In debates on institutional approaches to community college developmental instruction, this research examines one form of web-enabled formative assessments and presents a case study conducted in a linguistically-diverse urban community college. The study was designed to examine the multivariate associations between final class average, total user activity, total user activity in hours, and quiz average as measures of formative assessment and college writing achievement. The results of Pearson correlation analysis indicated 6 significant associations between micro engagement, formative assessment, and college writing achievement. Further, micro engagement, time on task, and formative assessment predicted 61% of college writing achievements. This study contributes to the conversation on community college writing instruction by demonstrating that employing multiple risk-free formative assessments provides one path toward college writing achievements.

In New York State Public Schools, social studies education centers on employing interdisciplinary approaches to help students learn civic values and historical events. Increasingly, due in no small part to the influence of popular culture, social studies education research is making fewer distinctions about racial and ethnic identities. Following some trends in the larger academic community, more of the research in social studies education categorizes ethnically and religiously diverse European and African groups into the narrow categories of White or Black. This practice of flattening diverse European and African groups into current day race frameworks can be problematic when teaching high school social studies, particularly in highly diverse urban centers, because it perpetuates binary racial constructions that both are rooted in the historical fallacy of presentism and tend to contradict the students' ontological realities.

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This chapter builds on the body of work that has depicted cryptocurrency as a model for science and higher education funding. To that end, this work examines the degree to which one or more cryptocurrencies would need to be adopted and achieve a network effect prior to implementation of such a funding model. Empirical data from three different cryptocurrencies were examined. The current work deploys generalized autoregressive conditional heteroskedasticity (GARCH) to analyze stochastic volatility. This work contends that the examined coins are likely overdistributed and too volatile, thereby limiting the wealth generation possibilities for funding science or higher education. Additionally, based on the GARCH analysis, this work highlights that cryptocurrency pricing metrics and valuation models, to this point, may be insufficiently complex to persuade institutional investors to seriously allocate capital to this ecosphere.