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ObjectiveCompetitive learning techniques are being successfully used in courses of different disciplines. However, there is still a significant gap in analyzing their effects in medical students competing individually. The authors conducted this study to assess the effectiveness of the use of a competitive learning tool on the academic achievement and satisfaction of medical students.MethodsThe authors collected data from a Human Immunology course in medical students (n = 285) and conducted a nonrandomized (quasi-experimental) control group pretest-posttest design. They used the Mann-Whitney U-test to measure the strength of the association between two variables and to compare the two student groups.ResultsThe improvement and academic outcomes of the experimental group students were significantly higher than those of the control group students. The students using the competitive learning tool had better academic performance, and they were satisfied with this type of learning. The study, however, had some limitations. The authors did not make a random assignment to the control and experimental groups and the groups were not completely homogenous.ConclusionThe use of competitive learning techniques motivates medical students, improves their academic outcomes and may foster the cooperation among students and provide a pleasant classroom environment. The authors are planning further studies with a more complete evaluation of cognitive learning styles or incorporating chronometry as well as team-competition.

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In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.