Abstract
Learner models are constructed from learner understanding states regarding learning materials, obtained by observing learner behaviors. A learner model collects information for adaptive tutoring and uses this information to determine system actions such as hints, problem presentations, and explanations appropriate to learner understanding. A learner model thus plays an essential role in intelligent tutoring systems (ITSs). Our previously developed ITSs have adopted a simple learner model that considers once-solved problems as learner-acquired solutions for problem solving. However, such models cannot grasp learner proficiency for solutions, which is an important aspect of constructing learner models because learners often make problem-solving mistakes despite having already learned the solution. In such situations, it is effective for an ITS to apply scaffolding based on learner proficiency regarding the solution. However, excess scaffolding decreases comprehension, a phenomenon known as the “assistance dilemma.” A learner model that adaptively scaffolds based on analysis of learner proficiency in solutions can effectively mitigate the assistance dilemma. In this study, we develop and evaluate an ITS with a learner model that considers learner proficiency in solutions. Experimental results indicate that the developed learner model can reduce total feedback without changing learner comprehension.
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References
Hirashima, T., Kashihara, A., Toyoda, J.: Providing problem explanation for ITS. In: Frasson, C., Gauthier, G., McCalla, G.I. (eds.) ITS 1992. LNCS, vol. 608, pp. 76–83. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-55606-0_11
Hu, M., Winikoff, M., Cranefield, S.: A process for novice programming using goals and plans. In: Proceedings of the Fifteenth Australasian Computing Education Conference, pp. 3–12 (2013)
Koedinger, K.R., Aleven, V.: Exploring the assistance dilemma in experiments with cognitive tutors. Educ. Psychol. Rev. 19(3), 239–264 (2007). https://doi.org/10.1007/s10648-007-9049-0
Koike, K., Mogi, T., Tomoto, T., Horiguchi, T., Hirashima, T.: Compogram: development and evaluation of ITS for organizing programming-knowledge by visualizing behavior. In: Stephanidis, C., et al. (eds.) HCII 2020. LNCS, vol. 12427, pp. 151–162. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60152-2_12
Koike, K., Tomoto, T., Hirashima, T.: Proposal of a stepwise support for structural understanding in programming. In: ICCE 2017–25th International Conference on Computers in Education, Workshop Proceedings, pp. 471–481, December 2017
Koike, K., Tomoto, T., Horiguchi, T., Hirashima, T.: Proposal of the expandable modular statements method for structural understanding of programming, and development and evaluation of a learning support system. Trans. Jpn. Soc. Inf. Syst. Educ. 36(3), 190–202 (2019). https://doi.org/10.14926/jsise.36.190. (in Japanese)
Koike, K., Tomoto, T., Horiguchi, T., Hirashima, T.: Supporting knowledge organization for reuse in programming: proposal of a system based on function–behavior–structure models. In: ICCE 2019–27th International Conference on Computers in Education, Workshop Proceedings, vol. 2, pp. 388–398, December 2019
Koike, K., Tomoto, T., Horiguchi, T., Hirashima, T.: Intelligent learning support for reusability oriented knowledge organization in programming learning. Trans. Jpn. Soc. Artif. Intell. 35(5), C-J82–1-17 (2020). https://doi.org/10.1527/tjsai.35-5_C-J82. (in Japanese)
Nwana, H.S.: Intelligent tutoring systems: an overview. Artif. Intell. Rev. 4(4), 251–277 (1990)
Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann Publishers Inc., San Francisco (1987)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Numbers JP18K11586, JP19H04227, and JP17H01839.
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Koike, K., Fujishima, Y., Tomoto, T., Horiguchi, T., Hirashima, T. (2021). Learner Model for Adaptive Scaffolding in Intelligent Tutoring Systems for Organizing Programming Knowledge. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information-Rich and Intelligent Environments. HCII 2021. Lecture Notes in Computer Science(), vol 12766. Springer, Cham. https://doi.org/10.1007/978-3-030-78361-7_6
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