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Player Types and Player Behaviors: Analyzing Correlations in an On-the-field Gamified System

Published: 23 October 2018 Publication History

Abstract

When the promotion of a positive behavioral change is the goal, most often persuasion techniques are used. To maximize the effectiveness of these techniques designers need to tailor the employed persuasion strategies to each individual. Many studies faced the problem of modeling players' profile by designing taxonomies. However, none of them verified if this approach works in practice. In this paper we investigate whether using a well-known player types categorization based on a questionnaire is an effective mean to represent the way players actually behave in an on-the-field gamified system.

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  • (2024)Forming the Depth-Annex-Motion-Placement (DAMP) Conceptual Model for Gamified LearningIntegrating Technology in Problem-Solving Educational Practices10.4018/979-8-3693-6745-2.ch006(117-140)Online publication date: 25-Oct-2024
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  • (2023)Using Hobbies as Proxy for Gamification Player Types2023 46th MIPRO ICT and Electronics Convention (MIPRO)10.23919/MIPRO57284.2023.10159940(62-66)Online publication date: 22-May-2023
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  1. Player Types and Player Behaviors: Analyzing Correlations in an On-the-field Gamified System

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        cover image ACM Conferences
        CHI PLAY '18 Extended Abstracts: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts
        October 2018
        725 pages
        ISBN:9781450359689
        DOI:10.1145/3270316
        • General Chairs:
        • Florian 'Floyd' Mueller,
        • Daniel Johnson,
        • Ben Schouten,
        • Program Chairs:
        • Phoebe O. Toups Dugas,
        • Peta Wyeth
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Publication History

        Published: 23 October 2018

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        Author Tags

        1. gamification
        2. model evaluation
        3. player behaviors
        4. player modeling

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        CHI PLAY '18
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        CHI PLAY '18 Extended Abstracts Paper Acceptance Rate 43 of 123 submissions, 35%;
        Overall Acceptance Rate 421 of 1,386 submissions, 30%

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        Cited By

        View all
        • (2024)Forming the Depth-Annex-Motion-Placement (DAMP) Conceptual Model for Gamified LearningIntegrating Technology in Problem-Solving Educational Practices10.4018/979-8-3693-6745-2.ch006(117-140)Online publication date: 25-Oct-2024
        • (2024)The role of video games in enhancing managers' strategic thinking and cognitive abilities: An experiential surveyEntertainment Computing10.1016/j.entcom.2024.10069450(100694)Online publication date: May-2024
        • (2023)Using Hobbies as Proxy for Gamification Player Types2023 46th MIPRO ICT and Electronics Convention (MIPRO)10.23919/MIPRO57284.2023.10159940(62-66)Online publication date: 22-May-2023
        • (2023)The Dynamics of Students’ Playing Profiles in a Programming Educational Escape RoomProceedings of TEEM 202310.1007/978-981-97-1814-6_2(21-31)Online publication date: 25-Oct-2023
        • (2023)“Draw Fast, Guess Slow”: Characterizing Interactions in Cooperative Partially Observable Settings with Online Pictionary as a Case StudyHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_16(283-303)Online publication date: 25-Aug-2023
        • (2022)ComParE: A User Behavior Centric Framework for Personalized Recommendations in Skill Gaming PlatformsProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493733(186-194)Online publication date: 8-Jan-2022
        • (2022)Player profiles for game‐based applications in engineering educationComputer Applications in Engineering Education10.1002/cae.2257631:1(154-175)Online publication date: 10-Oct-2022
        • (2021)Exploiting limited players’ behavioral data to predict churn in gamificationElectronic Commerce Research and Applications10.1016/j.elerap.2021.10105747(101057)Online publication date: May-2021
        • (2020)Reading Between the Lines – Towards an Algorithm Exploiting In-game Behaviors to Learn Preferences in Gameful SystemsProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3403016(1-12)Online publication date: 15-Sep-2020
        • (2020)Automatic generation and recommendation of personalized challenges for gamificationUser Modeling and User-Adapted Interaction10.1007/s11257-019-09255-2Online publication date: 24-May-2020
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