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Effective writing is important for communicating science ideas, and for writing-to-learn in science. This paper investigates lab reports from a large-enrollment college physics course that integrates scientific reasoning and science writing. While analytic rubrics have been shown to define expectations more clearly for students, and to improve reliability of assessment, there has been little investigation of how well analytic rubrics serve students and instructors in large-enrollment science classes. Unsurprisingly, we found that grades administered by teaching assistants (TAs) do not correlate with reliable post-hoc assessments from trained raters. More important, we identified lost learning opportunities for students, and misinformation for instructors about students’ progress. We believe our methodology to achieve post-hoc reliability is straightforward enough to be used in classrooms. A key element is the development of finer-grained rubrics for grading that are aligned with the rubrics provided to students to define expectations, but which reduce subjectivity of judgements and grading time. We conclude that the use of dual rubrics, one to elicit independent reasoning from students and one to clarify grading criteria, could improve reliability and accountability of lab report assessment, which could in turn elevate the role of lab reports in the instruction of scientific inquiry. 

This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences. 

Molecular dynamics (MD) simulations are routinely used to study structural dynamics of membrane proteins. However, conventional MD is often unable to sample functionally important conformational transitions of membrane proteins such as those involved in active membrane transport or channel activation process. Here we describe a combination of multiple MD based techniques that allows for a rigorous characterization of energetics and kinetics of large-scale conformational changes in membrane proteins. The methodology is based on biased, nonequilibrium, collective-variable based simulations including nonequilibrium pulling, string method with swarms of trajectories, bias-exchange umbrella sampling, and rate estimation techniques.