An active, collaborative approach to learning skills in flow cytometry.

Area of Science:

• Biomedical Sciences
• Science Education

Background:

• Traditional science education struggles to effectively teach complex data analysis skills.
• Flow cytometry is a crucial technique in hematology and immunology, but data analysis presents a learning challenge.

Purpose of the Study:

• To develop and evaluate an active, collaborative pedagogical approach for teaching flow cytometry data analysis.
• To assess the impact of this new method on student engagement, confidence, and competency in interpreting flow cytometry data.

Main Methods:

• Undergraduate students and research trainees engaged in hands-on activities using FlowJo software with clinical flow cytometry data.
• Participants designed gating strategies for diagnosing hematological malignancies and quantifying immune cell subsets.
• Student learning and perceptions were assessed via surveys and competency-based tasks.

Main Results:

• The active, collaborative method enabled students to achieve advanced learning outcomes, such as designing gating strategies, which are not possible with traditional methods.
• Student confidence and positive perceptions of flow cytometry were correlated with career interest but not necessarily data analysis ability.
• The pedagogical approach proved beneficial for student understanding and interpretation of complex flow cytometry concepts.

Conclusions:

• Active, collaborative learning is an effective pedagogical strategy for teaching flow cytometry data analysis.
• This approach enhances student comprehension of complex scientific concepts and data interpretation skills.
• The method offers a valuable new way to integrate complex data analysis, like flow cytometry, into science curricula.