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This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patterns, and equilibrium. In contrast, ST has shown promise as an approach for making sense of complex problems.

As technology advances, opportunities and challenges of its uses arise. The educational field is no different, and along with the need for educators to adapt to technological changes that occur spontaneously (widespread use of smartphones, for example), the opportunity of utilizing various digital platforms for educational purposes that were not possible or at least not cost-effective until now exist. The authors in this section illustrate these opportunities and challenges.

Understanding the world around us is a growing necessity for the whole public, as citizens are required to make informed decisions in their everyday lives about complex issues. Systems thinking (ST) is a promising approach for developing solutions to various problems that society faces and has been acknowledged as a crosscutting concept that should be integrated across educational science disciplines. However, studies show that engaging students in ST is challenging, especially concerning aspects like change over time and feedback.

This longitudinal study examines the relationship between students' knowledge-in-use performance and their performance on third-party designed summative tests within a coherent and equitable learning environment. Focusing on third-grade students across three consecutive project-based learning (PBL) units aligned with the Next Generation Science Standards (NGSS), the study includes 1067 participants from 23 schools in a Great Lakes state. Two-level hierarchical linear modeling estimates the effects of post-unit assessments on end-of-year summative tests.

The Framework for K-12 Science Education recognizes modeling as an essential practice for building deep understanding of science. Modeling assessments should measure the ability to integrate Disciplinary Core Ideas and Crosscutting Concepts. Machine learning (ML) has been utilized to score and provide feedback on open-ended Learning Progression (LP)-aligned assessments. Analytic rubrics have been shown to be easier to evaluate the validity of ML-based scores. A possible drawback of using analytic rubrics is the potential for oversimplification of integrated ideas.

Inquiry-Based Teaching Practice (IBTP) is an essential component of science education, and promoting its implementation is at the heart of various reform efforts. Even though science teachers regard IBTP as an essential pedagogical method, they rarely use it for various reasons. This study utilizes Bronfenbrenner's ecological framework to examine potential factors at various levels of the educational ecosystem that predict the implementation of inquiry-based teaching practices among Grade 9 science teachers in South Africa.

One reason for the widespread use of the energy concept across the sciences is that energy analysis can be used to interpret the behavior of systems even if one does not know the particular mechanisms that underlie the observed behavior. By providing an approach to interpreting unfamiliar phenomena, energy provides a lens on phenomena that can set the stage for deeper learning about how and why phenomena occur. However, not all energy ideas are equally productive in setting the stage for new learning.

This study explores the adaptations made by elementary school teachers to promote student Productive Disciplinary Engagement (PDE) during modeling activities within an online learning context. PDE means students make collective intellectual progress by using disciplinary practices and ideas to formulate possible solutions or answers. Given the abrupt transition to remote education instigated by the COVID-19 pandemic, student engagement and hands-on learning opportunities have faced significant challenges.

We recently published a paper in this journal (de Jong et al., 2023) that presented an overview of the literature on learning in science domains through direct instruction and guided inquiry-based learning. This paper was, in part, a response to Zhang et al. (2022) who argued that the evidence firmly supported the superiority of direct instruction over inquiry learning. Sweller et al.