An exploratory analysis of the relationship between interest, knowledge, and engagement of adolescents playing Minecraft in STEM-focused summer camps

Gadbury, Matthew

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https://hdl.handle.net/2142/124239 Copy

Description

Title

An exploratory analysis of the relationship between interest, knowledge, and engagement of adolescents playing Minecraft in STEM-focused summer camps

Author(s)

Gadbury, Matthew

Issue Date

2024-04-11

Director of Research (if dissertation) or Advisor (if thesis)

Lane, H Chad

Doctoral Committee Chair(s)

Lane, H Chad

Committee Member(s)

• Cromley, Jennifer
• Tissenbaum, Mike
• Paquette, Luc

Department of Study

Educational Psychology

Discipline

Educational Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Informal Learning
• Minecraft
• Engagement
• Interest
• Cognition

Language

eng

Abstract

The psychological construct of interest has been demonstrated to be a powerful tool for motivating learners to meaningfully engage with specific content or activities. When learners interact with a digital learning environment, their existing interests will likely influence the ways in which they engage, and the ways they engage will affect their level of interest in and knowledge of the topic. This study takes adolescent engagement patterns in a digital STEM learning environment, a customized version of Minecraft, and connects engagement patterns to the degree of interest in STEM learners enter and leave the experience with, as well as performance on knowledge assessments. Engagement is defined across three dimensions: affective, behavioral, and cognitive, and log data from the Minecraft server is extracted and engineered to be positioned within one of the dimensions of engagement. Time-series clustering is then used to extract patterns of behavior over time that differentiate learners. Emergent patterns revealed 5 profiles of engagement, including high performers, balanced performers, exploration only, point of interest only, and low performers. Scores on STEM, Astronomy, and Minecraft interest surveys were also divided by level of interest and correlated with time-series clusters. Bayesian models were used to incorporate additional factors into models that predict post-test individual interest. Knowledge and interest appeared to be deeply intertwined for these participants engaging in these Minecraft tasks, emphasizing the importance of knowledge for productive engagement, and increasing interest in a domain.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124239

Copyright and License Information

Copyright 2024 Matthew Gadbury

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