Using funding from the National Science Foundation, the team hired a center director whose duties included packaging CCLA program elements for broad implementation in a "computing center in a box" \cite{9p5z2}. Like writing centers across the nation, the CCLA is fueled by peer-to-peer support. Peer instructors develop short tutorials and guides on common computing needs, provide support via a Slack workspace and email address, and provide face-to-face support in the learning commons. The CCLA is now in its third year. A quarter of DIVAS scholars have served as peer consultants and one former scholar is the current training manager for 11 new consultants. Individuals majoring in six different disciplines have utilized its services. The center is growing steadily as the campus community becomes more familiar with its goal. The CCLA team has reached out to the humanities and social sciences departments to support their computing needs as well.
Introductory biology and chemistry courses. The DIVAS team has brought computational thinking through image analysis into introductory biology and chemistry courses in two ways. First, we designed an image processing module for general chemistry students to investigate the hydrophobicity of materials by measuring the contact angle of a drop of water upon them. Students take pictures of the water droplets using their smartphones and then use ImageJ, an open-source and widely used image analysis and processing tool \cite{imagej}, to manually measure the angle of incidence from each image. This module has now been used for over five years.
Second, in an inquiry-based introductory biology course, mostly first-year students used a Google Colab notebook written by former DIVAS scholars to analyze images from a system to measure bacterial movement toward molecules (chemotaxis). A drop of agarose containing a test molecule or saline (control) is added to the center of each well in a six-well plate. The solid agarose droplet is then surrounded by stained E. coli in saline. Images are taken over time using flatbed scanners. Students first use ImageJ to develop their own strategies for measuring the change in cloudiness around the agarose plug over time (an indication that chemotaxis has occurred). Completely novice coders then review the code in the Colab notebook by drawing out a flowchart of what they see the code doing. When students were unsure of how the code was functioning, they tried changing part of it to see what effect it had. As a group, the class added clarifying annotations and create a full code map. From there, students used this code and modified parameters as needed to measure the change in cloudiness around the agarose plugs in their own well plates. This intervention is now in its second year.
Teacher training workshop. Finally, Nebraska high school teachers used another Google Colab notebook written by former DIVAS scholars last summer. Scholars wrote this code to measure the height and density of invasive grass growing in pots. The grass system provides a variety of angles to engage students in inquiry-based learning and to learn about asking questions, designing experiments, and analyzing and representing data. Teachers explored the pros and cons of manual measurements. They then studied the image-based approach to taking similar measurements by creating their own code maps as they worked through the Colab notebook. As teachers worked through the code, they identified other ways to take the same measurements and how to test the code to confirm it was functioning. Most of these teachers had never coded. Nevertheless, within a couple of hours, they learned to use the notebook. Further, they could evaluate it for use in the classroom both as a measurement tool and as a source of inquiry-based curriculum.  

Broadening the alliance

Over the three years of the pilot study, we have learned a lot about how to support self-efficacy in computing early in a student’s college career. The pilot data and anecdotal experiences alone might inspire other schools to try similar approaches. However, we still need to test the DIVAS program interventions across institutional types and different student populations. Broader implementation of all or part of DIVAS program elements will also help us identify its most critical elements. Teams wanting to implement a more streamlined program could implement just those elements with success. A broader DIVAS alliance will provide additional opportunities for students to collaborate, build their skills, and strengthen the community of practice. We also hope to expand the teacher training workshops, including both secondary and undergraduate educators. These workshops would use the Training of Trainers (ToT) model, empowering educators to integrate DIVAS interventions into their classrooms and research labs. 

Discussion

We have long needed to find ways to infuse computational thinking, coding, and the use of scientific software into natural and physical science undergraduate education. Our experience with the DIVAS project, our pilot study, and the additional opportunities it fostered suggest that this can be done in many environments friendly to a community of practice approach. We have seen how a community can change the way students view computing from a specialized, esoteric skill to a set of tools anyone can learn to use. We have seen that novice learners can learn to use computational tools to solve problems relevant to their disciplines, gaining confidence in computational skills, and highly desirable workforce skills. As DIVAS program elements are adopted at other institutions, we will see this impact more clearly.
Tessa Durham Brooks is an associate professor of Biology at Doane University in Crete, NE. She completed her bachelor's in Biochemistry at the University of Nebraska and her Ph.D. in Cell and Molecular Biology at the University of Wisconsin. She teaches courses in introductory biology, physiology, and a third-year seminar on making meaning. Her teaching focus is to promote a sense of belonging in order to enhance learning in inquiry-based and flipped classroom environments. Durham Brooks and her undergraduate team explore phenotypic responses of plants to environmental stimuli and the effects of stress in early development on later growth phenotypes. She has an interest in developing infrastructure and academic experiences that promote computational and quantitative self-efficacy of undergraduate students in the natural sciences. Contact her at tessa.durhambrooks@doane.edu.
Raychelle Burks is an Associate Professor of Chemistry at American University in Washington, DC. Her lab research team is focused on the development of colorimetric and luminescent sensing systems with integrated image and chemometric analysis for forensic applications. She is on the leadership team of the Digital Imaging and Vision Applications in Science (DIVAS) project and DIVAS Scholars research advisor. Beyond the bench, Dr. Burks is a popular science communicator appearing regularly on TV, radio, podcasts, and print outlets. Central to Dr. Burks' research, teaching, and service is the central tenet that equitable, diverse, and inclusive practices both respect people and produce scientific outcomes of greater integrity. She is a member of several local, national, and international committees, task forces, and projects focused on social justice and STEM. Contact her at burks@american.edu.
Mark Meysenburg is a Professor of Computing at Doane University, a small liberal arts university in Crete, NE. He teaches the university's programming sequence, networking, and cybersecurity courses. Mark's research interests are varied and eclectic, including evolutionary computation, machine learning, robotics, and computer vision. Mark also teaches an annual first-year seminar course, using intense role-playing games to teach the history of science. Contact him at mark.meysenburg@doane.edu
Erin Doyle is an associate professor of Biology at Doane University in Crete, NE.  She completed her bachelor's degree in Applied Mathematics at the University of Tulsa and her Ph.D. in Bioinformatics and Computational Biology at Iowa State University.  Her teaching focuses on helping students understand the importance of mathematics, computer science, and statistics to modern biology, and supporting positive student experiences in these areas. She teaches courses in introductory biology as well as upper-level electives in genetics and bioinformatics and computational biology.  Students in her undergraduate research lab use computational approaches to generate experimentally testable hypotheses and use experimental results to develop and refine computational models of biological processes.  Recent projects in her lab have focused on the identification of disease susceptibility genes in rice plants and functional characterization of bacteriophage genes. Contact her at erin.doyle@doane.edu.