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Navigating Digital Classrooms As Educators—Are We Ready to Adapt?by@revising
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Navigating Digital Classrooms As Educators—Are We Ready to Adapt?

by RevisingFebruary 11th, 2025
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This paper is presented as a revised meta-architectural design for intelligent tutoring systems, incorporating educator roles for enhanced system transparency.
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Authors:

(1) Florian Gnadlinger, Faculty of Computer Science, Communication, and Economics, University of Applied Sciences Berlin, Germany;

(2) Simone Kriglstein, Faculty of Informatics, Masaryk University, Czech Republic.

Abstract and 1 Introduction

2 Background

3 Methode & Results

4 Discussion

5 Conclusion and References

3 METHODE & RESULTS

The presumption to the given research question (RQ1) is that educators are only able to use the full potential of intelligent tutoring systems if they (1) have access to the information obtained from the learners, (2) are able to understand and interpret this information, (3) and can transform this interpretation into valuable pedagogical and didactical actions. This aligns with the learning analytics process model [31] (see Figure 2), which is applicable to learner or teaching dashboards and authoring interfaces.


Figure 2: learning analytics process model [31].


Hence, regarding the results from an ongoing systematic literature review, we concluded a revised meta-architecture of intelligent tutoring systems that incorporate the role of educators (Figure 1 black elements). This draws attention to the design of teaching dashboards, allowing views into and interactions with the different models of such systems (compare with Figure 1). Besides static dashboards, a functional entity is needed to support the educators´ reflection process about the effectiveness of their teaching methods. We call this entity educator model.


4 DISCUSSION

With the given summary, the illustrated current systematic overview (Figure 1 gray elements), and a visualized extension proposal (Figure 1 black elements), we would like to point out a major implication for teachers in higher education when introducing intelligent tutoring systems into their educational setting.



Figure 1: Adaptive learning loop within digital learning environments. All gray elements represent the state-of-the-art system architecture of intelligent tutoring systems. All black elements represents the proposed extension.



If teachers in higher education are using or will start using intelligent tutoring systems, they should reflect on three main questions.


(1) Do I have access to all information incorporated into the different models of intelligent tutoring systems?


(2) I am able to understand and interpret this information?


(3) I am able to transform this interpretation into pedagogical or didactical actions?


5 CONCLUSION

With this contribution, we hope to give higher education teachers some leverage to participate in the discussion of the design of intelligent tutoring systems.


REFERENCES

  1. Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022): The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. doi:10.1007/s11528-022-00715-y


  2. Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., Wasson, B. (2023): Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning. doi:10.1111/jcal.12844


  3. Pishtari, G., Ley, T., Khalil, M., Kasepalu, R., Tuvi, I. (2023): Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review. Education Sciences. doi:10.3390/educsci13050498


  4. The Glossary of Education Reform (2014): Competency-Based Learning. https://www.edglossary.org/competencybased-learning/. Accessed 14 December 2023


  5. Alt, D., Nirit, R. (2018): Lifelong Citizenship. Lifelong Learning as a Lever for Moral and Democratic Values. Moral Development and Citizenship Education, vol. 13. Brill/Sense, Leiden, the Netherlands. doi:10.1163/9789463512398. 978-94-6351- 239-8


  6. Alt, D., Naamati-Schneider, L., Weishut, D.J. (2023): Competency-based learning and formative assessment feedback as precursors of college students’ soft skills acquisition. Studies in Higher Education. doi:10.1080/03075079.2023.2217203


  7. Levine, E., Patrick, S. (2019): What is competency-based education? An updated definition. https://files.eric.ed.gov/fulltext/ED604019.pdf. Accessed 14 December 2023


  8. Ghaicha, A. (2016): Theoretical Framework for Educational Assessment: A Synoptic Review. Journal of Education and Practice 7, 212–231


  9. Rust, C. (2002): Purposes and Principles of Assessment. Learning and Teaching Briefing Papers Series


  10. Curry, R.A., Gonzalez-DeJesus, N.T. (2010): A Literature Review of Assessment. Journal of Diagnostic Medical Sonography. doi:10.1177/8756479310361374


