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The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students’ contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the teaching and learning of science consistent with contemporary calls.

There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts.

An ability to engage in system thinking is necessary to understand complex problems. While many pre-college students use system modeling tools, there is limited evidence of student reasoning about causal relationships that interact in diverging and converging chains, and how these affect system behavior. A chemistry unit on gas phenomena was implemented in two successive years with 73 high school students. Although the phenomena could be explained with simple linear causal reasoning, many student models included surprising and problematic causal chains and non-linear patterns.

Attention to improving science education has grown nationally and globally, as science and policy communities fnd themselves challenged by complex real-world problems that have neither straight-forward nor ultimate solutions (Anderson & Li, 2020). Science, technology, engineering, and math-ematics (STEM) permeate nearly every facet of modern life; indeed, STEM holds the key to meeting many of the problems facing humans today and in the future (OECD, 2019). Moreover, understand-ing science can fundamentally improve people’s lives (National Research Council, 2012b; OECD, 2019).

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.