Human-Computer Interaction

GAMBIT - Gamified Application based on a Mixed-reality Boardgame as Interactive Tutorial



With an estimated number of 600 million players, chess is the most famous board game existing. It grants a positive impact on analysis, logic and problem solving (Fergusson, 1994, 1995). A Venezuelan project called Learning to Think Project from 1979-1983 reached a general conclusion that chess, methodologically taught, is a incentive system sufficient to accelerate the increase of IQ in elementary age children of both sexes at all socio-economic levels (Fergusson,1994,1995; Linder,1990; Tudela,1984) In Canada a system called Challenging Mathematics, which uses chess to teach logic and problem solving from grades 2 to 7. By using this system, the average problem-solving score of pupils increases from 62% to 81% (Liptrap, 1998). These benefits led nearly 30 countries (e.g. Venezuela, Iceland, Russia, etc. ) to invent chess as a subject in all public schools (Liptrap, 1998). And even in Germany, chess is involved as a regular subject.

But despite these positive facts about chess, the game itself is tainted with negative stereotypes and prejudices which leads to the fact, that many people are not interested or not motivated to play chess. (Wyde, 2014)


The goal of this bachelor thesis is to create a mixed reality application that motivates the users to learn chess using dedicated gamification methods. To refute these stereotypes and prejudice, this application should show the users that chess is anything but boring. A interactive surface will be used to substitute the chessboard. This will provide the advantage, that some key events, like rules or moving possibilities can be illustrated more understandable than with a normal chessboard. Using a digital board allows much more and different feedback then when using a physical board (Bakker, S. et al., 2007), because an additional multimediale feedback is given. Furthermore the multitouch table allows to invent novel and entertaining interactions with the chess system trough the novel possibilities by mixing Virtual Reality and Physical content (Leitner, J. et al., 2008). Using a multitouch table for hybrid board games can increase the satisfaction of users (Giebler-Schubert, A. et al., 2013) and may also boost the motivation. A major benefit is also, that the automatic detection of the human players’ actions is facilitated (Fischbach, M. et al., 2018). This recognition is paramount to reason about the game’s context in order to provide feedback about the users’ action in time and thus a training effect is granted. To substitute the role of a trainer, a social robot can also be used to convey the rules and tasks. The implemented prototype will be evaluated in terms of cognitive workload, game experience and learning success . Through the extrinsic motivational factors of gamification like Badges or Stars to collect, the user should get more motivated to solve given problems and to progress in the application and associated with this, a bigger motivation to learn the rules (Frith, 2012; McDaniel, R. et al., 2012).


Implementing a Chess tutorial using a multitouch table

The tutorial should convey the basic movement abilities of every single chess piece, basic ways to checkmate the opponent with a certain amount of pieces and some easy tactical tasks to consolidate the learned abilities. To motivate the user to reach the long term goal of learning the rules and basics of chess, badges, secret achievements and a own ELO-Rating gets invented and the progress also gets shown in a ranking. Therefore the application has to be implemented responsive to the actions of the users and also have to take the guidance, to make sure, the user has understood all given input. The Software will be designed as a game made for multitouch tables, where the users use the with markers equipped chess pieces to fulfill their tasks or use the touch surface to navigate through the application. The application will be created with Unreal Engine.

Comparing the implemented Prototype to common chess learning methods

To compare the implemented Software to common chess learning possibilities a normal chess board with the rules and tasks printed on paper is used. There will be two groups of participants. Group one uses the implemented Software to learn the basics. Group two uses the non technical alternative. In the end, both groups gets tasks to measure the acquired skills of the participants. Questionnaires to measure the cognitive load and the game experience will e used (e.g. NASA-TLX, GEQ, ..)


Because of as well the benefits of gamification as the advantage of feedback in realtime, the implemented software is more efficient than common chess learning methods. To test this hypothesis, both groups of participants gets tasks at the end of their learning session, which they have to fulfill.



To teach users how to play chess, it’s important to implement the basic tutorials at first. Therefore basic tasks get implemented, which explain the basic rules like movement abilities of every chess piece. The Software should recognize if the user is able to move every piece correctly. The user is able to get achievements like stars for fulfilling the tasks correctly in dependence of his performance. If the first levels are done correctly and enough Stars are collected, the user is able to participate in the second stage of the software. Therefore the user gets taught how to win a game of chess and practice the basic ways to checkmate the opponent with given pieces. If the user has passed all these exercises, he gets to the last Stage, in which he has to solve basic chess puzzles, which contains tasks like checkmate the opponent in one or two moves.

Supervising the user

The supervision of the user should be adopted by two different ways. The first way is to give all informations about rules and tasks by displaying text on the table. The second way is to let a social robot takeover the role of the trainer.

Interacting with the System

The application should substitute a normal chessboard, therefore the chess pieces are physical 3D pieces equipped with markers on the bottom of each piece. The user should interact with the system by moving the chess pieces like on a normal chessboard. The pieces of the opponent should either be displayed in 2D or should get moved by the user in 3D. Therefore the pieces get equipped with markers on the bottom of the piece, which gets recognized by the table. The user should be able to navigate trough the system by using the touch surface of the table to interact with its menu. There, the user is able to see the rankings or to start a training session. The user can use the main menu to navigate through the system. At the beginning of the tutorial, the user gets to know each chess piece and their ability to move. The abilities get shortly explained to the user at beginning. After that, the user has to fulfill tasks (e.g. collecting all stars distributed on the board) to progress in the tutorial. Therefore he has to use the required chess piece and place it on the board. The user gets rewarded with stars. If the User fulfill the tasks perfect, he will get all stars. if the user has enough stars, he will progress in the tutorial. Otherwise he has to practice until he has enough stars to progress.


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Ferguson, R. (1994). Teaching the Fourth ‘R’(Reflective Reasoning) through Chess (Doctoral dissertation, doctoral dissertation).

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Martin Fischbach, Michael Brandt, Chris Zimmerer, Jean-Luc Lugrin, Marc Erich Latoschik, Birgit Lugrin (2018). Follow the White Robot - A Role-Playing Game with a Robot Game Master, In Proceedings of the 17th Conference on Autonomous Agents and MultiAgent Systems. ACM, to be published

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McDaniel, R., Lindgren, R., & Friskics, J. (2012, October). Using badges for shaping interactions in online learning environments. In Professional Communication Conference (IPCC), 2012 IEEE International (pp. 1-4). IEEE.

Contact Persons at the University Würzburg

Dr. Martin Fischbach (Primary Contact Person)
Mensch-Computer-Interaktion, Universität Würzburg

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