Analyzing gameplay data to inform feedback loops in The Radix Endeavor

Meng Tzu Cheng, Louisa Rosenheck, Chen Yen Lin, Eric Klopfer

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The purpose of this study is to explore some of the ways in which gameplay data can be analyzed to yield results that feed back into the learning ecosystem. There is a solid research base showing the positive impact that games can have on learning, and useful methods in educational data mining. However, there is still much to be explored in terms of what the results of gameplay data analysis can tell stakeholders and how those results can be used to improve learning. As one step toward addressing this, researchers in this study collected back-end data from high school students as they played an MMOG called The Radix Endeavor. Data from a specific genetics quest in the game were analyzed by using data mining techniques including the classification tree method. These techniques were used to examine the relationship between tool use and quest completion, how use of certain tools may influence content-related game choices, and the multiple pathways available to players in the game. The study identified that in this quest use of the trait examiner tool was most likely to lead to success, though a greater number of trait decoder tool uses could also lead to success, perhaps because in those cases players solving problems about genetic traits at an earlier point. These results also demonstrate the multiple strategies available to Radix players that provide different pathways to quest completion. Given these methods of analysis and quest-specific results, the study applies the findings to suggest ways to validate and refine the game design, and to provide useful feedback to students and teachers. The study suggests ways that analysis of gameplay data can be part of a feedback loop to improve a digital learning experience.

Original languageEnglish
Pages (from-to)60-73
Number of pages14
JournalComputers and Education
Volume111
DOIs
Publication statusPublished - 2017 Aug 1

Fingerprint

Feedback
Data mining
Students
learning
Ecosystems
examiner
data analysis
student
stakeholder
teacher
school
experience
Genetics

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Education

Cite this

Cheng, Meng Tzu ; Rosenheck, Louisa ; Lin, Chen Yen ; Klopfer, Eric. / Analyzing gameplay data to inform feedback loops in The Radix Endeavor. In: Computers and Education. 2017 ; Vol. 111. pp. 60-73.
@article{c2259ab331ef4ad588a7fde01a0d837e,
title = "Analyzing gameplay data to inform feedback loops in The Radix Endeavor",
abstract = "The purpose of this study is to explore some of the ways in which gameplay data can be analyzed to yield results that feed back into the learning ecosystem. There is a solid research base showing the positive impact that games can have on learning, and useful methods in educational data mining. However, there is still much to be explored in terms of what the results of gameplay data analysis can tell stakeholders and how those results can be used to improve learning. As one step toward addressing this, researchers in this study collected back-end data from high school students as they played an MMOG called The Radix Endeavor. Data from a specific genetics quest in the game were analyzed by using data mining techniques including the classification tree method. These techniques were used to examine the relationship between tool use and quest completion, how use of certain tools may influence content-related game choices, and the multiple pathways available to players in the game. The study identified that in this quest use of the trait examiner tool was most likely to lead to success, though a greater number of trait decoder tool uses could also lead to success, perhaps because in those cases players solving problems about genetic traits at an earlier point. These results also demonstrate the multiple strategies available to Radix players that provide different pathways to quest completion. Given these methods of analysis and quest-specific results, the study applies the findings to suggest ways to validate and refine the game design, and to provide useful feedback to students and teachers. The study suggests ways that analysis of gameplay data can be part of a feedback loop to improve a digital learning experience.",
author = "Cheng, {Meng Tzu} and Louisa Rosenheck and Lin, {Chen Yen} and Eric Klopfer",
year = "2017",
month = "8",
day = "1",
doi = "10.1016/j.compedu.2017.03.015",
language = "English",
volume = "111",
pages = "60--73",
journal = "Computers and Education",
issn = "0360-1315",
publisher = "Elsevier Limited",

}

Analyzing gameplay data to inform feedback loops in The Radix Endeavor. / Cheng, Meng Tzu; Rosenheck, Louisa; Lin, Chen Yen; Klopfer, Eric.

In: Computers and Education, Vol. 111, 01.08.2017, p. 60-73.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Analyzing gameplay data to inform feedback loops in The Radix Endeavor

AU - Cheng, Meng Tzu

AU - Rosenheck, Louisa

AU - Lin, Chen Yen

AU - Klopfer, Eric

PY - 2017/8/1

Y1 - 2017/8/1

N2 - The purpose of this study is to explore some of the ways in which gameplay data can be analyzed to yield results that feed back into the learning ecosystem. There is a solid research base showing the positive impact that games can have on learning, and useful methods in educational data mining. However, there is still much to be explored in terms of what the results of gameplay data analysis can tell stakeholders and how those results can be used to improve learning. As one step toward addressing this, researchers in this study collected back-end data from high school students as they played an MMOG called The Radix Endeavor. Data from a specific genetics quest in the game were analyzed by using data mining techniques including the classification tree method. These techniques were used to examine the relationship between tool use and quest completion, how use of certain tools may influence content-related game choices, and the multiple pathways available to players in the game. The study identified that in this quest use of the trait examiner tool was most likely to lead to success, though a greater number of trait decoder tool uses could also lead to success, perhaps because in those cases players solving problems about genetic traits at an earlier point. These results also demonstrate the multiple strategies available to Radix players that provide different pathways to quest completion. Given these methods of analysis and quest-specific results, the study applies the findings to suggest ways to validate and refine the game design, and to provide useful feedback to students and teachers. The study suggests ways that analysis of gameplay data can be part of a feedback loop to improve a digital learning experience.

AB - The purpose of this study is to explore some of the ways in which gameplay data can be analyzed to yield results that feed back into the learning ecosystem. There is a solid research base showing the positive impact that games can have on learning, and useful methods in educational data mining. However, there is still much to be explored in terms of what the results of gameplay data analysis can tell stakeholders and how those results can be used to improve learning. As one step toward addressing this, researchers in this study collected back-end data from high school students as they played an MMOG called The Radix Endeavor. Data from a specific genetics quest in the game were analyzed by using data mining techniques including the classification tree method. These techniques were used to examine the relationship between tool use and quest completion, how use of certain tools may influence content-related game choices, and the multiple pathways available to players in the game. The study identified that in this quest use of the trait examiner tool was most likely to lead to success, though a greater number of trait decoder tool uses could also lead to success, perhaps because in those cases players solving problems about genetic traits at an earlier point. These results also demonstrate the multiple strategies available to Radix players that provide different pathways to quest completion. Given these methods of analysis and quest-specific results, the study applies the findings to suggest ways to validate and refine the game design, and to provide useful feedback to students and teachers. The study suggests ways that analysis of gameplay data can be part of a feedback loop to improve a digital learning experience.

UR - http://www.scopus.com/inward/record.url?scp=85017662061&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017662061&partnerID=8YFLogxK

U2 - 10.1016/j.compedu.2017.03.015

DO - 10.1016/j.compedu.2017.03.015

M3 - Article

AN - SCOPUS:85017662061

VL - 111

SP - 60

EP - 73

JO - Computers and Education

JF - Computers and Education

SN - 0360-1315

ER -