Students in a Course-Based Undergraduate Research Experience Course Discovered Dramatic Changes in the Bacterial Community Composition Between Summer and Winter Lake Samples

Can We Quantify If It's a CURE?

Course-based undergraduate research experiences (CUREs) rapidly have become more common in biology laboratory courses. The effort to implement CUREs has stimulated attempts to differentiate CUREs from other types of laboratory teaching. The Laboratory Course Assessment Survey (LCAS) was developed to measure students' perceptions of how frequently they participate in activities related to iteration, discovery, broader relevance, and collaboration in their laboratory courses. The LCAS has been proposed as an instrument that can be used to define whether a laboratory course fits the criteria for a CURE or not. However, the threshold LCAS scores needed to define a course as a CURE are unclear. As a result, we examined variation in published LCAS scores among different laboratory course types. In addition, we examined the distribution of LCAS scores for students enrolled in our research-for-credit course. Overall, we found substantial variation in scores among CUREs and broad overlap among course types in scores related to all three scales measured by the LCAS. Furthermore, the mean LCAS scores for all course types fell within the main part of the distribution of scores for our mentored research students. These results suggest that the LCAS cannot be used to easily quantify whether a course is a CURE or not. We propose that the biology education community needs to move beyond trying to quantitatively identify whether a course is a CURE. Instead, we should use tools like the LCAS to investigate what students are actually doing in their laboratory courses and how those activities impact student outcomes.

A course-based undergraduate research experience for bioinformatics education in undergraduate students

With rapid development of sequencing technology and the continuous accumulation of biological big data, people who are capable of using bioinformatic skills to analyze omics data and work out biological problems are urgently needed in the workforce, which highlights the importance of developing bioinformatics skills early in the undergraduate curriculum. Meanwhile, course-based undergraduate research experience (CURE) courses have been proved to be an effective teaching format that have many advantages over traditional labs and lectures. Here we introduced an implementation of CURE course of bioinformatics data analysis and visualization for undergraduate students in major of bioinformatics and evaluated the learning outcomes. We were able to address 10 out of 15 core competencies identified by Network for Integrating Bioinformatics into Life Sciences Education. Besides, results evaluated by Laboratory Course Assessment Survey demonstrated the goals of collaboration, discovery and relevance, and iteration were accomplished in our course. Meanwhile, a significant increase in scores of final examinations and a long-term improvement on students' research ability on bioinformatics data analysis and visualization were also observed. In summary, this CURE course is useful for undergraduate students learning related knowledge and participate in authentic research in the field of bioinformatics.

Development of a data science CURE in microbiology using publicly available microbiome datasets

Scientific and technological advances within the life sciences have enabled the generation of very large datasets that must be processed, stored, and managed computationally. Researchers increasingly require data science skills to work with these datasets at scale in order to convert information into actionable insights, and undergraduate educators have started to adapt pedagogies to fulfill this need. Course-based undergraduate research experiences (CUREs) have emerged as a leading model for providing large numbers of students with authentic research experiences including data science. Originally designed around wet-lab research experiences, CURE models have proliferated and diversified globally to accommodate a broad range of academic disciplines. Within microbiology, diversity metrics derived from microbiome sequence information have become standard data products in research. In some cases, researchers have deposited data in publicly accessible repositories, providing opportunities for reproducibility and comparative analysis. In 2020, with the onset of the COVID-19 pandemic and concomitant shift to remote learning, the University of British Columbia set out to develop an online data science CURE in microbiology. A team of faculty with collective domain expertise in microbiome research and CUREs developed and implemented a data science CURE in which teams of students learn to work with large publicly available datasets, develop and execute a novel scientific research project, and disseminate their findings in the online Undergraduate Journal of Experimental Microbiology and Immunology. Analysis of the resulting student-authored research articles, including comments from peer reviews conducted by subject matter experts, demonstrate high levels of learning effectiveness. Here, we describe core insights from course development and implementation based on a reverse course design model. Our approach to course design may be applicable to the development of other data science CUREs.