Anatomy of STEM teaching in North American Universities

A large body of evidence demonstrates that strategies that promote student interactions and cognitively engage students with content lead to gains in learning and attitudinal outcomes for students in science, technology, engineering, and mathematics (STEM) courses. Many educational and governmental bodies have called for and supported adoption of these student centered strategies throughout the undergraduate STEM curriculum

Anatomy of STEM Teaching in American Universities: A Snapshot from a Large-Scale Observation Study

National and local initiatives focused on the transformation of STEM teaching in higher education have multiplied over the last decade. These initiatives often focus on measuring change in instructional practices, but it is difficult to monitor such change without a national picture of STEM educational practices, especially as characterized by common observational instruments. We characterized a snapshot of this landscape by conducting the first large scale observation-based study. We found that lecturing was prominent throughout the undergraduate STEM curriculum, even in classrooms with infrastructure designed to support active learning, indicating that further work is required to reform STEM education. Additionally, we established that STEM faculty’s instructional practices can vary substantially within a course, invalidating the commonly-used teaching evaluations based on a one-time observation

Unobserved classes and extra variables in high-dimensional discriminant analysis

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. We introduce a model-based discriminant approach, Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA), which can detect unobserved classes and adapt to the increasing dimensionality. Model estimation is carried out via a full inductive approach based on an EM algorithm. The method is then embedded in a more general framework for adaptive variable selection and classification suitable for data of large dimensions. A simulation study and an artificial experiment related to classification of adulterated honey samples are used to validate the ability of the proposed framework to deal with complex situations.Comment: 29 pages, 29 figure

Assessing the Efficacy of Virtual Experiments in the General Chemistry Laboratory

As more students enroll in chemistry courses, institutions are faced with increasing costs and limited laboratory space to keep up with the demand. One solution some institutions have turned to is the incorporation of virtual experiments into the curriculum, as this can lower costs and increase the availability of laboratory space. Some institutions have offered sections that complete all of their experiments in a virtual environment, others have offered sections that alternate between a traditional hands-on experiment and a virtual experiment, and some institutions have replaced only select experiments throughout the curriculum with a virtual experiment. To begin to be able to assess the affective outcomes in laboratory settings that include virtual experiments, six existing affective scales were modified for use in the laboratory setting. Sufficient evidence of the reliability and validity of the data from the existing scales was found. The functioning scales were then used to assess the affective outcomes of a Beer\u27s Law experiment, a calorimetry experiment, and a titration experiment in both a hands-on and virtual learning environment. To assess the cognitive outcomes in these experiments, rubrics based on common learning objectives were used to determine if students in both learning environments were able to meet instructors\u27 learning objectives for the experiment. The affective and cognitive outcomes were compared for each experiment to determine whether there was a difference between learning environments and also across the three experiments. The findings of this work are presented throughout this dissertation