Leveraging Cluster Analysis to Understand Educational Game Player Experiences and Support Design

• URL: http://arxiv.org/abs/2210.09911v1
• Date: Tue, 18 Oct 2022 14:51:15 GMT
• Title: Leveraging Cluster Analysis to Understand Educational Game Player Experiences and Support Design
• Authors: Luke Swanson, David Gagnon, Jennifer Scianna, John McCloskey, Nicholas Spevacek, Stefan Slater, Erik Harpstead
• Abstract summary: The ability for an educational game designer to understand their audience's play styles is an essential tool for improving their game's design. We present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio.
• Score: 3.07869141026886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio. We utilize the method to analyze a real-time strategy game, processing game telemetry data to determine categories of players based on their in-game actions, the feedback they received, and their progress through the game. An interpretive analysis of these clusters results in actionable insights for the game's designers.

Related papers

• Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment [4.398130586098371]
  evaluating the effectiveness of tutorials usually requires multiple iterations with testers who have no prior knowledge of the game. Recent Vision-Language Models (VLMs) have demonstrated significant capabilities in understanding and interpreting visual content. We propose an automated game-testing solution to evaluate the quality of game tutorials.
  arXiv  Detail & Related papers  (2024-08-15T19:46:21Z)
• The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer [2.778721019132512]
  This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and muses'' for game design. Our goal is to determine whether AI-assistance can improve, hinder, or provide an alternative quality to games when compared to the creative intents implemented by human designers.
  arXiv  Detail & Related papers  (2024-03-04T20:14:38Z)
• Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games [58.720142291102135]
  We describe an effort to add an experimental reinforcement learning system to an existing automated game testing solution based on scripted bots. We show a use-case of leveraging reinforcement learning in game production and cover some of the largest time sinks anyone who wants to make the same journey for their game may encounter. We propose a few research directions that we believe will be valuable and necessary for making machine learning, and especially reinforcement learning, an effective tool in game production.
  arXiv  Detail & Related papers  (2023-07-19T18:19:23Z)
• Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics [4.1692797498685685]
  Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. This paper extracts information from game design (i.e. patch notes) and uses clustering techniques to propose a new form of character representation.
  arXiv  Detail & Related papers  (2023-05-29T11:05:20Z)
• A Pilot Study on Teacher-Facing Real-Time Classroom Game Dashboards [0.0]
  We present the results of a participatory design process for a teacher-facing, real-time game data dashboard. We analyze post-gameplay survey and interview data to understand teachers' experiences with the tool.
  arXiv  Detail & Related papers  (2022-10-17T20:44:07Z)
• CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories [65.35714948506032]
  The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to explore. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers.
  arXiv  Detail & Related papers  (2022-02-21T09:08:33Z)
• SuSketch: Surrogate Models of Gameplay as a Design Assistant [1.9222706856050082]
  This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.
  arXiv  Detail & Related papers  (2021-03-22T11:05:27Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games [64.11746320061965]
  We study reinforcement learning for text-based games, which are interactive simulations in the context of natural language. We aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
  arXiv  Detail & Related papers  (2020-10-22T12:40:22Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)
• Exploration Based Language Learning for Text-Based Games [72.30525050367216]
  This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents.
  arXiv  Detail & Related papers  (2020-01-24T03:03:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.