Biology

Science instruction should involve learners in generating and warranting ideas, what we call epistemic generation. In modeling instruction, epistemic generation should be achieved by coordinating a model structure with the experienced world in reciprocal directions denoted as developing models and using models. In the former, a model structure is shaped from experience. In the latter, experience is shaped by a model structure. Focusing on this latter direction, the present study combats the perception that using models is not a generative practice but merely the dutiful application of others' ideas.

Science instruction should involve learners in generating and warranting ideas, what we call epistemic generation. In modeling instruction, epistemic generation should be achieved by coordinating a model structure with the experienced world in reciprocal directions denoted as developing models and using models. In the former, a model structure is shaped from experience. In the latter, experience is shaped by a model structure. Focusing on this latter direction, the present study combats the perception that using models is not a generative practice but merely the dutiful application of others' ideas.

There is a compelling need to strengthen professional learning (PL) regarding systems thinking, since it crosses all disciplinary boundaries and is a crosscutting concept in the Next Generation Science Standards. Yet research has shown that effectively deploying systems thinking in the classroom is challenging both for students and for teachers. This paper presents a case study of how two middle school science teachers, engaged in systems thinking and a game design task, worked together to modify a Scratch game during a PL workshop.

This study is part of a larger project on culturally and linguistically responsive instruction for multilingual learners (MLs) in biology (CLIMB), aimed at enhancing MLs’ engagement in science practices, biology content, and language development. We developed the CLIMB observation protocol to capture how teachers implement responsive instruction, integrating language development, biology content, and science practices. The protocol is comprised of six elements that align with research-backed practices to support MLs: Attention to Language, Multiple Modalities, Collaboration, Affirming Identities, Funds of Knowledge, and Sociopolitical Consciousness.

With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With the indirect aim of introducing Bayesian methods at the K-12 level, we have developed a Bayesian data analysis activity and implemented it with 35 mathematics and science pre-service teachers. In this article, we describe the activity, the web app supporting the activity, and pre-service teachers’ perceptions of the activity.

This study leverages natural language processing (NLP) to deepen our understanding of how students integrate their ideas about genetic inheritance while engaging in an adaptive dialog. 

This study leverages natural language processing (NLP) to deepen our understanding of how students integrate their ideas about genetic inheritance while engaging in an adaptive dialog. 

This collection of curriculum units focus on the biology topics of natural selection, matter and energy, and genetics. Each unit contains a series of lessons, videos, and materials for students and teachers.