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Contextualizing Plans: Aligning Students Goals and Plans During Game-Based Inquiry Science Learning

Conference Paper/Proceedings
Contextualizing Plans: Aligning Students Goals and Plans During Game-Based Inquiry Science Learning
Publication Year:
2024
Publication Source:
Learning And Collaboration Technologies, Pt Ii, Lct 2024
Volume:
14723
Funding Type:
ECR:Core
Author(s):
Wiedbusch, Megan; Dever, Daryn; Goslen, Alex; Carpenter, Dan; Marano, Cameron; Smith, Kevin; Azevedo, Roger
Supporting Project(s):

A key aim for science education is the improvement of scientific reasoning through inquiry-based learning which asks students to think and act like scientists. This requires the regulation of specific skills and abilities such as identifying problems, generating hypotheses and evidence, and drawing conclusions. Goal setting and planning can help learners regulate their learning as they engage with scientific inquiry, especially within investigative exploration. Contemporary work on scaffolds for science inquiry-learning requires we (1) understand how students set goals and plans, (2) measure the quality of goals and plans, and (3) develop adaptive intelligent goal and planning scaffolds. This paper presents the development of a new planning scaffold used by 101 middle school students during interactions with CRYSTAL ISLAND, a game-based learning environment designed to teach students about microbiology and through scientific inquiry. We map student goals and plans from the scaffold to a series of epistemic scientific reasoning activities to use in conjunction with online trace data of student behaviors to analyze student goal setting and planning constructed throughout the game. This study describes the development of an analytical mapping approach between (sub)goals and (sub)activities to measure student plan quality build using a planning scaffold. We report on the use of the scaffold as well as implications for future development of an adaptive and intelligent version of the tool. This work highlights that online-trace data should be contextualized to (sub)goals, and the design of intelligent and adaptive goal and planning tools should be dynamic to account for open-ended exploration that differs across learners.