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Automatic generation and recommendation of personalized challenges for gamification

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Abstract

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster end users’ engagement and to induce a change in their behavior. Despite its impact potential, well-known limitations concern retaining players and sustaining over time the newly adopted behavior. This problem can be sourced from two common errors: basic game elements that are considered at design time and a one-size-fits-all strategy in generating game content. The former issue refers to the fact that most gamified applications focus only on the superficial layer of game design elements, such as points, badges and leaderboards, and do not exploit the full potential of games in terms of engagement and motivation; the latter relates to a lack of personalization, since the game content proposed to players does not take into consideration their specific abilities, skills and preferences. Taken together, these issues often lead to players’ boredom or frustration. The game element of challenges, which propose a demanding but achievable goal and rewarding completion, has empirically proved effective to keep players’ interest alive and to sustain their engagement over time. However, they require a significant effort from game designers, who must periodically conceive new challenges, align goals with the objectives of the gamification campaign, balance those goals with rewards and define assignment criteria to the player population. Our hypothesis is that we can overcome these limitations by automatically generating challenges, which are personalized to each individual player throughout the game. To this end, we have designed and implemented a fully automated system for the dynamic generation and recommendation of challenges, which are personalized and contextualized based on the preferences, history, game status and performances of each player. The proposed approach is generic and can be applied in different gamification application contexts. In this paper, we present its implementation within a large-scale and long-running open-field experiment promoting sustainable urban mobility that lasted 12 weeks and involved more than 400 active players. A comparative evaluation is performed, considering challenges that are generated and assigned fully automatically through our system versus analogous challenges developed and assigned by human game designers. The evaluation covers the acceptance of challenges by players, the impact induced on players’ behavior, as well as the efficiency in terms of rewarding cost. The evaluation results are very encouraging and suggest that procedural content generation applied to the customization of challenges has a great potential to enhance the performance of gamification applications and augment their engagement and persuasive power.

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Notes

  1. See https://github.com/smartcommunitylab/smartcampus.gamification.

  2. https://www.smartcommunitylab.it/apps/viaggia-trento-e-rovereto-playgo/.

  3. This may configure, at the very beginning of the game, a sort of cold start problem; to bypass that, one can have an initial phase of the game without injection of challenges (2 weeks, in our game), in which sufficient game data are collected; alternatively, one can leverage data from previous instantiations of the same game, or any other suitable statistics, to establish an initial baseline distribution.

  4. Non-RS challenges were managed by Trento Play&Go game administration team, who were involved in the design and day-to-day management also of two previous editions of the game, and had become very knowledgeable of the game mechanics and dynamics, as well as of the urban mobility gamification domain.

  5. We used TOST function which is available in “TOSTER” package in r: https://cran.r-project.org/web/packages/equivalence/equivalence.pdf.

  6. For those estimates, we took advantage of a function made available to the R statistical suite by Jurasinski and Günther (2014).

  7. In order to avoid confusion with AUC, which usually indicates the area under the curve in a receiver operating characteristic (ROC) plot.

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  1. Fondazione Bruno Kessler (FBK), Trento, Italy

    Reza Khoshkangini, Giuseppe Valetto, Annapaola Marconi & Marco Pistore

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Khoshkangini, R., Valetto, G., Marconi, A. et al. Automatic generation and recommendation of personalized challenges for gamification. User Model User-Adap Inter 31, 1–34 (2021). https://doi.org/10.1007/s11257-019-09255-2

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