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Awe is a transformative emotion associated with positive educational and psychological outcomes, and is caused by experiences of vastness that induce accommodation. Vast VR scenes have been found to elicit awe. We examined self-reported causes of awe among grade 3–8 students — a previously unstudied age group regarding awe — in a virtual environment portraying entities over 20 orders of magnitude from atom to Sun. Most students reported feeling awe, around half specifically enough to be coded based on a priori categories drawn from the literature. Vastness of scale (including both large and small entities, and large differences in scale) was the most common cause of awe. Surprisingly, no student responses were related to accommodation. Vastness of evolution and degree of immersion were identified as novel causes of awe. Thus, even young children can experience awe in VR, opening possibilities for productive VR in education at the elementary school level. 

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. 

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. 

Public critiques of technologies and the algorithms that power them have pushed designers to critically consider for whom they design and who they include in design processes. In education, similar critiques highlight how computational technologies designed for novice learners commonly privilege certain ways of knowing and being. In response, this poster explores how the cultural construct of time is represented across computational platforms for novices and what this means, particularly for Indigenous learners and designers. 

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.