Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still an expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in various fields, such as speech recognition, image classification, bioinformatics, etc. One potential application of these AI techniques is in the field of drug discovery. The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers' preparation for and experiences teaching statistical modeling have focused on probabilistic models. Subsequently, much of the research literature related to teachers' understanding has focused on probabilistic models. This study explores the extent to which secondary statistics teachers appear to understand ideas of statistical modeling, specifically the processes of model building and evaluation, when introduced using classification trees, a type of algorithmic model. Results of this study suggest that while teachers were able to read and build classification tree models, they experienced more difficulty when evaluating models. Further research could continue to explore possible learning trajectories, technology tools, and pedagogical approaches for using classification trees to introduce ideas of statistical modeling.

Graduate teaching assistants (GTAs) are responsible for the instruction of many statistics courses offered at the university level, yet little is known about these students’ preparation for teaching, their beliefs about how introductory statistics should be taught, or the pedagogical practices of the courses they teach. An online survey to examine these characteristics was developed and administered as part of an NSF-funded project. The results, based on responses from 213 GTAs representing 38 Ph.D.–granting statistics departments in the United States, suggest that many GTAs have not experienced the types of professional development related to teaching supported in the literature. Evidence was also found to suggest that, in general, GTAs teach in ways that are not aligned with their own beliefs. Furthermore, their teaching practices are not aligned with professionally-endorsed recommendations for teaching and learning statistics. First published May 2017 at Statistics Education Research Journal Archives

Statistics students’ conceptions of the work of statisticians and the discipline of statistics may play an important role in the topics to which they attend and their interest in pursuing further study. To learn about students’ conceptions, we collected open-ended survey responses from 44 undergraduate students who had completed introductory statistics courses. We used a grounded theory phenomenographical qualitative approach to identify several themes in students’ conceptions. In addition to the test-and-procedure conception, we offer several other themes, such as acknowledgement of variation and the role of ethical integrity. We use a metaphor of painting styles to compare to experts’ conceptions of statistics. By identifying “seeds” of what may be developed into expert conceptions, these preliminary results set possible foundations to explore trajectories that may help shape students’ conceptions of statistics. First published June 2020 at Statistics Education Research Journal Archives

One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.