Using Multi-Modal Network Models to Visualize and Understand How Players Learn a Mechanic in a Problem-Solving Game

Zack Carpenter; Yeyu  Wang; David  DeLiema; Panayiota Kendeou; David Williamson  Shaffer

Using Multi-Modal Network Models to Visualize and Understand How Players Learn a Mechanic in a Problem-Solving Game

Zack Carpenter
, Yeyu Wang
, David DeLiema
, Panayiota Kendeou
, David Williamson Shaffer

Educational Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3666 Downloads (Pure)

Abstract

The incipient work in this poster aims to extend work on multi-modal learning
analytics by exploring how blending think-aloud, eye gaze, and log data in network models informs how players learn a game mechanic. Preliminary models demonstrate differing patterns between players who have learned and have not yet learned the mechanic.

Original language	English (US)
Title of host publication	Society for Learning Analytics Research
Pages	99-101
Number of pages	3
State	Published - 2023
Event	The 13th International Learning Analytics and Knowledge Conference - Arlington, Texas Duration: Mar 13 2023 → Mar 17 2023

Conference

Conference	The 13th International Learning Analytics and Knowledge Conference
Period	3/13/23 → 3/17/23

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LAK23_CompanionProceedingsFinal published version, 31.4 MB

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@inproceedings{0e2be5ba2f584fa9b377cb80a7db1e02,

title = "Using Multi-Modal Network Models to Visualize and Understand How Players Learn a Mechanic in a Problem-Solving Game",

abstract = "The incipient work in this poster aims to extend work on multi-modal learning analytics by exploring how blending think-aloud, eye gaze, and log data in network models informs how players learn a game mechanic. Preliminary models demonstrate differing patterns between players who have learned and have not yet learned the mechanic.",

author = "Zack Carpenter and Yeyu Wang and David DeLiema and Panayiota Kendeou and Shaffer, \{David Williamson\}",

year = "2023",

language = "English (US)",

pages = "99--101",

booktitle = "Society for Learning Analytics Research",

note = "The 13th International Learning Analytics and Knowledge Conference ; Conference date: 13-03-2023 Through 17-03-2023",

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TY - GEN

T1 - Using Multi-Modal Network Models to Visualize and Understand How Players Learn a Mechanic in a Problem-Solving Game

AU - Carpenter, Zack

AU - Wang, Yeyu

AU - DeLiema, David

AU - Kendeou, Panayiota

AU - Shaffer, David Williamson

PY - 2023

Y1 - 2023

N2 - The incipient work in this poster aims to extend work on multi-modal learning analytics by exploring how blending think-aloud, eye gaze, and log data in network models informs how players learn a game mechanic. Preliminary models demonstrate differing patterns between players who have learned and have not yet learned the mechanic.

AB - The incipient work in this poster aims to extend work on multi-modal learning analytics by exploring how blending think-aloud, eye gaze, and log data in network models informs how players learn a game mechanic. Preliminary models demonstrate differing patterns between players who have learned and have not yet learned the mechanic.

UR - https://www.solaresearch.org/core/lak23-companion-proceedings/

M3 - Conference contribution

SP - 99

EP - 101

BT - Society for Learning Analytics Research

T2 - The 13th International Learning Analytics and Knowledge Conference

Y2 - 13 March 2023 through 17 March 2023

ER -

Using Multi-Modal Network Models to Visualize and Understand How Players Learn a Mechanic in a Problem-Solving Game

Abstract

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