaining the zone of proximal development in adaptive instructional systems. In: Proceedings of the 6th International Conference on Intelligent Tutoring Systems. 749–758 (2002)

    Google Scholar 

  30. N. L. Miller, J. E. Sanchez-Galan and B. E. FernándezUse of an Intelligent Tutoring System for Mathematics by Students Who Aspire to Enter the Technological University of Panama. In: 7th International Engineering. Sciences and Technology Conference (IESTEC) 2019, 255–260 (2019). https://doi.org/10.1109/IESTEC46403.2019.00-66

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

    Article  Google Scholar 

  32. Nye, B., Pavlik, P., Windsor, A., et al.: SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. IJ STEM Ed 5, 12 (2018). https://doi.org/10.1186/s40594-018-0109-4. Últimoacceso 07 Feb 2023

  33. Olney, A.M., et al.: Guru: A Computer Tutor That Models Expert Human Tutors. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 256–261. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_32

    Chapter  Google Scholar 

  34. Perkins, D. (1995) La escuela inteligente. Gedisa

    Google Scholar 

  35. Pozo, J. I. (1998). Aprendices y maestros. Alianza

    Google Scholar 

  36. Zhang, B., Jia, J.: Evaluating an intelligent tutoring system for personalized math teaching. Int. Symp. Edu. Technol. (ISET) 2017, 126–130 (2017)

    Google Scholar 

  37. Rosen. P.: Tutoring Kids With Dyscalculia | Tutors for Math Issues. https://www.understood.org/en/articles/tutoring-kids-with-dyscalculia-what-you-need-to-know. Último Acceso: 8/02/2023

  38. Understood -For learning and thinking differences. Brain Breaks for Kids

    Google Scholar 

  39. VanLehn, K., Siler, S., Murray, C., Yamauchi, T., Baggett, W.B.: Why do only some events cause learning during human tutoring? Cogn. Instr. 21(3), 209–249 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)

    Article  Google Scholar 

  42. Villanueva, N.M., Costas, A.E., Hermida, D.F., Rodríguez, A.C.: SIMPLIFY ITS: An intelligent tutoring system based on cognitive diagnosis models and spaced learning. IEEE Global Eng. Educ. Conf. (EDUCON) 2018, 1703–1712 (2018). https://doi.org/10.1109/EDUCON.2018.8363440

    Article  Google Scholar 

  43. Vygotsky, L.: Mind in society: The development of higher psychological processes: Harvard University Press (1978)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to María Cecilia Pezzini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Pezzini, M.C., Thomas, P. (2023). A Review of Intelligent Tutoring in Secondary Education. In: Pesado, P. (eds) Computer Science – CACIC 2022. CACIC 2022. Communications in Computer and Information Science, vol 1778. Springer, Cham. https://doi.org/10.1007/978-3-031-34147-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34147-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34146-5

  • Online ISBN: 978-3-031-34147-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics