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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 Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co‐design.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.

 

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.

 

We explore how a Teacher Action Planner (TAP) that synthesizes student ideas impacts teacher noticing. The TAP uses Natural Language Processing (NLP) to detect student ideas in written explanations. We compared teacher noticing while using the TAP to noticing when reviewing student explanations. The TAP helped teachers deepen their analysis of student ideas. We did not see any impact on immediate instructional practice. We propose redesigns to the TAP to better connect noticing to instruction. 

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. 

Constructed response items can both measure the coherence of student ideas and serve as reflective experiences to strengthen instruction. We report on new automated scoring technologies that can reduce the cost and complexity of scoring constructed‐response items. This study explored the accuracy of c‐rater‐ML, an automated scoring engine developed by Educational Testing Service, for scoring eight science inquiry items that require students to use evidence to explain complex phenomena. Automated scoring showed satisfactory agreement with human scoring for all test takers as well as specific subgroups. These findings suggest that c‐rater‐ML offers a promising solution to scoring constructed‐response science items and has the potential to increase the use of these items in both instruction and assessment. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 215–233, 2016.