TY - GEN
T1 - Mining for STEM Interest Behaviors in Minecraft
AU - Gadbury, Matt
AU - Lane, H. Chad
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - We consider how pre-existing STEM interest influences the way in which adolescents engage an astronomy-themed Minecraft environment. Participants in an after-school program met for five sessions over the course of five weeks and explored a variety of hypothetical versions of Earth, such as Earth with no moon, in Minecraft. An association rule mining approach was taken to understand how differing levels of STEM interest influence in-game science tool usage and observations across worlds. Highest science tool use was observed among participants with moderate STEM interest, suggesting high engagement and desire to learn compared with the low and high STEM interest groups. High recorded observations among the high STEM interest group suggests confidence or high prior knowledge, while moderate tool use among low STEM interest learners might suggest development of interest in content.
AB - We consider how pre-existing STEM interest influences the way in which adolescents engage an astronomy-themed Minecraft environment. Participants in an after-school program met for five sessions over the course of five weeks and explored a variety of hypothetical versions of Earth, such as Earth with no moon, in Minecraft. An association rule mining approach was taken to understand how differing levels of STEM interest influence in-game science tool usage and observations across worlds. Highest science tool use was observed among participants with moderate STEM interest, suggesting high engagement and desire to learn compared with the low and high STEM interest groups. High recorded observations among the high STEM interest group suggests confidence or high prior knowledge, while moderate tool use among low STEM interest learners might suggest development of interest in content.
KW - Association rule mining
KW - Minecraft
KW - Motivation
KW - STEM interest
UR - http://www.scopus.com/inward/record.url?scp=85135951087&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135951087&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-11647-6_42
DO - 10.1007/978-3-031-11647-6_42
M3 - Conference contribution
AN - SCOPUS:85135951087
SN - 9783031116469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 236
EP - 239
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
Y2 - 27 July 2022 through 31 July 2022
ER -