Student evaluation of teaching (SET) has steadily, but surely, become an important assessment tool in higher education. Although SET provides feedback on students level of satisfaction with the course and the lecturer, the validity of its results has been questioned. After extensive studies, the factor which is believed to distort the SET results is gender of the lecturer. In this paper, Potthoff analysis is employed to additionally explore whether there is gender bias in SET. Namely, this analysis has been used with great success to compare linear regression models between groups. Herein, we aimed to model the overall lecturer impression with independent variables related to teaching, communication skills, and grading and compare the models between genders. The obtained results reveal that gender bias exists in certain cases in the observed SET. We believe that our research might provide additional insights on the interesting topic of gender bias in SET.
Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups.
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