Abstract
Serious Games, i.e., games not just for pure entertainment and with characterizing goals, are gaining huge popularity for the purpose of education and training. To further increase the learning outcome of serious games, assistance functionalities like adaptive systems observe the users and try to guide them to achieve their learning objectives. The research question is how to model the user’s behavior, their progress, and how to determine the best adaptation strategies to motivate the users and provide assistance whenever required. Using experience-data in a serious game is one approach to develop and train models for adaptivity. In this paper, we present SeGaAdapt, an adaptive framework that is based on a Hidden Markov Model (HMM) algorithm for providing dynamic user-assistance and learning analytics for a serious game. For the development and training of the HMM, we use activity streams or user interaction data gained from an Experience API (xAPI) tracker. The adaptivity mechanism uses the HMM to analyze the current state of the user (player) in order to predict the best feasible activity for future states. Technical verification of this work-in-progress implementation shows the feasibility of the approach and hints at future research directions.
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
Change history
07 September 2020
The original version of the book was inadvertently published with wrong values in Table 2B and Figure 2 in Chapter 31.
The values of emission probability were corrected in ‘Table 2B: Emission Probability’ and ‘Fig. 2. HMM Probabilities from Table 1 & 2’, by replacing the wrong values of emission probability with the appropriate ones to justify the behaviour of the Hidden Markov Model.
The original version of the book was inadvertently published with a typo in the name of the second author in Chapter 42. In the contribution it read “Sebastian Dannerlein” but correctly it should have read “Sebastian Dennerlein”.
The original version of the book was inadvertently published with a wrong wording of the affiliation of the second author. It read “Technical University of Graz” but correctly it should have read “Graz University of Technology”.
The corrected chapters and the book have been updated with the changes.
References
Streicher, A., Smeddinck, J.D.: Personalized and Adaptive Serious Games. In: Dörner, R., Göbel, S., Kickmeier-Rust, M., Masuch, M., Zweig, K. (eds.) Entertainment Computing and Serious Games. LNCS, vol. 9970, pp. 332–377. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46152-6_14
ADL.net: xAPI Specification 1.0.1. https://github.com/adlnet/xAPI-Spec/blob/a752217060b83a2e15dfab69f8c257cd86a888e6/xAPI.md
Bunian, S., Canossa, A., Colvin, R., El-Nasr, M.S.: Modeling individual differences in game behavior using HMM. In: Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference (2017)
Derbali, L., Ghali, R., Frasson, C.: Assessing motivational strategies in serious games using hidden markov models. In: The Twenty-Sixth International FLAIRS Conference (2013)
Gagniuc, P.A.: Markov Chains: From Theory to Implementation and Experimentation. Wiley, Hoboken (2017)
Rabiner, L., Juang, B.: An introduction to hidden markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)
Musoff, H., Zarchan, P.: Fundamentals of Kalman filtering: a practical approach. American Institute of Aeronautics and Astronautics (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yadav, V., Streicher, A., Prabhune, A. (2020). User Assistance for Serious Games Using Hidden Markov Model. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science(), vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-57717-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57716-2
Online ISBN: 978-3-030-57717-9
eBook Packages: Computer ScienceComputer Science (R0)