Abstract
This paper examines the relationships of various functional elements within a class of instructional technologies called adaptive instructional systems (AISs) which include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. Under Project 2247.1, The Institute for Electrical and Electronic Engineers (IEEE) is developing standards and guidance for the modeling of AIS to characterize what is and is not an AIS. This paper was composed to document recommendations and generate discussion about the four models that have been proposed as core to the concept of AISs: learner models, adaptive models, domain models and interface models.
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References
Sottilare, R., Brawner, K.: Component interaction within the generalized intelligent framework for tutoring (GIFT) as a model for adaptive instructional system standards. In: Proceedings of the 14th International Conference of the Intelligent Tutoring Systems (ITS) Conference, Montreal, Quebec (2018)
Anderson, J.R., Boyle, C.F., Reiser, B.J.: Intelligent tutoring systems. Science 228(4698), 456–462 (1985)
Psotka, J., Sharon, A.M.: Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates (1988). ISBN 978-0-8058-0192-7
Baylor, A.: Beyond butlers: intelligent agents as mentors. J. Educ. Comput. Res. 22(4), 373–382 (2000)
Sottilare, R.A.: A comprehensive review of design goals and emerging solutions for adaptive instructional systems. Technol. Instr. Cognition Learn. 11(1), 5–38 (2018)
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)
Sottilare, R., et al.: Introduction to team tutoring and GIFT. Des. Recomm. Intell. Tutor. Syst. 6, 1–15 (2018)
Sottilare, R.: Considerations in the development of an ontology for a generalized intelligent framework for tutoring. In: Proceedings of the 13M Conference on International Defense and Homeland Security Simulation Workshop, Vienna (2012)
Sottilare, R., Ragusa, C., Hoffman, M., Goldberg, B.: Characterizing an adaptive tutoring learning effect chain for individual and team tutoring. In: Proceedings of the Interservice/Industry Training Simulation and Education Conference, Orlando (2013)
Sottilare, R.A., Burke, C.S., Salas, E., Sinatra, A.M., Johnston, J.H., Gilbert, S.B.: Designing adaptive instruction for teams: a meta-analysis. Int. J. Artif. Intell. Educ. (2017). https://doi.org/10.1007/s40593-017-0146-z
Mitrovic, A., Martin, B., Suraweera, P.: Intelligent tutors for all: the constraint-based approach. IEEE Intell. Syst. 4, 38–45 (2007)
Sottilare, R., Holden, H., Brawner, K., Goldberg, B.: Challenges and emerging concepts in the development of adaptive, computer-based tutoring systems for team training. In: Proceedings of the Interservice/Industry Training Simulation and Education Conference, Orlando (2011)
Johnston, J., et al.: Building Intelligent Tutoring Systems For Teams: What Matters. Emerald Group Publishing, Bingley (2018)
Salas, E.: Team Training Essentials: A Research-Based Guide. Routledge, London (2015)
Gagné, R.M.: Essentials of Learning for Instruction. Dryden Press, Hinsdale (1975)
Krathwohl, D.R.: A revision of bloom’s taxonomy: an overview. Theory Into Pract. 41(4), 212–218 (2002)
Sottilare, R.A., Proctor, M.: Passively classifying student mood and performance within intelligent tutors. J. Educ. Technol. Soc. 15(2), 101–114 (2012)
Aleven, V., Mclaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: a model of help seeking with a cognitive tutor. Int. J. Artif. Intell. Educ. 16(2), 101–128 (2006)
Rus, V., Graesser, A.C.: The question generation shared task and evaluation challenge. In: Proceedings of the University of Memphis. National Science Foundation (2009)
Graesser, A.C.: Conversations with autotutor help students learn. Int. J. Artif. Intell. Educ. 26(1), 124–132 (2016)
Johnson, W.L., Lester, J.C.: Face-to-face interaction with pedagogical agents, twenty years later. Int. J. Artif. Intell. Educ. 26(1), 25–36 (2016)
Nye, B.D., Graesser, A.C., Hu, X.: AutoTutor and family: a review of 17 years of natural language tutoring. Int. J. Artif. Intell. Educ. 24(4), 427–469 (2014)
D’Mello, S.K., Graesser, A., King, B.: Toward spoken human-computer tutorial dialogues. Hum. Comput. Interact. 25(4), 289–323 (2010)
Baker, R.S., D’Mello, S.K., Rodrigo, M.M., Graesser, A.C.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68(4), 223–241 (2010)
D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Model. User Adap. Interact. 20(2), 147–187 (2010)
Acknowledgments
The authors wish to gratefully acknowledge all of the contributions of each and every member of the IEEE AIS Working Group under Project 2247. We especially wish to acknowledge the contributions of the members of the AIS Conceptual Modeling Subgroup led by Anne Knowles: Avron Barr, Jeanine DeFalco, Jim Goodell, Vladimir Goodkovsky, Xiangen Hu, Dale Johnson, Bruce Peoples, Ram Rajendran, Khanh-Phuong (KP) Thai.
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Sottilare, R., Knowles, A., Goodell, J. (2020). Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_13
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