Science

In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups.

In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups.

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.

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.

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.

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.

The limited availability of research instruments that reflect the vision of the Next Generation Science Standards (NGSS) restricts the field's understanding of whether and how teachers are making instructional shifts called for by the standards. The need for such instruments is particularly urgent with teachers of multilingual learners (MLs), who are called on to make shifts in how they think about and enact instruction related to both science and language.

We explored how various contextual resources accumulated over multiple years operated together to facilitate a team of high school teachers' sustained and agentive learning after a 4-year research–practice partnership (RPP) grant concluded. Specifically, we examined constellations of resources that promoted the co-evolution of the teachers' collective inquiry in the professional learning community (PLC) and classroom instruction, focused on supporting students' scientific explanations.

To better understand integrated STEM education, this work explored scores on the STEM Observation Protocol (STEM-OP), a newly developed observation protocol for use in K-12 science and engineering classrooms. The goals of this work were to better understand how integrated STEM might look throughout an integrated STEM unit and identify limitations of the instrument when examining daily scores and full unit implementation scores. The work takes a mixed methods approach to first examine what scores may be typically seen with daily and unit implementations.

To better understand integrated STEM education, this work explored scores on the STEM Observation Protocol (STEM-OP), a newly developed observation protocol for use in K-12 science and engineering classrooms. The goals of this work were to better understand how integrated STEM might look throughout an integrated STEM unit and identify limitations of the instrument when examining daily scores and full unit implementation scores. The work takes a mixed methods approach to first examine what scores may be typically seen with daily and unit implementations.