University undergraduate course grades have several purposes: they provide feedback to the student and motivation to perform well; serve as admission criteria for entering a major; and are used as selection criteria for future employers and graduate programs. Accurate assignment of grades is therefore important and critical to ensure fairness. However, grades may also impact the student’s assessment of the instructor, which leads to a conflict of interest when such assessments are a component of employment, salary, or tenure decisions. This paper performs a detailed descriptive analysis of undergraduate grades collected over an eight year period from a major metropolitan university. Interesting grading patterns are identified and discussed, and the analysis suggests that grading policies vary substantially at the department, course, and instructor level. A connection is observed between course/department enrollment and average grades assigned. A particular focus of this study involves describing the grading behavior of instructors, with the goal of identifying instructors that assign grades that are statistically far above or below the norm. The analysis performed in this study can be applied to grade data from other universities using our publicly available Python-based analytics tool. The results of these analyses can be used to better understand existing grading policies, identify potential sources of grading inequities, and, when appropriate, take corrective action.

With limited time in the school day to meet required state-level objectives across subjects, it is challenging to meet the need to address declining physical activity levels and to incorporate additional computer science instruction in elementary schools. Project moveSMART uses a web-based platform to integrate opportunities for physical education with computer science and computational thinking (CS/CT) learning activities. Fourth grade students who underwent a single-day intervention experienced a significant improvement in their interest in coding and their perceptions of coders. This paper presents a series of three new lessons in the fifth grade moveS MART curriculum that allow students to create and improve a physical activity monitor. We employ a project-based learning approach to expose students to data analysis and machine learning in the framework of classification and pattern recognition.