Enhancing complex bioprocess learning through simulation technology and hybrid teaching: A case study in university education.
Davor CotorasPublished in: Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology (2024)
The utilization of computer simulators in university education is progressively being embraced to offer students a practical exposure to industrial bioprocesses. Bioreactor computer simulators hold various advantages over conventional laboratory experiments, such as cost-effectiveness and enhanced safety. The research objective is to assess the effectiveness of integrating bioreactor computer simulators into hybrid teaching to promote active and collaborative learning experiences and evaluate their impact on student participation and understanding. A hybrid strategy combining synchronous, face-to-face, and online teaching has been implemented to enhance the teaching-learning processes in the Industrial Bioprocesses course for Biochemistry students. The simulation software BIOSTAT®T Yeast was used. This software models the production of ethanol with Saccharomyces cerevisiae through batch cultivation and the determination of the k L a value of a bioreactor. In the first simulation activity, students analyzed the software response based on parameter values input by the instructor, while in the second simulation activity, students autonomously used the computer simulator under the primary oversight of the instructor. The survey results indicate that the pedagogical innovation was positively received and significantly motivating for the students. Comparing student satisfaction surveys between the two simulation activities suggests that fostering student autonomy and engagement through simulation technology can improve satisfaction and learning outcomes in bioprocess education.
Keyphrases
- high school
- medical students
- wastewater treatment
- virtual reality
- saccharomyces cerevisiae
- medical education
- healthcare
- quality improvement
- deep learning
- social media
- systematic review
- type diabetes
- physical activity
- metabolic syndrome
- high resolution
- solid phase extraction
- liquid chromatography
- molecularly imprinted