  11. Machin, L. (2016): A complete guide to the level 5 diploma in education and training. Further Education. Critical Publishing Ltd, Northwich. 978-1910391785


  12. Schildkamp, K., van der Kleij, F.M., Heitink, M.C., Kippers, W.B., Veldkamp, B.P. (2020): Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research. doi:10.1016/j.ijer.2020.101602


  13. Spatioti, A.G., Kazanidis, I., Pange, J. (2022): A Comparative Study of the ADDIE Instructional Design Model in Distance Education. Information. doi:10.3390/info13090402


  14. Frerejean, J., van Merriënboer, J.J., Kirschner, P.A., Roex, A., Aertgeerts, B., Marcellis, M. (2019): Designing instruction for complex learning: 4C/ID in higher education. Euro J of Education. doi:10.1111/ejed.12363


  15. Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R. (eds.) (2012): Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining 4(1). doi:10.5281/zenodo.3554641


  16. Gnadlinger, F., Selmanagić, A., Simbeck, K., Kriglstein, S. (2023): Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In: Jovanovic, J., Chounta, I.-A., Uhomoibhi, J., McLaren, B. (eds.) Proceedings of the 15th International Conference on Computer Supported Education. 15th International Conference on Computer Supported Education, Prague, Czech Republic, 21.04.2023 - 23.04.2023, pp. 272–280. SCITEPRESS. doi:10.5220/0011964700003470


  17. Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D., Williamson, D.M. (2015): Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. doi:10.1007/978-1- 4939-2125-6. 9781493921256


  18. Shute, V.J., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., Sun, C. (2021): Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. J Comput Assist Learn. doi:10.1111/jcal.12473


  19. Bryant, J., Heitz, C., Sanghvi, S., Wagle, D. (2020): How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/. Accessed 9 February 2024


  20. Hughes, J. (2022): Deskilling of Teaching and the Case for Intelligent Tutoring Systems. J. Eth. Emerg. Tech. doi:10.55613/jeet.v31i2.90


  21. Vanbecelaere, S., van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., Depaepe, F. (2020): The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning. doi:10.1111/jcal.12416


  22. Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A. (2020): Online Adaptive Learning. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. SITA'20: Theories and Applications, Rabat Morocco, 23 09 2020 24 09 2020, pp. 1–6. ACM, New York, NY, USA. doi:10.1145/3419604.3419759


  23. Gao, Y. (2023): The Potential of Adaptive Learning Systems to Enhance Learning Outcomes: A Meta-Analysis. doi:10.7939/r3-a6xdm403


  24. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011): Recommender Systems in Technology Enhanced Learning. In: Ricci, F. (ed.) Recommender systems handbook, pp. 387–415. Springer, New York. doi:10.1007/978-0-387-85820-3_12


  25. Ricci, F., Rokach, L., Shapira, B. (eds.) (2015): Recommender Systems Handbook. Springer, New York. doi:10.1007/978-1-4899- 7637-6. 978-1-4899-7636-9


  26. Ramadhan, A., Warnars, H.L.H.S., Razak, F.H.A. (2023): Combining intelligent tutoring systems and gamification: a systematic literature review. Educ Inf Technol. doi:10.1007/s10639-023-12092-x


  27. Kurni, M. (2023): A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 1st edn. Springer International Publishing; Springer, Cham. 978-3-031-32653-0


  28. Abdelbaset R. Almasri, Adel Ahmed, Naser Al-Masri, Yousef S. Abu Sultan, Ahmed Y. Mahmoud, Ihab Zaqout, Alaa N. Akkila, Samy S. Abu-Naser (2019): Intelligent Tutoring Systems Survey for the Period 2000- 2018. International Journal of Academic Engineering Research (IJAER) vol. 3, 21-37


  29. Woolf, B.P. (2010): Building Intelligent Interactive Tutors. Elsevier Science. 978-0-08- 092004-7


  30. Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D. (2018): Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature. Int J Artif Intell Educ. doi:10.1007/s40593- 017-0157-9


  31. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L. (2013): Learning Analytics Dashboard Applications. American Behavioral Scientist. doi:10.1177/0002764213479363

This paper is available on arxiv under CC BY 4.0 DEED license.