Playing styles based on experiential learning theory

Boyan Bontchev, Dessislava Vassileva, Adelina Aleksieva-Petrova, Milen Petrov

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    In recent years, researchers have reported positive outcomes and effects from applying computer games to the educational process. The preconditions for an effective game-based learning process include the presence of high learning interest and the desire to study hard. Therefore, educational video game design has to tailor gameplay to the style of the playing learner, i.e. to the psycho-cognitive abilities, attitudes, and skills of the individual player, in order to foster the player's motivation and creativity. To achieve this goal, it is necessary to draw a parallel between learning styles and styles of playing video games, and to investigate the correlations between these types of constructs. The article presents a new family of playing styles based on Kolb's experiential learning theory that is appropriate to be used for educational video games. This family is composed of four playing styles: Competitor, Dreamer, Logician, and Strategist, and corresponds to Honey and Mumford's learning styles based also on the theory of experiential learning, namely Activist, Reflector, Theorist, and Pragmatist. To measure the four playing styles, a 40-item questionnaire was designed. In order to verify the consistency, validity, and reliability of that questionnaire as an accurate tool for recognizing the four suggested player styles, a pilot study was conducted. The article reports the results obtained from the study, along with their analysis and applicability.
    Original languageEnglish
    Pages (from-to)319-328
    Number of pages10
    JournalComputers in Human Behavior
    Early online date5 Apr 2018
    Publication statusPublished - Aug 2018



    • Experiential learning
    • GAMES
    • HONEY
    • Playing style
    • Questionnaire
    • Video games

    Cite this

    Bontchev, B., Vassileva, D., Aleksieva-Petrova, A., & Petrov, M. (2018). Playing styles based on experiential learning theory. Computers in Human Behavior, 85, 319-328.