Abstract
The topic of this contribution is characterization and analysis of assistance systems in order to enable adaptivity, i.e., as personalized adaptive systems. The research question of this article is how to facilitate the modeling efforts in adaptive e-learning assistance systems. Adaptivity here means to personalize the usage experience to the individual needs of the users and their current working context. For that, adaptive systems need usage models and user models. The problem statement is that expert knowledge and recurrent efforts are needed to create and update these types of models. Data driven and graph analytics approaches can help here, in particular when looking at standardized interaction data and models which encode sequences such as interaction paths or learning paths. This article studies how to make use of interaction usage data to create sequence-typed domain and user models for their use in adaptive assistance systems. The main contribution of this work is an innovative concept and implementation framework to dynamically create Ideal Paths Models (IPM) as reference models for adaptive control in adaptive assistance systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gorecky, D., Schmitt, M., Loskyll, M., Zühlke, D.: Human-machine-interaction in the industry 4.0 era. In: 2014 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289–294 (2014). https://doi.org/10.1109/INDIN.2014.6945523
Woolf, B.P.: Building Intelligent Interactive Tutors. Morgan Kaufmann, Burlington (2009)
Shute, V., Zapata-Rivera, D.: Adaptive educational systems. In: Adaptive Technologies for Training and Education, vol. 7, pp. 1–35 (2012). https://doi.org/10.1017/CBO9781139049580.004
Streicher, A., Leidig, S., Roller, W.: Eye-tracking for user attention evaluation in adaptive serious games. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, pp. 583–586. Springer, Leeds (2018). https://doi.org/10.1007/978-3-319-98572-5_50
Dörner, R., Göbel, S., Effelsberg, W., Wiemeyer, J. (eds.): Serious Games - Foundations, Concepts and Practice. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-40612-1
Nakamura, J., Csikszentmihalyi, M.: The concept of flow. In: Csikszentmihalyi, M. (ed.) Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi, pp. 239–263. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-017-9088-8_16
Streicher, A., Heberle, F.: learning progress and learning pathways. In: Fuchs, K., Henning, P.A. (eds.) Computer-Driven Instructional Design with INTUITEL, pp. 37–55. River Publishers Series (2017)
Conati, C., Manske, M.: Evaluating adaptive feedback in an educational computer game. In: Ruttkay, Z., Kipp M., Nijholt A., Vilhjálmsson H.H. (eds) Intelligent Virtual Agents, IVA 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 146–158 (2009). https://doi.org/10.1007/978-3-642-04380-2_18
Kurup, L.D., Joshi, A., Shekhokar, N.: A review on student modeling approaches in ITS. In: Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016 (2016)
Melero, J., El-Kechaï, N., Labat, J.-M.: Comparing two CbKST approaches for adapting learning paths in serious games. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, E. (eds.) Design for Teaching and Learning in a Networked World, pp. 211–224. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-24258-3_16
Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis – a new alternative to knowledge tracing. In: 14th International Conference on Artificial Intelligence in Education 2009 (2009)
Åström, K.J., Wittenmark, B.: Adaptive Control, 2nd edn. Dover Publications, Mineola (2013)
Nguyen, N.T.: Model-Reference Adaptive Control: A Primer. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-56393-0
Streicher, A., Pickl, S.W.: Characterization and Analysis with xAPI based graphs for adaptive interactive learning environments. In: WMSCI, Orlando, FL, USA (2020)
Needham, M., Hodler, A.E.: Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O’Reilly Media, Sebastopol (2019)
Aydinbas, M.: Realizing Cognitive User Models for Adaptive Serious Games (2019)
Acknowledgments
The underlying project to this article is funded by the Federal Office of Bundeswehr Equipment, Information Technology and In-Service Support under promotional references.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Streicher, A., Schönbein, R., Pickl, S. (2021). Graph-Based Modeling for Adaptive Control in Assistance Systems. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-80624-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80623-1
Online ISBN: 978-3-030-80624-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)