No abstract

Electricity is regarded as one of the most challenging topics for students of all ages. Several researchers have suggested that naïve misconceptions about electricity stem from a deep incommensurability (Slotta and Chi 2006;Chi 2005) or incompatibility (Chi et al. 1994) between naïve and expert knowledge structures. In this paper we argue that adopting an emergent levels-based perspective as proposed by Wilensky and Resnick (1999), allows us to reconceive commonly noted misconceptions in electricity as behavioral evidences of ''slippage between levels,'' i.e., these misconceptions appear when otherwise productive knowledge elements are sometimes activated inappropriately due to certain macro-level phenomenological cues only. We then introduce NIELS (NetLogo Investigations In Electromagnetism), a curriculum of emergent multi-agent-based computational models. NIELS models represent phenomena such as electric current and resistance as emergent from simple, body-syntonic interactions between electrons and other charges in a circuit. We discuss results from a pilot implementation of NIELS in an undergraduate physics course, that highlight the ability of an emergent levels-based approach to provide students with a deep, expert-like understanding of the relevant phenomena by bootstrapping, rather than discarding their existing repertoire of intuitive knowledge.

Science, Technology, Engineering and Mathematics (STEM) education garnered significant attention in recent years and has emerged as a key field of research globally. The goal of this article is to offer a critical review of how STEM education and its transdisciplinarity were defined and/or positioned in empirical studies published during the early formulation of the field. In particular, we sought to identify how these studies conceptualize learners and learning and portray the underlying assumptions in light of the macrosystemic discourses that often serve as ideological forces in shaping research and practice of STEM education. We examined 154 peer-reviewed articles published between January 2007 and March 2018 and analysed them along several emergent dimensions: their geo-spatial focus, focal disciplinary areas, methodological and theoretical assumptions, and major findings. Grounded in a critical transdisciplinary perspective, we used critical discourse analysis to identify how macrosystemic and institutionalized forces-overtly and implicitly-shape what counts as STEM education research, including its goals and conceptualizations of learners and learning. Our analysis highlights the need for aesthetic expansion and diversification of STEM education research by challenging the disciplinary hegemonies and calls for reorienting the focus away from human capital discourse.

In this paper, we present a third-grade ecology learning environment that integrates two forms of modeling--embodied modeling and agent-based modeling (ABMs)--through the generation of mathematical representations that are common to both forms of modeling. The term "agent" in the context of ABMs indicates individual computational objects or actors that obey simple rules assigned or controlled by the user. It is the interactions between these agents that give rise to emergent, aggregate-level behaviors in complex systems. While several researchers have argued for the effectiveness of ABMs for learning about complex systems, the design of classroom activity systems using ABMs, especially for elementary students, has received relatively less attention. In this paper, we report on a 2-week long proof-of-concept study conducted in a third-grade classroom of 15 students in which students began with an embodied modeling activity of foraging behavior, followed with the generation of mathematical inscriptions based on their embodied actions, and finally, conducted further inquiry of interdependence in an ecosystem using two separate ABMs. Furthermore, we show that the lens of mechanistic reasoning can be productively used to identify the process of students' conceptual development of interdependence in an Correspondence to: Pratim Sengupta;

Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students' learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to support synergistic learning remains an important challenge. Relatively, little is known about how a student's conceptual understanding develops in such learning environments and the difficulties they face when learning with such integrated curricula. In this paper, we present a research study with CTSiM (Computational Thinking in Simulation and Modeling)-computational thinking-based learning environment for K-12 science, where students build and simulate computational models to study and gain an understanding of science processes. We investigate a set of core challenges (both computational and science domain related) that middle school students face when working with CTSiM, how these challenges evolve across different modeling activities, and the kinds of support provided by human observers that help students overcome these challenges. We identify four broad categories and 14 subcategories of challenges and show that the human-provided scaffolds help reduce the number of challenges students face over time. Finally, we discuss our plans to modify the CTSiM interfaces and embed scaffolding tools into CTSiM to help students overcome their various programming, modeling, and science-related challenges and thus gain a deeper understanding of the science concepts.