Enhancing student learning with case-based teaching and audience response systems in an interdisciplinary Food Science course

A growing body of research in higher education suggests that teachers should move away from traditional lecturing towards more active and student-focus education approaches. Several classroom techniques are available to engage students and achieve more effective teaching and better learning experiences. The purpose of this paper is to share an example of how two of them – case-based teaching, and the use of response technologies – were implemented into a graduate-level food science course. The paper focuses in particular on teaching sensory science and sensometrics, including several concrete examples used during the course, and discussing in each case some of the observed outcomes.Overall, it was observed that the particular initiatives were effective in engaging student participation and promoting a more active way of learning. Case-base teaching provided students with the opportunity to apply their knowledge and their analytical skills to complex, real-life scenarios relevant to the subject matter. The use of audience response systems further facilitated class discussion, and was extremely well received by the students, providing a more enjoyable classroom experience

Perception and description of premium beers by panels with different degrees of product expertise

The present study compares subjects with varying degrees of product expertise with regards to their ability to provide a sensory profile of beverages. Eight premium beers were evaluated by three different panels using a Napping® test, followed by a descriptive task. Two panels were constituted of consumers, classified according to their self-assessed product expertise into “Novices” (N = 14) and “Enthusiasts” (N = 26). The sensory panel at a large brewery, and a group of master brewers constituted the third panel (“Experts”, N = 15). The Napping® data from the three panels were digitalized using a coordinate system, whereas attributes were entered separately and treated as frequency table crossing products and attributes. The position data were analyzed by Hierarchical Multiple Factor Analysis (HMFA). Partial Least Squares-Discriminant Analysis (PLS-DA) was used to test differences between the three panels with regards to the use of attributes. The HMFA results showed a separation of the samples into two distinct groups on the first dimension, whereas the second dimension highlighted the specificity of two of the samples. RV coefficients between partial configurations obtained from the three panels were all above 0.90, indicating high configurational similarity. In contrast, PLS-DA showed significant differences in the use of attributes, particularly between Experts and Novices, suggesting that product expertise is more associated with descriptive, rather than perceptual, ability