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The undergraduate laboratory plays a pivotal role in science and engineering curricula. However, traditional physical laboratories are resource intensive, and due to these constraints, do not always achieve their diverse set of intended learning outcomes. One way to overcome these limitations is to use alternative modes of delivery, such as virtual or remote laboratories. In a virtual laboratory, students do not interact with real equipment to obtain data, but rather with computer simulations of laboratory or industrial process equipment that produce results that can be obscured by pre-programmed statistical variation. In the most common approach, the virtual laboratory is used as an alternative mode and simulates a similar set of activities as in the corresponding physical laboratory. In a few cases, virtual laboratories have been used to create learning activities with no analog to the university instructional laboratory. The instructional and software design of the virtual laboratories described in this study falls into the latter case and is based on the situated context of a practicing engineer in industry. The cognitive apprenticeship approach used is structured around the task of having students determine the operating parameters for chemical processes for volume production through experimental design, interpretation and iteration. In this sense, the virtual laboratory project simulates what expert engineers do in practice, and ends up looking very different than the physical laboratory at the university. In this paper, a method is presented to characterize student groups’ model development as they proceed through the task situated in the cyber-environment of the virtual laboratory. Data sources include laboratory journals, an initial design memorandum, the final written and oral reports, and experimental records available through the instructor interface. Classifications in the graphical representation of model development include: nature of the model component (quantitative, qualitative, empirical, statistical), utility of the model component (operationalized, modified, abandoned), correctness of the model component, action based on the model component (did it direct the values of input variables for a future run, was a run used to quantify model parameters, was the model qualitatively verified, etc.), and emotional responses to model verification or mismatch. Preliminary results from this model representation scheme are presented for two industrial scale virtual laboratories, one based on a transient biological system and one based on a steady-state chemical system. Different types of qualitative and quantitative models are evident in the students’ solutions and can be generally related to differences in the type of knowledge structures of the physical systems embodied by each of the virtual laboratories. Student groups also show distinct differences in ability to apply schematic and strategic knowledge, and strength in one knowledge type does not necessarily indicate strength in the other. This study is part of a larger project to compare and contrast the nature of learning elicited in the virtual laboratory experience. P ge 15030.2

Representations Of Student Model Development In Virtual Laboratories Based On A Cognitive Apprenticeship Instructional Design