The primary goal of this engagement was to bridge the gap between theoretical instruction and practical research application in computational biology and bioinformatics.
Specific objectives included:
Enhancing research quality by reviewing laboratory protocols and project methodologies.
Guiding undergraduate students in research design, data handling, and result interpretation.
Integrating bioinformatics tools and data analysis techniques into the university’s ICT curriculum.
Promoting interactive learning and problem-solving approaches for scientific computing.
Successfully redesigned course modules to emphasize bioinformatics and computational biology principles.
Improved research engagement and performance among undergraduate students through targeted mentorship.
Enhanced the quality of laboratory reports and data-driven analyses in student projects.
Contributed to the university’s transition toward data-centric academic instruction in life sciences and technology.
This experience played a crucial role in fostering a data-driven academic culture at Lead City University, aligning ICT education with the growing demand for computational literacy in biological research.
The initiative helped:
Strengthen the research capacity of undergraduate students.
Integrate bioinformatics as a core analytical skill in ICT-related programs.
Inspire collaborative innovation between computing and life sciences disciplines.
Programming Languages: R, Python, Unix shell scripting
Data Analysis Tools: SPSS, Excel, and MATLAB
Bioinformatics Skills: Sequence analysis, data integration, and visualization
Curriculum Development: Course design, assessment frameworks, and learning evaluation.
