Civic learning is increasingly recognized as important by the higher education and workforce communities. The development of high‐quality assessments that can be used to evaluate students' civic learning during the college years has become a priority. This paper presents a comprehensive review of existing frameworks, definitions, and assessments of civic‐related constructs from approximately 30 projects relevant to higher education, and includes a discussion of the challenges related to assessment design and implementation. Synthesizing information from the review, we propose an assessment framework to guide the design of a next‐generation assessment of individuals' civic learning that takes advantage of recent advances in assessment methods. The definition identifies 2 key domains within civic learning: civic competency and civic engagement. Civic competency encompasses 3 areas (civic knowledge; analytic skills; and participatory and involvement skills), and civic engagement also captures 3 areas (motivations, attitudes, and efficacy; democratic norms and values; and participation and activities). We discuss item formats and task types that would ensure fair and reliable scoring for the assessment. The review of definitions of civic learning and its components developed by organizations, the proposed assessment framework, and assessment considerations presented here have potential benefits for a range of higher education institutions. This includes institutions that currently have students engaged in relevant curricular or cocurricular activities and also institutions that would find assessments of civic competency and engagement helpful in program development or in evaluating students' accomplishments.

Interpreting and creating graphs plays a critical role in scientific practice. The K-12 Next Generation Science Standards call for students to use graphs for scientific modeling, reasoning, and communication. To measure progress on this dimension, we need valid and reliable measures of graph understanding in science. In this research, we designed items to measure graph comprehension, critique, and construction and developed scoring rubrics based on the knowledge integration (KI) framework. We administered the items to over 460 middle school students. We found that the items formed a coherent scale and had good reliability using both item response theory and classical test theory. The KI scoring rubric showed that most students had difficulty linking graphs features to science concepts, especially when asked to critique or construct graphs. In addition, students with limited access to computers as well as those who speak a language other than English at home have less integrated understanding than others. These findings point to the need to increase the integration of graphing into science instruction. The results suggest directions for further research leading to comprehensive assessments of graph understanding.

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.