Clustering Game Behavior Data

Bauckhage, Christian; Drachen, Anders; Sifa, Rafet

doi:10.1109/tciaig.2014.2376982

Cited by 85 publications

(65 citation statements)

References 28 publications

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“…Bauckhage and colleagues also discussed the challenges stemming from a similar research question with respect to clustering game behavior data (Bauckhage et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Polyak¹,

Davier²,

Peterschmidt³

2017

Front. Psychol.

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This paper describes a psychometrically-based approach to the measurement of collaborative problem solving skills, by mining and classifying behavioral data both in real-time and in post-game analyses. The data were collected from a sample of middle school children who interacted with a game-like, online simulation of collaborative problem solving tasks. In this simulation, a user is required to collaborate with a virtual agent to solve a series of tasks within a first-person maze environment. The tasks were developed following the psychometric principles of Evidence Centered Design (ECD) and are aligned with the Holistic Framework developed by ACT. The analyses presented in this paper are an application of an emerging discipline called computational psychometrics which is growing out of traditional psychometrics and incorporates techniques from educational data mining, machine learning and other computer/cognitive science fields. In the real-time analysis, our aim was to start with limited knowledge of skill mastery, and then demonstrate a form of continuous Bayesian evidence tracing that updates sub-skill level probabilities as new conversation flow event evidence is presented. This is performed using Bayes' rule and conversation item conditional probability tables. The items are polytomous and each response option has been tagged with a skill at a performance level. In our post-game analysis, our goal was to discover unique gameplay profiles by performing a cluster analysis of user's sub-skill performance scores based on their patterns of selected dialog responses.

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“…Bauckhage and colleagues also discussed the challenges stemming from a similar research question with respect to clustering game behavior data (Bauckhage et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Polyak¹,

Davier²,

Peterschmidt³

2017

Front. Psychol.

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“…The first paper to specifically utilize behavioral profiling in commercial game titles was Drachen et al [10] who worked with Self-Organizing Networks. The majority of previous work is focused on employing cluster analysis or segmentation methods, but comparative analyses were provided by Bauckhage et al [11] and Drachen et al [12]. Drachen et al [9] developed behavioral profiles for a set of 10,000 players of Destiny, focusing on discovering the best performing cluster models for the task of handling high-dimensional behavioral clustering.…”

Section: Related Workmentioning

confidence: 99%

Controlling the crucible

Sifa

Pawlakos²,

Zhai³

et al. 2018

Proceedings of the Australasian Computer Science Week Multiconference

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“…Results show that the number of moderators, their connectedness, and the group size correlate with group activity. Categories of player groups developed via archetypal analysis [19], [20], [21] across a series of group features show the presence of four types of player groups with varying degrees of social cohesion, moderator activity, activity levels, character level, etc. Group activity is also presented as a function of weekdays to investigate when the100.io players schedule activities, across groups comprised of casual or serious players.…”

Section: Introductionmentioning

confidence: 99%

Inside the Group: Investigating Social Structures in Player Groups and Their Influence on Activity

et al. 2019

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Social features, matchmaking, and grouping functions are key elements of online multi-player experiences. Understanding how social connections form in and around games and their relationship to in-game activity offers insights for building and maintaining player bases and for improving engagement and retention. This paper presents an analysis of the groups formed by users of the the100.io -a social matchmaking website for different commercial titles, including Destiny on which we focus in this paper. Groups formed on the100.io can be described across a range of social network related metrics. Also, the social network formed within a group is evaluated in combination with userprovided demographic and preference data. Archetypal analysis is used to classify groups into archetypes and a correlation analysis is presented covering the effect of group characteristics on in-game-activity. Finally, weekly activity profiles are described.Our results indicate that group size as well as the number of moderators within a group and their connectedness to other team members influences a group's activity. We also identified four prototypical types of groups with different characteristics concerning composition, social cohesion, and activity.

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Clustering Game Behavior Data

Cited by 85 publications

References 28 publications

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Controlling the crucible

Inside the Group: Investigating Social Structures in Player Groups and Their Influence on Activity

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