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  1. Abstract

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 Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co‐design.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

     
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    Free, publicly-accessible full text available November 1, 2024
  2. Blikstein, P ; Van_Aalst, J ; Kizito, R ; Brennan, K (Ed.)
  3. Blikstein, P ; Van_Aalst, J ; Kizito, R ; Brennan, K (Ed.)
    We explored how Natural Language Processing (NLP) adaptive dialogs that are designed following Knowledge Integration (KI) pedagogy elicit rich student ideas about thermodynamics and contribute to productive revision. We analyzed how 619 6-8th graders interacted with two rounds of adaptive dialog on an end-of-year inventory. The adaptive dialog significantly improved students’ KI levels. Their revised explanations are more integrated across all grades, genders, and prior thermodynamics experiences. The dialog elicited many additional ideas, including normative ideas and vague reasoning. In the first round, students refined their explanation to focus on their normative ideas. In the second round they began to elaborate their reasoning and add new normative ideas. Students added more mechanistic ideas about conductivity, equilibrium, and the distinction between how an object feels and its temperature after the dialog. Thus, adaptive dialogs are a promising tool for scaffolding science sense-making. 
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  4. Abstract

    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.

     
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  5. null (Ed.)
  6. de Vries, E ; Hod, Y. ; Ahn, J. (Ed.)
    We report on design-based research to refine a professional development workshop that supports teachers to customize online curricula. We iteratively design representations to make the knowledge integration pedagogy of the curricula visible. We study ways to make the work of students using the curricula actionable for participating teachers. We analyze participants’ trajectories across the three iterations of the workshop. Initially, when participants realized they could customize the online curriculum, they developed feelings of ownership. Then, as participants deepened their understanding of the pedagogy, they began to use it to evaluate their own instruction. The trajectory culminated in participants connecting the pedagogy to student work from their own classroom. This led to a shift from focusing on remedies for misconceptions to seeking opportunities for building on students’ nascent ideas when customizing. The workshop refinements empowered teachers to mobilize the pedagogy to interpret their students' work to inform their customization decisions. 
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  7. null (Ed.)
    Recent work on automated scoring of student responses in educational applications has shown gains in human-machine agreement from neural models, particularly recurrent neural networks (RNNs) and pre-trained transformer (PT) models. However, prior research has neglected investigating the reasons for improvement – in particular, whether models achieve gains for the “right” reasons. Through expert analysis of saliency maps, we analyze the extent to which models attribute importance to words and phrases in student responses that align with question rubrics. We focus on responses to questions that are embedded in science units for middle school students accessed via an online classroom system. RNN and PT models were trained to predict an ordinal score from each response’s text, and experts analyzed generated saliency maps for each response. Our analysis shows that RNN and PT-based models can produce substantially different saliency profiles while often predicting the same scores for the same student responses. While there is some indication that PT models are better able to avoid spurious correlations of high frequency words with scores, results indicate that both models focus on learning statistical correlations between scores and words and do not demonstrate an ability to learn key phrases or longer linguistic units corresponding to ideas, which are targeted by question rubrics. These results point to a need for models to better capture student ideas in educational applications. 
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  8. The effectiveness of using learning analytics for learning design primarily depends upon two concepts: grounding and alignment. This is the primary conjecture for the study described in this paper. In our design-based research study, we design, test, and evaluate teacher-facing learning analytics for an online inquiry science unit on global climate change. We design our learning analytics in accordance with a socioconstructivism-based pedagogical framework,called Knowledge Integration, and the principles of learning analytics Implementation Design. Our methodology for the design process draws upon the principle of the Orchestrating for Learning Analytics framework to engage stakeholders (i.e. teachers, researchers, and developers). The resulting learning analytics were aligned to unit activities that engaged students in key aspects of the knowledge integration process. They provided teachers with actionable insight into their students’ understanding at critical junctures in the learning process. We demonstrate the efficacy of the learning analytics in supporting the optimization of the unit’s learning design. We conclude by synthesizing the principles that guided our design process into a framework for developing and evaluating learning analytics for learning design. 
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  9. 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. 
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