research-article

Nanogenetic learning analytics: illuminating student learning pathways in an online fraction game

Authors:

Ani Aghababyan,

Stephanie Baker,

Philip Janisiewicz,

Rachel Phillips,

Carmen Petrick SmithAuthors Info & Claims

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Pages 165 - 169

https://doi.org/10.1145/2460296.2460328

Published: 08 April 2013 Publication History

Abstract

A working understanding of fractions is critical to student success in high school and college math. Therefore, an understanding of the learning pathways that lead students to this working understanding is important for educators to provide optimal learning environments for their students. We propose the use of microgenetic analysis techniques including data mining and visualizations to inform our understanding of the process by which students learn fractions in an online game environment. These techniques help identify important variables and classification algorithms to group students by their learning trajectories.

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Cited By

Arztmann MDomínguez Alfaro JHornstra LWong JJeuring JKester L(2025)Game over? Investigating students’ working memory, situational interest, and behavioral patterns as predictors of dropout in an educational gameInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2025.100721(100721)Online publication date: Jan-2025
https://doi.org/10.1016/j.ijcci.2025.100721
Da Silva Neves Lima PDas Almas Silva LFranco Brandão ARibeiro Hagstrom RDe Oliveira Brandão L(2024)How Digital Games are Engaging Our Children Toward STEM Careers?2024 IEEE Frontiers in Education Conference (FIE)10.1109/FIE61694.2024.10892917(1-9)Online publication date: 13-Oct-2024
https://doi.org/10.1109/FIE61694.2024.10892917
Chaudy YConnolly T(2022)Integrating Assessment, Feedback, and Learning Analytics in Educational GamesResearch Anthology on Developments in Gamification and Game-Based Learning10.4018/978-1-6684-3710-0.ch088(1803-1846)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3710-0.ch088
Show More Cited By

Index Terms

Nanogenetic learning analytics: illuminating student learning pathways in an online fraction game
1. Applied computing
  1. Education
2. Social and professional topics
  1. Professional topics
    1. Computing education

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Published In

cover image ACM Conferences

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

April 2013

300 pages

ISBN:9781450317856

DOI:10.1145/2460296

Editors:
Dan Suthers
University of Hawaii, USA
,
Katrien Verbert
Eindhoven University of Technology, The Netherlands
,
Erik Duval
University of Leuven, Belgium
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, Ecuador

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
K.U. Leuven: K.U. Leuven
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

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LAK '13

Sponsor:

SIGWEB
K.U. Leuven
SIGCHI

LAK '13: Third Conference on Learning Analytics and Knowledge

April 8 - 13, 2013

Leuven, Belgium

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LAK '13 Paper Acceptance Rate 16 of 58 submissions, 28%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Arztmann MDomínguez Alfaro JHornstra LWong JJeuring JKester L(2025)Game over? Investigating students’ working memory, situational interest, and behavioral patterns as predictors of dropout in an educational gameInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2025.100721(100721)Online publication date: Jan-2025
https://doi.org/10.1016/j.ijcci.2025.100721
Da Silva Neves Lima PDas Almas Silva LFranco Brandão ARibeiro Hagstrom RDe Oliveira Brandão L(2024)How Digital Games are Engaging Our Children Toward STEM Careers?2024 IEEE Frontiers in Education Conference (FIE)10.1109/FIE61694.2024.10892917(1-9)Online publication date: 13-Oct-2024
https://doi.org/10.1109/FIE61694.2024.10892917
Chaudy YConnolly T(2022)Integrating Assessment, Feedback, and Learning Analytics in Educational GamesResearch Anthology on Developments in Gamification and Game-Based Learning10.4018/978-1-6684-3710-0.ch088(1803-1846)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3710-0.ch088
Torres CJörke MHill EPaulos EDow SMaher MKerne ALatulipe C(2019)Hybrid Microgenetic AnalysisProceedings of the 2019 Conference on Creativity and Cognition10.1145/3325480.3325498(2-14)Online publication date: 13-Jun-2019
https://dl.acm.org/doi/10.1145/3325480.3325498
Chaudy YConnolly T(2018)Integrating Assessment, Feedback, and Learning Analytics in Educational GamesHandbook of Research on E-Assessment in Higher Education10.4018/978-1-5225-5936-8.ch006(127-169)Online publication date: 14-Sep-2018
https://doi.org/10.4018/978-1-5225-5936-8.ch006
Chaudy YConnolly T(2018)Specification and evaluation of an assessment engine for educational games: Empowering educators with an assessment editor and a learning analytics dashboardEntertainment Computing10.1016/j.entcom.2018.07.00327(209-224)Online publication date: Aug-2018
https://doi.org/10.1016/j.entcom.2018.07.003
Tucker S(2016)The Modification of Attributes, Affordances, Abilities, and Distance for Learning Framework and Its Applications to Interactions with Mathematics Virtual ManipulativesInternational Perspectives on Teaching and Learning Mathematics with Virtual Manipulatives10.1007/978-3-319-32718-1_3(41-69)Online publication date: 22-Jun-2016
https://doi.org/10.1007/978-3-319-32718-1_3
Baker RMartin TRossi L(2016)Educational Data Mining and Learning AnalyticsThe Handbook of Cognition and Assessment10.1002/9781118956588.ch16(379-396)Online publication date: 26-Nov-2016
https://doi.org/10.1002/9781118956588.ch16

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