Individualization in Intelligent Tutoring Systems (ITS) is based on a specific digital copy of a learner (learner model) and a model of a learning situation. Both of these models are interpreted through a digital educational footprint and form the basis for making decisions on learning process management by the intelligent planner of the tutoring system. The effective ITSs, which provide individualization and reflection in interaction with a human learner, have to use means of generalization and data convolution. We propose to use a mapping mechanism based on the cognitive maps of knowledge diagnosis (CMKD) graphical notation. The paper shows how the maps are used by an ITS planner based on the cross-cutting approach to make decisions and form feedback in a learner-directed dialog. An example of such a dialog and its trajectory (master's student training) is given. The effectiveness of the approach is evaluated from the position of the Explicable Artificial Intelligence (XAI) concept. The results obtained during the pedagogical experiment are summarized and recommendations for the use of mapping in the implementation of ITS-based cross-cutting approach to managing the learning process are given.
AbyaaA.Khalidi IdrissiM.BennaniS. (2019). Learner modelling: Systematic review of the literature from the last 5 years. Educational Technology Research and Development, 67(5), 1105–1143. https://doi.org/10.1007/s11423-018-09644-1
2.
BakerR. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614. https://doi.org/10.1007/s40593-016-0105-0
3.
Barria-PinedaJ.AkhuseyinogluK.BrusilovskyP. (2023). Adaptive navigational support and explainable recommendations in a personalized programming practice system. Proceedings of the 34th ACM Conference on Hypertext and Social Media, 2023, 1–9. https://doi.org/10.1145/3603163.3609054
4.
BodilyR.VerbertK. (2017). Trends and issues in student-facing learning analytics reporting systems research. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017, 309–318. https://doi.org/10.1145/3027385.3027403
5.
BrusilovskyP. (2024). AI In education, learner control, and human-AI collaboration. International Journal of Artificial Intelligence in Education, 34(1), 122–135. https://doi.org/10.1007/s40593-023-00356-z
6.
ConatiC.BarralO.PutnamV.RiegerL. (2021). Toward personalized XAI: A case study in intelligent tutoring systems. Artificial Intelligence, 298, 103503. https://doi.org/10.1016/j.artint.2021.103503
7.
HorváthI. (2023). Horváth, I. Designing next-generation cyber-physical systems: Why is it an issue?Journal of Integrated Design and Process Science, 26(3-4), 317–349. https://doi.org/10.3233/JID-220008
8.
IlvesK.LeinonenJ.HellasA. (2018). Supporting self-regulated learning with visualizations in online learning environments. Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018, 257–262. https://doi.org/10.1145/3159450.3159509
9.
JacksonP. (1986). Introduction to expert systems. Addison-Wesley Pub. Co. (1986).
10.
JoshiA.KaleS.ChandelS.PalD. K. (2015). Likert Scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975
11.
KarpenkoA.DobryakovA. (2011). Model for automated training systems. Overview. Science and Education, 2011(7), 1–63. (in Russian). https://doi.org/10.7463/0715.0193116
12.
KhosraviH.ShumS. B.ChenG.ConatiC.TsaiY. S.KayJ.KnightS.Martinez-MaldonadoR.SadiqS.GaševićD. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074
13.
LiebenbergM.JarkeM. (2020). Information systems engineering with digital shadows: Concept and case studies: An exploratory paper. International Conference on Advanced Information Systems Engineering, 12127(9), 70–84. https://doi.org/10.1007/978-3-030-49435-3_5
14.
MilgramS. (1967). The small world problem. Psychology Today, 1967(2), 60–67.
15.
MillerG. (1956). The magical number seven, plus or minus two: Some limits on ourcapacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
16.
OsipovG. (2002). From situational control to applied semiotics. News of Artificial Intelligence, 2002(6), 3–7. (in Russian).
17.
PospelovD. (1996). Large semiotic model in control system. Proc. of Internation Workshop on Control Mechanisms and Complex Systems, 1996, 287–292. (in Russian).
18.
SinatraA.GraesserA. C.HuX.GoldbergB.HamptonA. J. (Eds.) (2020). Design recommendations for intelligent tutoring systems: Volume 8-data visualization. US Army Combat Capabilities Development Command–Soldier Center.
19.
TakadaS.Cuadros-VargasE.ImpagliazzoJ.GordonS.MarshallL.TopiH.Van der VeerG.WaguespackL. (2020). Toward the visual understanding of computing curricula. Education and Information Technologies, 25(5), 4231–4270. https://doi.org/10.1007/s10639-020-10127-1
20.
TanikM.GatchelS.HorváthI.WanT.KimK. Y.HuangJ.CoataneaE.KrämerB. J.ZengY. (2021). Footsteps towards a transdisciplinary design and process science. Journal of Integrated Design and Process Science, 2021(25), 1–16. https://doi.org/10.3233/https://doi.org/ JID-210035
21.
UglevV. (2023). Explanatory didactic dialogue in the intelligent tutoring systems based on the cross-cutting approach. International Conference on Intelligent Tutoring Systems. LNCS, 13891, 371–380. https://doi.org/10.1007/978-3-031-32883-1_34
22.
UglevV. (2024). Cognitive Maps of Knowledge Diagnosis (CMKD): the essence of the method, classification, characteristics and synthesis principles. In Novel & Intelligent Digital Systems: Proceedings of International Conference. LNNS, 1170, 2024(in press). https://doi.org/10.1007/978-3-031-73344-4_51.
23.
UglevV.GavrilovaT. (2022). Cross-cutting visual support of decision making for forming personalized learning spaces. Novel & Intelligent Digital Systems Conferences. LNNS, 556, 3–13. https://doi.org/10.1007/978-3-031-17601-2_1
24.
UglevV.SmirnovG. (2024). Combined maps as a tool of concentration and visualization of knowledge in the logic of operation of the Intelligent Tutoring Systems. Intelligent Tutoring Systems. LNCS, 14798, 27–39. https://doi.org/10.1007/978-3-031-63028-6_3
25.
UglevV.SychevO. (2022). Evaluation, comparison and monitoring of multiparametric systems by unified graphic visualization of activity method on the example of learning process. Algorithms, 15(12), 468. https://doi.org/10.3390/a15120468
26.
UglevV.ZakharinK.BaryshevR. (2020). Cognitive maps of knowledge diagnosis as an element of a digital educational footprint and a copyright object. Software Engineering Perspectives in Intelligent Systems: Proceedings of 4th Computational Methods in Systems and Software. AISC, 1295, 349–357. https://doi.org/10.1007/978-3-030-63319-6_31
