This is a project that has been funded by the NSF and is in the process of implementing the curriculum for the Environmental Sampling Research Module (ESRM) at a community college (Alvin Community College), Prairie View A and M University (HBU), a rural state university (Louisiana State University), a state university (Southern Utah University), and a private university (Rochester Institute of Technology) to engage students in inquiry-based research experiences and broaden participation. Students are mining the existing UH ESRM research database as well as data from ongoing ESRM research to conduct a variety of interdisciplinary learning activities. The outcomes of the project, which include interdisciplinary inquiry-based learning activities, an expanded database of the location and distribution pesticide activity, and newly discovered bacterial strains, are being disseminated through this web portal. Dr. Megan Mullins is serving as the external evaluator and is responsible for the overall assessment and evaluation of the project. The goals of this project are supported with diverse educational activities.  

Goal 1: Use the ESRM database to develop inquiry-based curricular learning material for training in interdisciplinary techniques to improve stem learning environment.
Beyond the advantages of the flexibility of integration, sustainability, and scalability, a unique feature of the ESRM is that it can be used as a learning tool to integrate a variety of STEM skills into different courses. The adaptability of these activities allows the exercises to be as simple or complex as desired, depending on the course into which they are integrated and the level of the participating students. Examples of interdisciplinary learning activities that will be developed for the project using the ESRM database are outlined below. 

• Activity 1: Comparative analysis of the kinetics of pesticide OP- degrading strains: For the project, students at participating institutions are using existing UH pesticide degradation data of 20+ strains to assess and compare the degradation kinetics of individual bacteria. These analyses are designed to familiarize students with utilizing mathematical data, a critical skill needed to understand the statistical significance of scientific data. Students will utilize the freeware package R for their statistical analyses. R is a very flexible software platform for biological data analysis and therefore allows the instructor to tailor the data analysis to the level of sophistication from simple T-tests and ANOVA to linear regression analysis as appropriate for that level of coursework. It is commonly used throughout academia and in the biotechnology industry and therefore experience with it will better prepare students for workforce entry. Participating institutions are provided with UH protocols for kinetics analysis which they can also use to compare their own strains and share best practices of implementation.
• Activity 2: Learning to use computational tools to retrieve and analyze biological data: We currently house 8 whole genome sequences of OP–degrading bacteria collected through ESRM. The genomic data of these 8 strains is freely available on the NCBI database. Using the whole genome sequences (WGS) of 8 bacterial genomes, the following bioinformatics exercises are being developed. (1) Navigating the bacterial genomes on the NCBI website and searching for proteins of interest. This exercise is meant to introduce genome sequence analysis for identifying proteins of interest and related information. (2) Using bioinformatics tools (unsupervised machine learning techniques) to find similarities between different genomes. Participants are using bioinformatics tools such as Vizbin to compare genomes and metagenomes. (3) Aligning bacterial genomes using Mauve. This exercise is introducing students to genome alignment and helping them to identify similar regions and recombination within the genomes.
• Activity 3: Analysis of the geographical distribution of OP-degradation: The mapped data allows for the simple statistical analysis of samples split spatially, temporally or both. Students are using UH genomic data, provided as raw 16S rDNA sequences, to identify the microorganisms present at a particular site, assess their distribution across the sampled region, and then use geographic information system (GIS) software to illustrate the phylogenetic relationships between strains of OP-degrading microorganisms. The GIS software provides a geographical interface that can be used to overlay phylogenetic trees upon mapped geographic data, thereby providing a visual representation of genetic relationships between microorganisms at sampled locations, i.e. phylogeography. As the database expands, each institution will be able to conduct its own phylogeographic analysis on the distribution of OP degradation of samples collected in the location of their choice.

Goal 2: Multi-institutional dissemination and integration of ESRM to engage students in collaborative research- broadening participation and enabling institutional transformation.
The content and flexibility of the ESRM makes it ideal for integration into a variety of courses (horizontally) and at different year levels (vertically). At UH, the ESRM has been integrated successfully in the BTEC 3100 Biotechnology Research Methods and Applications course for the past ten years. The ESRM protocol is relatively simple (20 minutes per week for 7 weeks), inexpensive (typically covered by lab fees), and easy to integrate into existing or new courses, including independent research experiences, as proven by the UH model. To replicate our model, we are training participating faculty in implementing the ESRM into their courses, recording student research data, and analyzing the data using learning activities described previously to improve STEM skills and contribute to an ongoing research activity. A workshop in Year1 has covered the proper techniques for soil collection and testing in the lab and training in the integration of the ESRM into courses at the collaborating institutions. Environmental sampling data gathered through implementation of the ESRM at these institutions is being added to the existing database, where it can be accessed by students and faculty of all participating institutions. In the future, as the database expands geographically, we intend to collaborate with other entities by providing remote access to other institutions, high schools, and biotech companies.

In summary, this project is contributing to STEM education research by exploring (1) how the adaptations of the ESRM benefits diverse students, institutions, and faculty and (2) how students’ participation in collaborative, multi-institutional research experiences and exposure to authentic research questions influence their engagement and interest in STEM fields.