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Studies show that Research-Based Instructional Strategies (RBIS) help students learn, however their adoption has been slow. The Teacher Centered Systematic Reform Model (TCRM) is a general model for organizing enablers and barriers to adoption of new teaching methods that includes departmental, personal and teacher thinking factors. We used the TCRM model as a framework to assess the amount of formal lecture reported by 634 mathematics instructors in their undergraduate courses. Regression analyses found that instructors who participated in Project NExT (a professional development workshop) during their early careers were less likely to use lecture than non-participants. Other significant predictors of lecture less included evaluation expectations emphasizing active teaching methods, involvement in equity and diversity efforts, and prior experience with RBIS. Factors with a positive correlational association with lecture included evaluation efforts by departments where lecture was expected. Results confirmed some prior models in different disciplines. 

Vertical transfer from community college to a university offers a promising, although unrealized, pathway to diversify STEM disciplines. Studying how successful transfer-­receiving universities support STEM transfer students can offer insights into the institutional practices that promote transfer student retention and success. Using institutional data is crucial to identify vulnerable populations within the STEM transfer population and to design necessary changes in practice or policy, especially at the department level. Providing discipline-specific multidimensional support throughout STEM transfer students’ undergraduate careers can improve transfer rates and retention and ease students’ transition to the univer­sity. Although universities have developed promising practices and programs, support for STEM transfer students is not systematically available and should be more targeted, intentional, and comprehensive throughout the transfer and adjustment process. 

Abstract BackgroundNumerous studies show that active and engaging classrooms help students learn and persist in college, but adoption of new teaching practices has been slow. Professional development programs encourage instructors to implement new teaching methods and change the status quo in STEM undergraduate teaching, and structured observations of classrooms can be used in multiple ways to describe and assess this instruction. We addressed the challenge of measuring instructional change with observational protocols, data that often do not lend themselves easily to statistical comparisons. Challenges using observational data in comparative research designs include lack of descriptive utility for holistic measures and problems related to construct representation, non-normal distributions and Type-I error inflation for segmented measures. ResultsWe grouped 790 mathematics classes from 74 instructors using Latent Profile Analysis (a statistical clustering technique) and found four reliable categories of classes. Based on this grouping we proposed a simple proportional measure we called Proportion Non-Didactic Lecture (PND). The measure aggregated the proportions of interactive to lecture classes for each instructor. We tested the PND and a measure derived from the Reformed Teaching Observation Protocol (RTOP) with data from a professional development study. The PND worked in simple hypothesis tests but lacked some statistical power due to possible ceiling effects. However, the PND provided effective descriptions of changes in instructional approaches from pre to post. In tandem with examining the proportional measure, we also examined the RTOP-Sum, an existing outcome measure used in comparison studies. The measure is based on the aggregated items in a holistic observational protocol. As an aggregate measure we found it to be highly reliable, correlated highly with the PND, and had more statistical power than the PND. However, the RTOP measure did not provide the thick descriptions of teaching afforded by the PND. ConclusionsFindings suggest that useful dependent measures can be derived from both segmented and holistic observational measures. Both have strengths and weaknesses: measures from segmented data are best at describing changes in teaching, while measures derived from the RTOP have more statistical power. Determining the validity of these measures is important for future use of observational data in comparative studies. 

Observations are widely used in research and evaluation to characterize teaching and learning activities. Because conducting observations is typically resource intensive, it is important that inferences from observation data are made confidently. While attention focuses on interrater reliability, the reliability of a single-class measure over the course of a semester receives less attention. We examined the use and limitations of observation for evaluating teaching practices, and how many observations are needed during a typical course to make confident inferences about teaching practices. We conducted two studies based on generalizability theory to calculate reliabilities given class-to-class variation in teaching over a semester. Eleven observations of class periods over the length of a semester were needed to achieve a reliable measure, many more than the one to four class periods typically observed in the literature. Findings suggest practitioners may need to devote more resources than anticipated to achieve reliable measures and comparisons. 

The practices of graduate education in chemistry shape the quantity, quality and diversity of Ph.D. chemists throughout the workforce. Those who go on to academic careers influence the diversity of the professoriate that then shapes the next generation of chemists. This chapter reviews a comparative, quantitative analysis of trends in the productivity and diversity of chemistry Ph.D.s awarded by top US Ph.D.-granting departments in recent decades. Using time series data for individual graduate programs from a public database, departments are compared with each other and with national averages. The findings highlight departments that stand out— both positively and negatively—from their peers in educating a diverse group of Ph.D. chemists. Qualitative data suggests best practices for improving diversity in doctoral departments.