In this study we investigate how federal and state policy makers, and school principals are working to improve science teacher quality. Interviews, focused discussions, and policy documents serve as the primary data source. Findings suggest that both policy makers and principals prioritize increasing incentives for teachers entering the science teaching profession, providing professional development for new teachers, and using students’ data to evaluate and improve instruction. Differences between the two leadership groups emerged in terms of the grain size and practicality of their concerns. Our findings indicate that the complexity of educational challenges to improve science teacher quality call for the co-construction of policy by multiple constituent groups including school principals, federal and state policy makers, and science education researchers.

This paper illustrates how the combination of teacher and computer guidance can strengthen collaborative revision and identifies opportunities for teacher guidance in a computer-supported collaborative learning environment. We took advantage of natural language processing tools embedded in an online, collaborative environment to automatically score student responses using human-designed knowledge integration rubrics. We used the automated explanation scores to assign adaptive guidance to the students and to provide real-time information to the teacher on students’ learning. We study how one teacher customizes the automated guidance tools and incorporates it with her in-class monitoring system to guide 98 student pairs in meaningful revision of two science explanations embedded in an online plate tectonics unit. Our study draws on video and audio recordings of teacher-student interactions during instruction as well as on student responses to pretest, embedded and posttest assessments. The findings reveal five distinct strategies the teacher used to guide student pairs in collaborative revision. The teacher’s strategies draw on the automated guidance to personalize guidance of student ideas. The teacher’s guidance system supported all pairs to engage in two rounds of revision for the two explanations in the unit. Students made more substantial revisions on posttest than on pretest yet the percentage of students who engaged in revision overall remained small. Results can inform the design of teacher professional development for guiding student pairs in collaborative revision in a computer-supported environment.

Guiding teachers to customize curriculum has shown to improve science instruction when guided effectively. We explore how teachers use student data to customize a web-based science unit on plate tectonics. We study the implications for teacher learning along with the impact on student self-directed learning. During a professional development workshop, four 7th grade teachers reviewed logs of their students’ explanations and revisions. They used a curriculum visualization tool that revealed the pedagogy behind the unit to plan their customizations. To promote self-directed learning, the teachers decided to customize the guidance for explanation revision by giving students a choice among guidance options. They took advantage of the web-based unit to randomly assign students (N = 479) to either a guidance Choice or a no-choice condition. We analyzed logged student explanation revisions on embedded and pre-test/post-test assessments and teacher and student written reflections and interviews. Students in the guidance Choice condition reported that the guidance was more useful than those in the no-choice condition and made more progress on their revisions. Teachers valued the opportunity to review student work, use the visualization tool to align their customization with the knowledge integration pedagogy, and investigate the choice option empirically. These findings suggest that the teachers’ decision to offer choice among guidance options promoted aspects of self-directed learning.

This study reports on the design, implementation, and impact of a remote professional development (PD) course for secondary school teachers who were transitioning to remote instruction during the COVID-19 pandemic. We designed technology innovations to strengthen the previously successful in-person course. The innovations support teachers to customize an instructional unit by setting and revising goals based on evidence from their students' prior work on the unit. A Curriculum Visualizer makes the pedagogy of the unit visible and guides planning for customization. Carefully curated small group activities using Zoom breakout rooms ensure that each teacher could share their thoughts, ideas, and impressions with other teachers. Participants were 23 science teachers from 12 different schools in a western U.S. state. We developed rubrics to code customization goals, plans, and moves using bottom-up methods and iterative refinement. Reflections on student work and use of the Curriculum Visualizer enabled teachers to set and refine customization goals and make evidence-based and pedagogy-aligned customization decisions that enhanced the interactive learning opportunities for their students. Our results reinforce the C-b model proposed by Sailer et al. (this issue) by illustrating the value of using technology to support collaborative, interactive PD activities.

Graph technologies are now widely available in K-12 science and mathematics classrooms. These technologies have the potential to impact the learning of science and mathematics, especially by supporting student investigations. We use meta-analysis to analyze 42 design and comparison studies involving data from 7699 students spanning over 35 years. In these studies, graphing technologies include computer software such as simulations; online tools such as graph utilities; and sensors such as temperature probes. We characterize the assessments used to measure graphing. We describe the investigative activities that graphing supports including generating hypotheses or predictions, collecting data, analyzing or interpreting data, and reflecting. Studies show that graphing technologies impact learning of mathematics and science topics as well as graphing itself. These technologies are especially advantageous for learning complex topics where students need to conduct investigations to interpret change over time or position such as functions, kinematics, and thermodynamics. Recent studies take advantage of logs of student interactions to study the design of automated guidance for graphing. We discuss the implications of these findings for instruction at the K-12 level.

