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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. 

This study takes advantage of advances in Natural Language Processing (NLP) to build an idea detection model that can identify ideas grounded in students’ linguistic experiences. We designed adaptive, interactive dialogs for four explanation items using the NLP idea detection model and investigated whether they similarly support students from distinct language backgrounds. The curriculum, assessments, and scoring rubrics were informed by the Knowledge Integration (KI) pedagogy. We analyzed responses of 1,036 students of different language backgrounds taught by 10 teachers in five schools in the western United States. The adaptive dialog engages students from both monolingual English and multilingual backgrounds in incorporating additional relevant ideas into their explanations, resulting in a significant improvement in student responses from initial to revised explanations. The guidance supports students in both language groups to progress in integrating their scientific ideas. 

Mobile technologies that include photo-taking elements help to support children’s observation and exploration of the natural world; however, how families engage with digital photo-taking features have not been fully explored. We investigated how 22 families engage with digital photo-taking features of a MAR app that prompts place-based observation of the outdoors. Families’ interactions with the app were qualitatively coded and then two vignettes from different place-based micro-learning locations were developed to understand learning practices related to photo-taking. Findings show that families were deliberate in taking photos that recorded their observations of the outdoors; however, the environment and features within the app impacted families’ observational talk and excitement levels during the photo-taking activities themselves. These findings contribute to the understanding of the importance of photo-taking activities during MAR experiences. 

In this study, we focused on the challenges that kindergarten teachers discussed related to curriculum, lack of teacher autonomy, and administrative control. We explored teachers' resistance using the lens of creative insubordination (Gutiérrez, 2016) to answer these questions: 1) What challenges do kindergarten teachers discuss about implementing playful math in kindergarten? and 2) How do teachers discuss navigating implementation challenges? We found that teachers used playful resistance as a way to enact “Using the Master’s Tools,” an approach to Creative Insubordination outlined by Gutièrrez (2016). This study corroborates and extends prior work on creative insubordination by focusing on early childhood mathematics and exploring teachers’ strategies to employ playful resistance. 

Developing assessment tools for computational thinking (CT) in STEM education is a precursor for science teachers to effectively integrate intervention strategies for CT practices. One problem to assessing CT skills is students’ varying familiarity with different programming languages and platforms. A text-neutral, open-source platform called iFlow, is capable of addressing this issue. Specifically, this innovative technology has been adopted to elicit underrepresented undergraduate students’ debugging skills. We present how the visual-based coding platform can be applied to bypass programming language bias in assessing CT. In this preliminary study, we discuss design principles of a visual-based platform to effectively assess debugging practices – identification, isolation, and iteration – with the use of iFlow assignments. Our findings suggest how the ability of iFlow to test parts of a program independently, dataflow connectivity, and equity in removing biases from students’ various backgrounds are advantageous over text-based platforms.