Science

In this paper we describe a qualitative study in which we examine individual student engagement during implementation of an instructional scaffold for critical evaluation of scientific models during Earth and space science lessons. We coded dialogic interactions of one student group in a sixth grade science classroom across three observations, wherein we analyzed the trajectory of engagement for a single student - Ray (a pseudonym), within the co-constructed learning of the group. The first of these observations involved implementation of a preconstructed scaffold, called the Model-Evidence Link (MEL) diagram, on the topic of hydraulic fracturing (fracking). With the MEL, students use evidence to compare a scientific model to an alternative model. In the second two observations, students used a more agentic variation of the activity called the build-a-MEL, to study the topics of fossils and freshwater resources respectively. After three observations, we transcribed and coded each interaction of students in the group. We then categorized and identified emerging patterns of Ray’s discourse and interactions with group members by using both a priori engagement codes and open coding. This paper was prepared for the 2020 AERA Annual Meeting. 

Strategies and strategic processing within science education are designed to help students learn not only what scientists have come to understand about the world but also how they learn it. Although many domain-general strategies can be implemented in science classrooms, some strategies are either specific to science or are encouraged within science. Historically, concept development and conceptual change approaches and empirical investigations dominated science’s strategies and strategic processing. More recently, argumentation, science as modeling, and the incorporation of socio-scientific topics dominate the strategies and strategic processing within science teaching and learning. Challenges to more widespread use of these approaches include lack of teacher experience and pedagogical knowledge around the strategies, as well as time and curricular limitations. Teacher education and professional development programs should seek to explicitly implement contemporary science strategy interventions to improve upon their use in K-12 classrooms and other learning environments. Doing so effectively will require well-researched and validated instructional scaffolds to facilitate the teaching and use of contemporary science learning strategies. This paper was prepared for the 2020 AERA Annual Meeting.

Critique and evaluation are considered essential to deeper science learning. Furthermore, critical evaluation may influence plausibility judgments about explanations through re-appraisal. We developed the YIS-activity (blinded for peer review) to activate students’ epistemic judgments (i.e., plausibility) about competing models explaining scientific phenomena and to further their learning about Earth science topics. This study seeks to answer the question, “How are the plausibility shifts and knowledge gains of students impacted by the evaluation of multiple explanatory models for the future availability of freshwater resources?” Participants (N=76) completed a YIS-activity about freshwater resources, including pre and post-instruction knowledge surveys and plausibility ratings. Paired-samples t-tests determined that the students showed significant knowledge gains [t(75)=4.46, p<.001, d=0.51]. Initial analysis of the omnibus plausibility shifts was not significant, however particular knowledge item score differences caused us to re-evaluate the plausibility relationships between the three presented models. Two models each showed significant differences with the third model, [t(75)=2.66, p<.001, d=0.30] and [t(75)=2.94, p=.004, d=0.33] respectively. These two models also did not have a significant plausibility shift between themselves. While students accomplished significant learning in the YIS-activity, this finding emphasizes the difficulty that students have when evaluating multiple scientific explanatory models. This presentation was prepared for the 2020 Annual International NARST Conference.