@inproceedings{bb4f6d8db0ca45c2b3b38fad88183d0c,
title = "Learning analytics for a puzzle game to discover the puzzle-solving tactics of players",
abstract = "Games can be used as effective learning tools, proved to enhance players{\textquoteright} 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.",
keywords = "Learning analytics, Educational data mining, Serious games, Technology Enhanced Learning, Puzzle games, Cluster analysis, Process mining",
author = "M. Vahdat and M.B. Carvalho and M. Funk and G.W.M. Rauterberg and J. Hu and D. Anguita",
year = "2016",
doi = "10.1007/978-3-319-45153-4\_89",
language = "English",
isbn = "978-3-319-45152-7",
series = "LNCS",
publisher = "Springer",
pages = "673--677",
editor = "K. Verbert and \{Sharples \}, M. and T. Klobu{\v c}ar",
booktitle = "Adaptive and Adaptable Learning",
address = "Germany",
}