Science

Participatory science conducted in formal K–12 settings has many benefits, including the potential to engage teachers and students authentically in the scientific enterprise and to make learning more meaningful. Despite these benefits and others, school-based participatory science (SBPS) is not widespread. In this essay, we put forth a theory of SBPS that is emerging from a four-year study of efforts to integrate participatory science in elementary classrooms.

Two teachers—whose students were concerned about environmental injustices in their communities and eager to take action—initiated a collaboration to design freely available, customizable curriculum materials and a model professional development workshop. The workshop was designed to foster teacher efficacy in incorporating social justice into science teaching. To cultivate teacher efficacy, the materials were created to respond to middle school science teachers’ concerns about supporting students’ emotions around social justice issues and empowering students to take action.

Two teachers—whose students were concerned about environmental injustices in their communities and eager to take action—initiated a collaboration to design freely available, customizable curriculum materials and a model professional development workshop. The workshop was designed to foster teacher efficacy in incorporating social justice into science teaching. To cultivate teacher efficacy, the materials were created to respond to middle school science teachers’ concerns about supporting students’ emotions around social justice issues and empowering students to take action.

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.