Skip to main content

Synthesizing Didactic Explanatory Texts in Intelligent Tutoring Systems Based on the Information in Cognitive Maps

  • Conference paper
  • First Online:
Augmented Intelligence and Intelligent Tutoring Systems (ITS 2023)

Abstract

The paper deals with the problem of synthesizing texts that explain decisions of an Intelligent Tutoring System for the learner. Solving this problem is important for increasing human trust in the ITS decisions by finding effective arguments and synthesizing explanatory texts. We describe an approach to preparing and concentrating raw data that is similar to the process used for mapping. The texts explaining the system’s decisions are generated using the method of parametric maps, which are visualized as Cognitive Maps of Knowledge Diagnosis: the transition from particular maps to the combined map and the development of a system of arguments based on these data. We show the structure of an explanatory text and the accompanying visualization of a cognitive map. We demonstrate the explanation synthesis on the example of a graduate student in the course “Simulation modeling”. The explanation consists of the text and supporting visualization. Preliminary analysis has shown significant student interest in receiving explanations, containing verbal and non-verbal (cognitive maps) components, from the Intelligent Tutoring System about its decisions.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Notes

  1. 1.

    https://aesfu.ru/.

References

  1. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012

    Article  Google Scholar 

  2. Bodily, R., Verbert, K.: Trends and issues in student-facing learning analytics reporting systems research. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 309–318. LAK 2017, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3027385.3027403

  3. Brusilovsky, P., Rus, V.: Social Navigation for Self-Improving Intelligent Educational Systems, pp. 131–145. Army Research Laboratory (2019). https://www.pitt.edu/peterb/papers/SocNav4SIS.pdf

  4. Fullér, R., et al.: Fuzzy Reasoning and Fuzzy Optimization. Turku Centre for Computer Science Turku, Finland (1998)

    Google Scholar 

  5. Gavrilova, T.A., Kokoulina, L.: Using ontology engineering to design an artificial intelligence course. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2019. SIST, vol. 144, pp. 201–207. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-8260-4_19

    Chapter  Google Scholar 

  6. Haspelmath, M., Michaelis, S.M.: Analytic and synthetic: typological change in varieties of European languages. In: Language Variation-European Perspectives VI: Selected Papers from the Eighth International Conference on Language Variation in Europe (ICLaVE 8), pp. 3–22. John Benjamins, Amsterdam (2017)

    Google Scholar 

  7. Ilves, K., Leinonen, J., Hellas, A.: Supporting self-regulated learning with visualizations in online learning environments. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 257–262. Association for Computing Machinery, New York, USA (2018). https://doi.org/10.1145/3159450.3159509

  8. Kelsey, E., Ray, F., Brown, D., Robson, R.: Design of a domain-independent, interactive, dialogue-based tutor for use within the GIFT framework. In: Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (giftsym3), pp. 161–168 (2015)

    Google Scholar 

  9. Mashbitz, E., Andrievskays, V., Komissarova, E.: Dialog in a Tutoring System. Higher School, Kiev (1989). (in Russian)

    Google Scholar 

  10. O’Rourke, E., Andersen, E., Gulwani, S., Popović, Z.: A framework for automatically generating interactive instructional scaffolding. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1545–1554. CHI 2015, Association for Computing Machinery, New York, NY, USA (2015). https://doi.org/10.1145/2702123.2702580

  11. Razzaq, L., Heffernan, N.T.: Scaffolding vs. hints in the assistment system. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 635–644. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_63

    Chapter  Google Scholar 

  12. Risha, Z., Barria-Pineda, J., Akhuseyinoglu, K., Brusilovsky, P.: Stepwise help and scaffolding for java code tracing problems with an interactive trace table. In: Proceedings of the 21st Koli Calling International Conference on Computing Education Research. Koli Calling 2021, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3488042.3490508

  13. Rubinstein, R.Y., Kroese, D.P.: Simulation and the Monte Carlo Method. Wiley, Hoboken (2016)

    Book  MATH  Google Scholar 

  14. 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. p. 209–210. L@S 2014, Association for Computing Machinery, New York, NY, USA (2014). https://doi.org/10.1145/2556325.2567885

  15. Rybina, G., Slinkov, A.: The implementation of the ontological approach to control of the processes of designing integrated expert systems based on the problem-oriented methodology. In: Kovalev, S.M., Kuznetsov, S.O., Panov, A.I. (eds.) RCAI 2021. LNCS (LNAI), vol. 12948, pp. 354–364. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86855-0_25

    Chapter  Google Scholar 

  16. Stefanuk, V.L.: Learning levels in intelligent tutoring systems. IEICE Trans. Inf. Syst. 78(9), 1103–1107 (1995)

    Google Scholar 

  17. Sychev, O., Anikin, A., Penskoy, N., Denisov, M., Prokudin, A.: CompPrehension - model-based intelligent tutoring system on comprehension level. In: Cristea, A.I., Troussas, C. (eds.) ITS 2021. LNCS, vol. 12677, pp. 52–59. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80421-3_6

    Chapter  Google Scholar 

  18. Troussas, C., Krouska, A.: Path-based recommender system for learning activities using knowledge graphs. Information 14(1), 9 (2023)

    Article  Google Scholar 

  19. Uglev, V., Rogozhnikova, M.: Individualized natural language dialogue with the students in the intellectual education system. In: Interactive Systems and Technologies: the Problem of Human-Computer Interaction, vol. 11, pp. 255–259 (2015). http://conf-is.ulstu.ru/sites/default/files/IS_2015_Part_II_1.pdf

  20. Uglev, V., Sychev, O.: Creating and visualising cognitive maps of knowledge diagnosis during the processing of learning digital footprint. In: Cristea, A.I., Troussas, C. (eds.) ITS 2021. LNCS, vol. 12677, pp. 93–98. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80421-3_11

    Chapter  Google Scholar 

  21. Uglev, V., Sychev, O.: Evaluation, comparison and monitoring of multiparameter systems by unified graphic visualization of activity method on the example of learning process. Algorithms 15(21), 468 (2022). https://doi.org/10.3390/a15120468

  22. Uglev, V., Sychev, O., Gavrilova, T.: Cross-cutting support of making and explaining decisions in Intelligent tutoring systems using cognitive maps of knowledge diagnosis. In: Crossley, S., Popescu, E. (eds.) Intelligent Tutoring Systems ITS 2022. Lecture Notes in Computer Science, vol. 13284, pp. 51–64. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09680-8_5

    Chapter  Google Scholar 

  23. Zadeh, L.A., Aliev, R.A.: Fuzzy Logic Theory and Applications: Part I and Part II. World Scientific Publishing, Singapore (2018)

    Book  Google Scholar 

  24. Zouri, M., Ferworn, A.: An ontology-based approach for curriculum mapping in higher education. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0141–0147. IEEE (2021). https://doi.org/10.1109/CCWC51732.2021.9376163

  25. Zykina, A., Kaneva, O., Munko, V.: The development of approaches for obtaining automated solution on the formation of the curriculum. Mod. Inf. Technol. IT-Educ. 14(4), 931–937 (2018). https://doi.org/10.25559/SITITO.14.201804.931-937

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Viktor Uglev .

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

Uglev, V., Sychev, O. (2023). Synthesizing Didactic Explanatory Texts in Intelligent Tutoring Systems Based on the Information in Cognitive Maps. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-32883-1_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32882-4

  • Online ISBN: 978-3-031-32883-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics