Abstract
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.
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References
Clark, D.B., et al.: Digital games, design, and learning: a systematic review and meta-analysis. Rev. Educ. Res. 86(1), 79–122 (2016). https://doi.org/10.3102/0034654315582065
Maltese, A.V., Tai, R.H.: Pipeline persistence: examining the association of educational experiences with earned degrees in STEM among U.S. students. Sci. Educ. 95(5), 877–907 (2011). https://doi.org/10.1002/sce.20441
Renninger, K.A., Hidi, S.E.: Interest development, self-related information processing, and practice. Theory Pract. 1–12 (2021). https://doi.org/10.1080/00405841.2021.1932159
Wouters, P., et al.: A meta-analysis of the cognitive and motivational effects of serious games. J. Educ. Psychol. 105(2), 249–265 (2013). https://doi.org/10.1037/a0031311
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Gadbury, M., Lane, H.C. (2022). Mining for STEM Interest Behaviors in Minecraft. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_42
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DOI: https://doi.org/10.1007/978-3-031-11647-6_42
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