Adaptation, or ability and willingness to consider an alternative approach, is a critical component of learning through reflection, especially in educational games, where there are often multiple avenues to success. As a domain, educational games have shown increased interest in using retrospective visualizations to promote and support reflection. Such visualizations, which can facilitate comparison with peer data, may also have an impact on adaptation in educational games. This has, however, not been empirically examined within the domain. In this work, we examine how comparison with other players’ data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming. Our results indicate that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways. We discuss what these results mean for future use of retrospective visualizations in educational games and present opportunities for future work.

/pdf/what-else-can-i-do-examining-the-impact-of-community-data-on-2chfjlm8.pdf

"What else can I do?" Examining the Impact of Community Data on Adaptation and Quality of Reflection in an Educational Game

Player-facing, retrospective gameplay visualizations help players track progress and learn from others. Visualizations of a user’s step by step process, may be able to advance retrospective visualization. However, we currently do not know how players make meaning from process visualizations of game data. In this work, we take a first step towards addressing this gap by examining how players make meaning from process visualizations of other players’ gameplay. We identify two interpretation methods comprised of six techniques and discuss what these results mean for future use of player-facing process visualizations. 

https://pure.itu.dk/ws/files/92565388/Towards_an_Understanding_of_How_Players_Make_Meaning_from_Post_Play_Process_Visualizations.pdf

Towards an Understanding of How Players Make Meaning from Post-Play Process Visualizations

Using provenance and replay for qualitative analysis of gameplay sessions

The usage of game data to analyze relevant information and possibilities about the game has been widely explored by academia and industry. Hence, numerous techniques exist for collecting data with distinct objectives. Among the strategies employed for analyzing gameplay sessions, the collection and analysis of provenance data emerge as prominent. In this paper, we improve provenance approaches through a Prov-Replay conceptual framework. Our framework uses provenance data providing an interactive provenance graph visualization synced with a replay to help to understand the context of gameplay events. To validate Prov-Replay, we applied this framework in an under-development commercial game, collecting real game session data and performing a test with the game developers. 

Prov-Replay: A Qualitative Analysis Framework for Gameplay Sessions Using Provenance and Replay

Data visualization is a strategy for seeing previously unknown data that will aid humanity in discovering essential information. There are various ways to represent datasets, but data visualization is the most effective because of its ability in seeing unseen information, which is a valuable tool for assisting in understanding and keeping interactive games enjoyable. The representation of the skeleton by the game is necessary when the skeleton is used to analyze the visualization patterns, and therefore the visualization of the dataset gives effective results. In this paper, we will present a new method that will effectively analyze the visualization patterns and present an interactive movie with interactive movements compared to the movements of the original visualization patterns in the dataset, and visualize movie datasets, by quantities of movie skeleton, which can be used in-game. This new method is quantity skeleton has a good level of speed, efficiency, and accuracy. In the conclusion of this study, we will present all of the outcomes of the comparison among our proposed method and other well-known ways, proving the effectiveness and efficiency of the proposed through the use of well-known comparison measures and the presentation of the results.

Design Interactive Game by Using Visualization: The Movie Data Set

Currently, there is no formal taxonomy for the activities that users engage in when interacting with and making meaning from spatio-temporal game data visualizations. As data visualization, especially spatio-temporal visualization, becomes more popular for game data analytics, it becomes increasingly crucial that we develop a formal understanding of how users, especially players, interact with and extract meaning from game data using these systems. However, existing taxonomies developed for InfoVis are not directly applicable due to domain differences and a lack of consensus within the literature. This paper presents the beginnings of a taxonomy for user interaction with spatio-temporal data specific to the domain of games, developed from the results of a qualitative user study (n=7) in which experienced players were tasked with using a spatio-temporal visualization system to explore and understand telemetry data from Defense of the Ancients 2 (DotA 2). The taxonomy includes seven activities organized into three categories: Data Interaction, Sense Making, and Validation. We discuss the implications of these activities on design and future research.

https://dl.acm.org/doi/pdf/10.1145/3474687

"What Happened Here!?" A Taxonomy for User Interaction with Spatio-Temporal Game Data Visualization

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Nikitha Preetham

Author Tools

Chat about Author

Papers

"What Happened Here!?" A Taxonomy for User Interaction with Spatio-Temporal Game Data Visualization