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Authentic open-ended problems are increasingly appearing in university classrooms at all levels. Formative feedback that leads to learning and improved student work products is a challenge, particularly in large enrollment courses. This is a case study of one first-year engineering student team's experience with teaching assistant and peer feedback during a series of open-ended mathematical modeling problems called Model-Eliciting Activities. The goal of this study was to gain deep insight into the interactions between students, feedback providers, and written feedback by examining one team's perceptions of the feedback they received and the changes they made to their solutions based on their feedback. The practical purpose of this work is to begin to make recommendations to improve students' interactions with written feedback. The data sources consisted of individual student interviews, videos of the team's meetings to revise their solutions, the team's iterativelydeveloped solutions, the team's documented changes to the their solutions, and the written feedback they received from their teaching assistant and peers. The students explained that helpful peer feedback requires a time commitment, focuses on the mathematical model, and goes beyond praise to prompt change. The students also stated that generic TA feedback was not helpful. The greatest difference between the students' perceptions of TA and peer feedback was that the TA had influence over the team's grade and therefore the TA feedback was deemed more important. Feedback strategies to increase peer participation and improve teaching assistant training are described. Suggestions for continued research on feedback are provided.

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This analysis of the K-8 statistics standards in 41 United States of America (USA) state documents that include grade level expectations (GLEs) is timely given the increased need for statistical literacy as the quantity of available data around us grows. This analysis endeavors to answer the question: What are K-8 students in the USA expected to know and be able to do with regard to statistics as represented in the state standards documents? The study was framed using the four process components outlined in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: (1) formulate questions, (2) collect data, (3) analyze data, and (4) interpret results (Franklin et al., 2007). Among other findings, the analysis highlights two major types of knowledge expected in the documents, the knowledge expected to “do” each of the four processes and the knowledge expected to “understand” and/or “evaluate” the processes.

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