An emphasis on higher-order thinking within the curriculum has been a subject of interest in the chemical and STEM literature due to its ability to promote meaningful, transferable learning in students. The systematic use of learning taxonomies could be a practical way to scaffold student learning in order to achieve this goal. This work proposes the use of Marzano's Taxonomy of Learning. Because it offers a functional way to distinguish lower from higher-order thinking, the taxonomy is particularly useful to instructors interested in helping students develop these skills. We outline and provide examples of how it was used in constructing Student Learning Outcomes (SLOs), class activities, and assessments for a first semester general chemistry course. Preliminary observations of the impact of this methodology on student learning are presented.

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Getting students to use grading feedback as a tool for learning is a continual challenge for educators. This work proposes a method for evaluating student performance that provides feedback to students based on standards of learning dictated by clearly delineated course learning outcomes. This method combines elements of standards-based grading into a framework that uses Marzano’s Taxonomy of Learning to guide the writing of clearly defined and scaffolded learning outcomes. By means of this methodology, students are equipped with increased levels of information obtained from assessments, both formative and summative. Students and faculty alike can more accurately diagnose strengths and weaknesses in learning down to the level of the concept(s). Early observations from a first-semester general chemistry course suggest that setting transparent standards for grading can serve as a valuable learning tool for students to allow them to focus on content proficiency rather than grades alone.

We propose an econometric procedure for obtaining de facto exchange rate regime classifications which we apply to study the relationship between exchange rate regimes and economic growth. Our classification method models the de jure regimes as outcomes of a multinomial logit choice problem conditional on the volatility of a country's effective exchange rate, a bilateral exchange rate and international reserves. An `effective' de facto exchange rate regime classification is then obtained by assigning country-year observations to the regime with the highest predictive probability obtained from the estimation problem. An econometric investigation into the relationship between exchange rate regimes and GDP growth finds that growth is higher under stable currency-value regimes.Significant asymmetric effects on country growth from not doing what is said are found for nonindustrialized countries. Countries that exhibit `fear of floating' experience significantly higher growth.

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