An official website of the United States government
Computational thinking (CT) involves breaking a problem into smaller components and solving it using algorithmic thinking and abstraction. CT is no longer exclusively for computer scientists but for everyone. While CT does not necessarily require programming, learning programming to enhance CT skills at a young age can help shape the next generation of children with knowledge that can help them succeed in our technological world. In order to produce teachers who are able to incorporate programming and CT into their future classrooms, we created an introductory Computer Science course (CS0) targeting future K-8 STEM teachers yet open to any student to enroll and learn computer science. We used a mixed-methods approach, examining both quantitative and qualitative data based on self-reported surveys, classroom artifacts, and focus groups from four semesters of data. We found that after taking the course, students’ self-efficacy in CT increased and while education students initially had lower confidence in their computing abilities than computer science students in the course, by the end of the semester there were no differences in their perceived and actual coding abilities when compared with computer science students. Furthermore, education students had many ideas on how to incorporate similar projects into their own future classrooms.
more »
« less
This content will become publicly available on January 2, 2026
Incorporating Coding into the Classroom: An Important Component of Modern Bioinformatics Instruction
Advancements in computation and machine learning have revolutionized science, enabling researchers to address once insurmountable challenges. Bioinformatics, a field that heavily relies on computer-driven analysis of biological data, has greatly benefited from these developments. However, traditional bioinformatics instruction frequently lacks the necessary coding skills. This article explores the transformation of a bioinformatics course in which feedback from students revealed limitations in traditional web application interfaces and the absence of coding automated pipelines for real-world applications. To address these shortcomings, the authors redesigned the project to incorporate computer programming using Google Colaboratory, where students access databases and websites by coding. The curriculum outlined the integration of modern programming skills with essential bioinformatics concepts. This article evaluates the effectiveness of this redesign by analyzing a selfresponse survey completed by course participants. Results show a positive impact on students’ perception of science and scientific research. Bayesian statistical analysis reveals that the programming component significantly predicts students’ career clarity in science and their pursuit of graduate education. Integrating coding exercises in bioinformatics education enhances students’ preparedness for real-world applications. The freely available GitHub repository will facilitate adoption. By embracing computational tools, students can become adept researchers capable of tackling complex biological questions.
more »
« less
- Award ID(s):
- 2142033
- PAR ID:
- 10592052
- Publisher / Repository:
- Taylor & Francis Group
- Date Published:
- Journal Name:
- Journal of College Science Teaching
- Volume:
- 54
- Issue:
- 1
- ISSN:
- 0047-231X
- Page Range / eLocation ID:
- 69 to 77
- Subject(s) / Keyword(s):
- Bioinformatics Python Laboratory exercise Genetics Google colab Coding Biological databases
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This Work-in-Progress paper in the Research Category uses a retrospective mixed-methods study to better understand the factors that mediate learning of computational modeling by life scientists. Key stakeholders, including leading scientists, universities and funding agencies, have promoted computational modeling to enable life sciences research and improve the translation of genetic and molecular biology high- throughput data into clinical results. Software platforms to facilitate computational modeling by biologists who lack advanced mathematical or programming skills have had some success, but none has achieved widespread use among life scientists. Because computational modeling is a core engineering skill of value to other STEM fields, it is critical for engineering and computer science educators to consider how we help students from across STEM disciplines learn computational modeling. Currently we lack sufficient research on how best to help life scientists learn computational modeling. To address this gap, in 2017, we observed a short-format summer course designed for life scientists to learn computational modeling. The course used a simulation environment designed to lower programming barriers. We used semi-structured interviews to understand students' experiences while taking the course and in applying computational modeling after the course. We conducted interviews with graduate students and post- doctoral researchers who had completed the course. We also interviewed students who took the course between 2010 and 2013. Among these past attendees, we selected equal numbers of interview subjects who had and had not successfully published journal articles that incorporated computational modeling. This Work-in-Progress paper applies social cognitive theory to analyze the motivations of life scientists who seek training in computational modeling and their attitudes towards computational modeling. Additionally, we identify important social and environmental variables that influence successful application of computational modeling after course completion. The findings from this study may therefore help us educate biomedical and biological engineering students more effectively. Although this study focuses on life scientists, its findings can inform engineering and computer science education more broadly. Insights from this study may be especially useful in aiding incoming engineering and computer science students who do not have advanced mathematical or programming skills and in preparing undergraduate engineering students for collaborative work with life scientists.more » « less
-
