Exploring Data-Driven Curriculum Design and Evaluation Methods in College Biology Instruction

Abstract

This study investigates the integration of data-driven strategies in the design and evaluation of undergraduate biology courses, addressing the critical need for enhanced educational outcomes. The research employs a comprehensive five-stage framework: needs assessment, data collection, data analysis, course development, and evaluation. Initially, a thorough needs assessment identified specific learning objectives and challenges through surveys, interviews, and historical performance data. Data collection encompassed pre-course assessments, exams, class participation, and student feedback. Advanced statistical analyses, including descriptive statistics, regression analysis, and machine learning algorithms, revealed pivotal factors influencing student performance. Leveraging these insights, course development was tailored using the ADDIE model to optimize content and delivery methods. The evaluation phase adopted a mixed-methods approach, blending quantitative metrics with qualitative feedback. A pilot study involving 100 students demonstrated significant improvements, with an average score increase and a 23.2% learning gain, alongside heightened student engagement and satisfaction. These findings underscore the efficacy of data-driven course design in augmenting student learning outcomes, highlighting its potential for widespread adoption in academic settings.