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Although prior research has highlighted the significance of representations for mathematical learning, there is still a lack of research on how students use multimodal external representations (MERs) to solve mathematical tasks in digital game-based learning (DGBL) environments. This exploratory study was to examine the salient patterns problem solvers demonstrated using MERs when they engaged in a single-player, three-dimensional architecture game that requires the acquisition and application of math knowledge and thinking in game-based context problem solving.

Digital game-based math learning environments (math DGBLE) are promising platforms that provide students with opportunities to master conceptual understanding and cultivate mathematical thinking, on which the contemporary mathematics education places an emphasis. Literature on learning support in digital game-based learning (DGBL) rarely investigate learners' support-use behaviors and interaction patterns in relation to math learning. We addressed this research gap in this exploratory mixed-methods study.

Adopting a pretest–posttest experimental design with repeated measures, this study examined the effects of three types of game-based learning supports in the form of modeling on knowledge development that contributed to successful math problem solving and students’ perceived game flow.

This study explores how the interplay between data and model design shifts 6th graders’ students' ideas about diffusion as they build a range of models (“paper and pencil” and computational models). We present a new web-based environment and approach that integrates model-based and data-based features in the same display which facilitates the comparison of models and real-world data. Further, we illustrate how this environment and approach lead students to converge on one canonical scientific model.

Coordinating modeling and real-world data is central to building scientific theories. This paper examines how a complementary focus on modeling and data contributed to 8th grade students’ learning of mechanisms underlying wildfire smoke spread in MoDa, a web-based environment that integrates computational modeling side-by-side with real-world data for comparison and validation. Epistemic network analysis of student responses in pre-post tests revealed a shift from primarily macro-level explanations to explanations that integrated macro and micro-level explanations of the phenomenon.

This study explored the use of a three-part suite of practice-based activities -- one- and two-player online simulations, an avatar-based simulation, and a virtual teaching simulator—for supporting preservice teachers in learning how to facilitate argumentation-focused discussions in elementary mathematics and science. We share findings from analysis of survey data examining four elementary teacher educators’ perceptions about using these activities within their respective elementary methods courses.

Eliciting and interpreting students’ ideas are essential skills in teaching, yet pre-service teachers (PSTs) rarely have adequate opportunities to develop these skills. In this study, we examine PSTs’ patterns of discourse and perceived learning through engaging in an interactive digital simulation called Eliciting Learner Knowledge (ELK). ELK is a seven-minute, chat-based virtual role play between a PST playing a “teacher” and a PST playing a “student” where the goal is for the teacher to find out what the student knows about a topic.

Facilitating discussions is a key approach that science teachers use to engage students in scientific argumentation. However, learning how to facilitate argumentation-focused discussions is an ambitious teaching practice that can be difficult to learn how to do well, especially for preservice teachers (PSTs) who typically have limited opportunities to tryout and refine this teaching practice.

Artificial intelligence (AI), an emerging technology, finds increasing use in STEM education and STEM education research (e.g., Zhai et al., 2020b; Ouyang et al., 2022; Linn et al., 2023). AI, defined as a technology to mimic human cognitive behaviors, holds great potential to address some of the most challenging problems in STEM education (Neumann and Waight, 2020; Zhai, 2021). Amongst these is the challenge of supporting all students to meet the vision for science learning in the 21st century laid out, for example in the U.S.