Learning Analytics for a Puzzle Game to Discover the Puzzle-Solving Tactics of Players

  • Mehrnoosh VahdatEmail author
  • Maira B. Carvalho
  • Mathias Funk
  • Matthias Rauterberg
  • Jun Hu
  • Davide Anguita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9891)


Games can be used as effective learning tools, proved to enhance players’ performance in a wide variety of cognitive tasks. In this context, Learning Analytics (LA) can be used to improve game quality and to support the achievement of learning goals. In this paper, we investigate the use of LA in digital puzzle games, which are commonly used for educational purposes. We describe our approach to explore the way players learn game skills and solve problems in an open-source puzzle game called Lix. We performed an initial study with 15 participants, in which we applied Process Mining and cluster analysis in a three-step analysis approach. This approach can be used as a basis for recommending interventions so as to facilitate the puzzle-solving process of players.


Learning Analytics Educational Data Mining Serious games Puzzle games Technology enhanced learning Cluster analysis Process mining 



This work was supported in part by the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments, funded by the EACEA Agency of the European Commission under EMJD ICE FPA n 2010-0012.


  1. 1.
    Erhel, S., Jamet, E.: Digital game-based learning: impact of instructions and feedback on motivation and learning effectiveness. Comput. Educ. 67, 156–167 (2013)CrossRefGoogle Scholar
  2. 2.
    Bohannon, J.: Game-miners grapple with massive data. Science 330(6000), 30–31 (2010)Google Scholar
  3. 3.
    Serrano-Laguna, Á., Torrente, J., Moreno-Ger, P., Fernández-Manjón, B.: Application of learning analytics in educational videogames. Entertainment Comput. 5(4), 313–322 (2014)CrossRefGoogle Scholar
  4. 4.
    Siemens, G., Baker, R.S.: Learning analytics and educational data mining: towards communication and collaboration. In: 2nd International Conference on Learning Analytics and Knowledge, pp. 252–254 (2012)Google Scholar
  5. 5.
    Trcka, N., Pechenizkiy, M., Van Der Aalst, W.: Process Mining from Educational Data. Chapman & Hall/CRC, London (2010)CrossRefGoogle Scholar
  6. 6.
    Vahdat, M., Oneto, L., Anguita, D., Funk, M., Rauterberg, M.: A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In: Design for Teaching and Learning in a Networked World, pp. 352–366 (2015)Google Scholar
  7. 7.
    Bauckhage, C., Drachen, A., Sifa, R.: Clustering game behavior data. IEEE Trans. Comput. Intell. AI Games 7(3), 266–278 (2015)CrossRefGoogle Scholar
  8. 8.
    Liu, E.Z.F., Lin, C.H.: Developing evaluative indicators for educational computer games. Br. J. Educ. Technol. 40(1), 174–178 (2009)CrossRefGoogle Scholar
  9. 9.
    Becker, K.: How are games educational? Learning theories embodied in games. In: DiGRA: Changing Views - Worlds in Play (2005)Google Scholar
  10. 10.
    Naarmann, S.: Lix (2011).
  11. 11.
    Carvalho, M.B., Bellotti, F., Hu, J., Baalsrud Hauge, J., Berta, R., Gloria, A.D., Rauterberg, M.: Towards a service oriented architecture framework for educational serious games. In: IEEE 15th International Conference on Advanced Learning Technologies (ICALT), pp. 147–151 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mehrnoosh Vahdat
    • 1
    • 3
    Email author
  • Maira B. Carvalho
    • 1
    • 3
  • Mathias Funk
    • 1
  • Matthias Rauterberg
    • 1
  • Jun Hu
    • 1
  • Davide Anguita
    • 2
  1. 1.Department of Industrial DesignEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.DIBRIS - Università degli Studi di GenovaGenoaItaly
  3. 3.DITEN - Università degli Studi di GenovaGenoaItaly

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