The demand for qualifed computing professionals is high, with thousands of positions remaining unflled each year. To create more qualifed professionals, initiatives to attract and engage students in computer science have been proposed, but they tend to concentrate on primary, secondary (K-12), and post-secondary (college) levels. With many adults looking for better career opportunities, it is sur- prising that few computer science initiatives focus on attracting adult learners to the feld. This paper presents the results of an infor- mal computer programming course that teaches the foundational concepts of computer programming to adults as they program hip- hop beats. This course is designed to attract adult learners that otherwise might have never considered computer programming, building their confdence and skills. We conducted this course on- line, two nights a week, for fve weeks, for about 40 participants. Afterward, we conducted a qualitative analysis of written survey data. We found that the adult learners’ perception of computer programming changed during the course, with many participants planning their next step in computing education.more » « less
-
Innovations by engineers and physical scientists working at the frontiers of microbiome engineering and discovery requires in-depth understanding of microbiome systems with parallel skills in bioinformatics and biostatistics. Despite the importance of integrating bioinformatics and biology into graduate student training in fields outside traditional biological sciences, academic institutions remain challenged with including these disciplines across departmental boundaries. Furthermore, it is critical for students in engineering, bioinformatics, and biostatistics to understand fundamentals behind the biological systems they model, and for biology students to gain competencies in applying bioinformatics and biostatistics to biological questions. To address these needs, we developed the Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM) graduate training partnership between Duke University and North Carolina Agricultural and Technical State University, which was funded by the National Science Foundation Research Traineeship (NRT) program. IBIEM’s goals include training interdisciplinary groups of students to: (a) transform conceptualization and develop skills for application of quantitative biology in microbiome areas; (b) perform cutting edge research requiring interdisciplinary team skills; and to (c) communicate their research across disciplinary barriers and to diverse audiences. The pedagogical framework adapted to foster trainee engagement is learner-centered teaching which emphasizes the importance of selfdirected learning with parallel ongoing assessment to optimize student outcomes. Since IBIEM trainees’ goals as well as entry-level knowledge and skills across disciplines varied greatly, program implementation was found to be challenging and required rigorous evaluation and refinements for effective training across disciplines and skill levels. A comprehensive program evaluation over five years found that the strongest learning and skills outcomes were linked to several “best practices”. Early provision of depth in fundamentals in R programming and reproducible research was found to be critical to “jump start” students without programming backgrounds. Addition of an overview of microbiome experimental design and analysis added important context as to how and where in the research process informatics fits into design progression and was highly motivating to students. Course modality was found to impact trainee outcomes with in-person classes that included hands-on practice and feedback showing greater improvements in training outcomes over hybrid, flipped and virtual course modalities. Furthermore, introduction of low, medium, and high level “challenges” along with in-person tutoring was found to be impactful in building a common foundation to span expertise levels and for engaging students across entry and advanced levels. Training impacts peaked during year four with cumulative implementation of revised strategies. Innovative training revisions and inclusion of critical elements was strongly linked to program satisfaction and ratings of advances in technical, professional and career skills as well as post-training carry over into trainees’ own research and leadership in their labs and careers. Furthermore, this training collaboration and partnership provided the foundation and training model for the newly funded NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) for work in the critical area of engineering the microbiome in built environments.more » « less
-
Palagi, Patricia M (Ed.)As genomics technologies advance, there is a growing demand for computational biologists trained for genomics analysis but instructors face significant hurdles in providing formal training in computer programming, statistics, and genomics to biology students. Fully online learners represent a significant and growing community that can contribute to meet this need, but they are frequently excluded from valuable research opportunities which mostly do not offer the flexibility they need. To address these opportunity gaps, we developed an asynchronous course-based undergraduate research experience (CURE) for computational genomics specifically for fully online biology students. We generated custom learning materials and leveraged remotely accessible computational tools to address 2 novel research questions over 2 iterations of the genomics CURE, one testing bioinformatics approaches and one mining cancer genomics data. Here, we present how the instructional team distributed analysis needed to address these questions between students over a 7.5-week CURE and provided concurrent training in biology and statistics, computer programming, and professional development. Scores from identical learning assessments administered before and after completion of each CURE showed significant learning gains across biology and coding course objectives. Open-response progress reports were submitted weekly and identified self-reported adaptive coping strategies for challenges encountered throughout the course. Progress reports identified problems that could be resolved through collaboration with instructors and peers via messaging platforms and virtual meetings. We implemented asynchronous communication using the Slack messaging platform and an asynchronous journal club where students discussed relevant publications using the Perusall social annotation platform. The online genomics CURE resulted in unanticipated positive outcomes, including students voluntarily discussing plans to continue research after the course. These outcomes underscore the effectiveness of this genomics CURE for scientific training, recruitment and student-mentor relationships, and student successes. Asynchronous genomics CUREs can contribute to a more skilled, diverse, and inclusive workforce for the advancement of biomedical science.more » « less