Students bring a multitude of ideas and experiences to the classroom while they are reasoning about scientific phenomena. They often need timely guidance to refine build upon their initial ideas. In this study we explore the development of guidance prompts to provide students with personalized, real-time feedback in the context of a pedagogically grounded chatbot. In the current version of the tool, guidance prompts are authored by learning scientists who are experts in the content of the items and in Knowledge Integration pedagogy. When students engage with the chatbot, an idea detection model is used to determine the ideas that are present in a student explanation and then the expert-authored guidance prompts are assigned based on rules about which ideas are or are not present in the student explanation. While this approach allows for close attention to and control of the pedagogical intent of each prompt, it is time consuming and not easily generalizable. Further this rule-based approach limits the ways in which students can interact with the chatbot. The work in progress study presented in this paper explores the potential of using generative AI to create similarly pedagogically grounded guidance prompts as a first step towards increasing the generalizability and scalability of this approach. Specifically, we ask: using criteria from the Knowledge Integration Pedagogical Framework, how do ChatGPT 3.5-authored guidance prompts compare to human expert-authored guidance prompts? We find that while prompt engineering can enhance the alignment of ChatGPT-authored guidance prompts with pedagogical criteria, the human expert-authored guidance prompts more consistently meet the pedagogical criteria.

Advances in technology, science, and learning sciences research over the past 100 years have reshaped science education. This chapter focuses on how investigators from varied fields of inquiry who initially worked separately began to interact, eventually formed partnerships, and recently integrated their perspectives to strengthen science education. Advances depended on the broadening of the participants in science education research, starting with psychologists, science discipline experts, and science educators; adding science teachers, psychometricians, computer scientists, and sociologists; and eventually including leaders in cultural studies, linguistics, and neuroscience. This process depended on renegotiating power structures, deliberate funding decisions by the National Science Foundation and others, and sustained, creative teamwork. It reflects a growing commitment to ensure that all learners are respected and that all students learn to address the complex scientific dilemmas they face in their lives. This chapter traces the evolution of research on science education in the United States with a focus on 5- to 17-year-olds. It highlights trends in the view of the learner, the design of instruction, the role of professional development, and the impact of technology. The chapter closes with recommendations designed to realize the full potential of these advances.

With the widespread adoption of the Next Generation Science Standards (NGSS), science teachers and online learning environments face the challenge of evaluating students' integration of different dimensions of science learning. Recent advances in representation learning in natural language processing have proven effective across many natural language processing tasks, but a rigorous evaluation of the relative merits of these methods for scoring complex constructed response formative assessments has not previously been carried out. We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.

To promote a justice-oriented approach to science education, we formed a research-practice partnership between middle school science teachers, their students, curriculum designers, learning scientists, and experts in social justice to co-design and test an environmental justice unit for middle school instruction. We examine teacher perspectives on the challenges and possibilities of integrating social justice into their standards-aligned science teaching as they participate in co-design and teach the unit. The unit supports students to investigate racially disparate rates of asthma in their community by examining pollution maps and historical redlining maps. We analyze interviews and co-design artifacts from two teachers who participated in the co-design and taught the unit in their classrooms. Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co-design. Findings also reveal that teachers can share similar goals for empowering students to use science knowledge for civic action while framing the local socio-political factors contributing to the injustice differently, due in part to different institutional supports and constraints. Student interviews and a pre/postassessment illustrate how the unit facilitated students' progress in connecting socio-political and science ideas to explain the impacts of particulate matter pollution and who is impacted most. Analyses illuminate how teachers' pedagogical choices may influence whether and how students discuss the impact of systemic racism in their explanations. The findings inform refinement of the unit and suggest supports needed for co-design partnerships focused on integrating social justice and science.

Content-based automated scoring has been applied in a variety of science domains. However, many prior applications involved simplified scoring rubrics without considering rubrics representing multiple levels of understanding. This study tested a concept-based scoring tool for content-based scoring, c-rater™, for four science items with rubrics aiming to differentiate among multiple levels of understanding. The items showed moderate to good agreement with human scores. The findings suggest that automated scoring has the potential to score constructed-response items with complex scoring rubrics, but in its current design cannot replace human raters. This article discusses sources of disagreement and factors that could potentially improve the accuracy of concept-based automated scoring. © 2014 by the National Council on Measurement in Education.