Course: BIO 493/494/593/594
Department: Department of Biological, Geological and Environmental Sciences (BGES)
Institution: Cleveland State University
Instructor: Dr. Peng Jiang
Number & Level: 11 undergraduate and 8 graduate students
Digital Tools/Technologies Used: Blackboard
Author Bio: Dr. Peng Jiang is an Assistant Professor in the Department of Biological, Geological and Environmental Sciences (BGES) at Cleveland State University in Cleveland, Ohio. He is also a member at the Center for Gene Regulation in Health and Disease (GRHD) & Center for Applied Data Analysis and Modeling (ADAM). His research is focused on developing computational approaches for integrating multi-source high-dimensional omics data to systematically investigate the variation and dynamics of gene regulation in development, tissue regeneration and human diseases. He has published 42 scientific papers and served as Principal Investigator (PI) or Co-Investigator (Co-I) leading the bioinformatic and statistical methods development and applications in numerous federally funded research projects, including Defense Advanced Research Projects Agency (DARPA) and NIH.
In Spring 2022, I taught a newly developed course “Introduction to Computational Biology” at Cleveland State University. Computational Biology is an interdisciplinary field requiring a combination of knowledge in Biology, Statistics, and Computer Science. Hence, it is new for almost all students, regardless of major (Biology, Statistics, or Computer Science). The class is a mixture of undergraduate and graduate students. The content of the course covers R programming, Genomics, Data Analysis, Molecular biology, and Evolution. It is technically challenging for me to teach this course because the levels and backgrounds of students are different. I am concerned that whether the undergraduates can successfully pass the exam if the teaching content is designed for graduate students. Although it is challenging, most of the undergraduate students work extremely hard. The mid-term exam score distribution indicates that there is no systematic difference between the undergraduate and graduate students. This suggests that everyone can succeed if they work hard, regardless of where they start.
What is Computational Biology and Why Is It Challenging?
Data science approaches began to revolutionize the life sciences. Computational Biology is a discipline focusing on developing statistical methods and software tools to analyze biomedical Big Data (large, complex, high-dimensional, and multi-source). The role of Computational Biology in novel scientific discovery has been moving from the passenger seat (e.g., computational support) to the driver’s seat (driving the discovery from exploring large-scale biological data). The foundation of Computational Biology includes molecular biology, applied mathematics, statistics, and software engineering. A student can be good in one field but cannot be good in all fields. The interdisciplinarity nature of this course makes it very challenging for almost all students, regardless of their levels or grades.
Starting at the Same Line
The class is a mixture of undergraduate and graduate (Master and PhD) students. Although the students come from different grades or academic backgrounds, they have one thing in common: Computational Biology is new to everyone. The only way to teach such a complex course is to keep a teaching logical flow as such: Why do we need to learn this: Applications (high-level case studies) -> Basic concept -> Technical details -> Back to case studies. This logical flow is opposite to most of the textbook. Because for a textbook, usually, the logical flow introduces technical details first and then discusses case studies. Without a high-level understanding, the students can be easily lost in the forest. Hence, starting with a case study, and then followed by technical details, and ending with more case studies can make the complicated concept easy to understand. Please see the feedback from students:
Undergraduate Students Can Perform Just As Well As Graduate Students
I was concerned at the beginning about whether the undergraduate students can succeed in this course, given the graduate students have multiple years of experience, and because this course is more designed for graduate students. During my teaching, I found that the undergraduate students were in fact highly active. Both graduate and undergraduate students are highly motivated and studied very hard. It is easy to get students motivated by introducing a few case studies but it is hard to keep them motivated throughout the whole semester. Grouping them together to solve a particular problem can keep them motivated. I grouped the students into different grade levels to work on the same project. This teamwork makes undergraduate students feel empowered that they are not studying alone. The mid-term exam results suggest that there is no systematic difference between undergraduate and graduate students.
Designing a Project That Every Student Can Fit In But Requiring Only a Team to Complete
There are two ways to design a group project for students: (a) a very hard problem requiring a group discussion and (b) a complex problem requiring assembling works from many students. I choose (b) to design a project for students in my class. Because when students come from different grades or experiences, (b) can make every student contribute something to a project. (a) is more suitable for students with similar backgrounds or experiences. The goal is to make students feel that a complex problem is in fact an assembly of a series of small problems. In the group project, some students did the programming work, some wrote technical reports, and some gave presentations. Each group will present their work via reports and presentations. Hence, there is also competition between groups. A mixture of collaborative and competitive environments can keep students motivated.
- Highly motivated undergraduate students selected this course: This is the first Computational Biology course at CSU. For graduate students, most students select it because of the research needs. But for undergraduate students, it is an option. They all understand the “risk” (e.g., interdisciplinary course) to select this course. The reason they selected this course is because they wanted to learn Computational Biology. If you are motivated, you can do great.
- Study hard because of the pressure: Undergraduate students feel pressured when they are studying with graduate students. The exam is the same for all students. To succeed in this class, undergraduate students need to study harder. Computational Biology is new to everyone. A student’s performance is based on what they learn in the class and not on their prior knowledge.
- Study with a group: When undergraduate and graduate students study together in the same group, it creates a classroom community and builds a strong classroom culture of working together. It helps undergraduate students grasp the material and learn effectively.
Overall, Undergraduate and Graduate students can perform well in Computational Biology if they stay motivated, study hard, and study collaboratively. This course is challenging for all students because it involves a variety of areas that they normally struggle with alone (applied mathematics, software engineering, statistics, and molecular biology). This course provides new knowledge that may be unfamiliar to all students enrolled. Everyone can succeed in this class if they are highly motivated and work hard, regardless of their enrollment status as an Undergraduate or Graduate student.