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Global science education reform calls for developing student knowledge-in-use that applies the integrated knowledge of core ideas and scientific practices to make sense of phenomena or solve problems. Knowledge-in-use development requires a long-term, standards-aligned, coherent learning system, including curriculum and instruction, assessment, and professional learning. This paper addresses the challenge of transforming standards into classrooms for knowledge-in-use and presents an iterative design process for developing a coherent and standards-aligned learning system. Using a project-based learning approach, we present a theory-driven, empirically validated learning system aligned with the U.S. science standards, consisting of four consecutive curriculum and instruction materials, assessments, and professional learning to support students’ knowledge-in-use in high school chemistry. We also present the iterative development and testing process with empirical evidence to support the effectiveness of our learning system in a five-year NSF-funded research project. This paper discusses the theoretical perspectives of developing an NGSS-aligned, coherent, and effective learning system and recaps the development and testing process by unpacking all essential components in our learning system. We conclude that our theory-driven and empirically validated learning system would inform high school teachers and researchers across countries in transforming their local science standards into curriculum materials to support students’ knowledge-in-use development.

 

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. Using computational system models and a system dynamics approach can support students in overcoming these challenges when making sense of complex phenomena. In this paper, we describe an empirical study that examines how 10th grade students engage in aspects of ST through computational system modeling as part of a Next Generation Science Standards-aligned project-based learning unit on chemical kinetics. We show students’ increased capacity to explain the underlying mechanism of the phenomenon in terms of change over time that goes beyond linear causal relationships. However, student models and their accompanying explanations were limited in scope as students did not address feedback mechanisms as part of their modeling and explanations. In addition, we describe specific challenges students encountered when evaluating and revising models. In particular, we show epistemological barriers to fruitful use of real-world data for model revision. Our findings provide insights into the opportunities of a system dynamics approach and the challenges that remain in supporting students to make sense of complex phenomena and nonlinear mechanisms.

 

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learn- ing (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student- drawn models and their written descriptions of those models. We developed six modeling assessment tasks for middle school students that integrate disciplinary core ideas and crosscutting concepts with the modeling practice. For each task, we asked students to draw a model and write a description of that model, which gave students with diverse backgrounds an opportunity to represent their understanding in multiple ways. We then collected student responses to the six tasks and had human experts score a subset of those responses. We used the human-scored student responses to develop ML algorithmic models (AMs) and to train the computer. Validation using new data suggests that the machine-assigned scores achieved robust agreements with human consent scores. Qualitative analysis of student-drawn models further revealed five characteristics that might impact machine scoring accuracy: Alternative expression, confusing label, inconsistent size, inconsistent position, and redundant information. We argue that these five characteristics should be considered when developing machine-scorable modeling tasks. 

We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

 

Developing and using models to make sense of phenomena or to design solutions to problems is a key science and engineering practice. Classroom use of technology-based tools can promote the development of students’ modelling practice, systems thinking, and causal reasoning by providing opportunities to develop and use models to explore phenomena. In previous work, we presented four aspects of system modelling that emerged during our development and initial testing of an online system modelling tool. In this study, we provide an in-depth examination and detailed evidence of 10th grade students engaging in those four aspects during a classroom enactment of a system modelling unit. We look at the choices students made when constructing their models, whether they described evidence and reasoning for those choices, and whether they described the behavior of their models in connection with model usefulness in explaining and making predictions about the phenomena of interest. We conclude with a set of recommendations for designing curricular materials that leverage digital tools to facilitate the iterative constructing, using, evaluating, and revising of models.

 

This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

 

Students' motivation plays an important role in successful science learning. However, motivation is a complex construct. Theories of motivation suggests that students' motivation must be conceptualized as a motivational system with numerous components that interact in complex ways and influence metacognitive processes such as self‐evaluation. This complexity is further increased because students' motivation and success in science learning influence each other as they develop over time. It is challenging to study the co‐development of motivation and learning due to these complex interactions which can vary widely across individuals. Recently, person‐centered approaches that capture students' motivational profiles, that is, the multiplicity of motivational factors as they co‐occur in students, have been successfully used in educational psychology to better understand the complex interplay between the co‐development of students' motivation and learning. We employed a person‐centered approach to study how the motivational profiles, constructed from goal‐orientation, self‐efficacy, and engagement data ofN = 401 middle school students developed over the course of a 10‐week energy unit and how that development was related to students' learning. We identified four characteristic motivational profiles with varying temporal stability and found that students' learning over the course of the unit was best characterized by considering the type of students' motivational profiles and the transitions that occurred between them. We discuss implications for the design and implementation of interventions and future research into the complex interplay between motivation and learning.

 

Student science proficiency development demands sustainable and coherent learning environment support. Scholars argue that project‐based learning (PBL) is an efficient approach to promoting student science learning, compared to conventional instructions. Yet, few studies have delved into the learning process to explore how a coherent PBL system consisting of curriculum, instruction, assessment, and professional learning promotes student learning. To address the gap, this study investigated whether students' science proficiency on the three post‐unit assessments predicted their achievement on a third‐party‐designed end‐of‐year summative science test in a coherent high school chemistry PBL system aligned with the recent US science standards. The study employed a cluster randomized experimental design to test an intervention using our PBL system and only used data from the treatment group. The sample consisted of 1344 treatment students who participated in our PBL intervention and underwent the pretest and end‐of‐year summative test. Students' responses to the three post‐unit assessments were selected and rated to indicate their science proficiency. Two‐level hierarchical linear models were employed to explore the effects of students' performances of three post‐unit assessments on their end‐of‐year summative achievement, considering and controlling for student prior knowledge (i.e., pretest and prior post‐unit assessments). This study suggests two main findings. First, students' science proficiency in the three units could cumulatively and individually predict their summative science achievement. Second, students' performances on the two types of tasks (i.e., developing and using models) in the three post‐unit assessments could also be used to predict their summative science achievement. This research contributes to the field by showing that a coherent standards‐aligned PBL system can significantly and sustainably impact student science proficiency development.