ORIGINAL PAPER Olivier D.T. Sessink Hendrik H. Beeftink Rob J.M. Hartog Johannes Tramper Virtual parameter-estimation experiments in Bioprocess-Engineering education Received: 9 December 2005 / Accepted: 15 December 2005 / Published online: 13 January 2006 Ó Springer-Verlag 2006 Abstract Cell growth kinetics and reactor concepts constitute essential knowledge for Bioprocess-Engi- neering students. Traditional learning of these concepts is supported by lectures, tutorials, and practicals: ICT offers opportunities for improvement. A virtual-experi- ment environment was developed that supports both model-related and experimenting-related learning objectives. Students have to design experiments to esti- mate model parameters: they choose initial conditions and ‘measure’ output variables. The results contain experimental error, which is an important constraint for experimental design. Students learn from these results and use the new knowledge to re-design their experi- ment. Within a couple of hours, students design and run many experiments that would take weeks in reality. Usage was evaluated in two courses with questionnaires and in the final exam. The faculties involved in the two courses are convinced that the experiment environment supports essential learning objectives well. Keywords Modeling education Virtual experiment Experimental design Introduction Cell growth kinetics and reactor concepts constitute essential knowledge for Bioprocess-Engineering stu- dents. In two courses at Wageningen University, cell growth kinetics are described with the Monod kinetics, while the consumption of substrate is described with the Pirt linear growth law. Traditionally, students learn about theoretical models in lectures and tutorials, and about practical issues in a wet-lab practical. In the lectures and tutorials, the stu- dents spend a considerable amount of time on the cal- culation of solutions to exercises. By solving exercises, students are supposed to discover various characteristics of the models. In wet-lab practicals, students learn many practical aspects of cell culturing and apply the theory to a practical situation. Because cell culturing includes many new procedures for students (e.g., reactor build- ing, medium preparation, sterile sampling, cell counting, etc.), application of theory receives often little attention. Among staffs there was a general agreement on two opportunities for the improvement of our traditional learning support. First, the BSc-graduates’ understand- ing of the integration of kinetics and reactor concepts and how theoretical models relate to reality could be improved. This opportunity is outlined under ‘‘Model- related learning objectives’’. Second, BSc-graduates’ understanding of how experiments relate to modeling could be improved as well. This opportunity is outlined under Experimenting-related learning objectives. This article discusses a virtual-experiment environ- ment that was developed to exploit these opportunities. The virtual-experiment environment was developed for two courses: ‘‘Introduction to Process Engineering’’ early in the second year of the BSc of students both in Food Science and Technology and Biotechnology, and ‘‘Bioprocess-Engineering’’ late in the second year of the BSc of students in Biotechnology. The virtual-experiment environment should comply with generally accepted learning and instruction princi- ples. First, retention and retrieval of knowledge improve when students actively elaborate on the knowledge [1]. The learning process is best supported if the elaboration with the experiment environment is well structured [2]. Second, the cognitive processing capacity of the student is a limiting factor in learning. By minimizing the cog- O. D.T. Sessink (&) H. H. Beeftink J. Tramper Food and Bioprocess Engineering Group, Wageningen University, P.O. Box 8129 6700 EV, Wageningen, The Netherlands E-mail: Olivier.Sessink@wur.nl Tel.: +31-317-483229 Fax: +31-317-482237 R. J.M. Hartog Wageningen Multimedia Research Centre, Wageningen University, Wageningen, The Netherlands Bioprocess Biosyst Eng (2006) 28: 379–386 DOI 10.1007/s00449-005-0042-z