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Owing to an exponential increase in the number of courses offered online, it is crucial to understand this mode of delivery on a deeper level. In this study, associations among course performance, the use of online resources (i.e., online homework assistance, practice questions and practice tests), mode of delivery (online versus in-person), and session length (Fall/Winter for 8 months versus Spring/Summer for10 weeks) were examined. Archival data were used from an educational website for an introductory statistics course at a medium-sized Canadian university. Anonymized data were retrieved from 738 students enrolled in the course between 2018 and 2021. Course performance was measured by final course grades and use of resources was assessed in terms of the number of site visits and downloads. It was found that use of online resources was significantly and positively correlated with course performance. However, session length and mode of delivery did not yield significant differences in terms of final course grades. Future studies could examine potential moderators in the relationships between the use of resources with the session length, the delivery method, and course performance to see the effectiveness of the resources in various course delivery models (in-person, hybrid, synchronous online, asynchronous online, etc.). [ABSTRACT FROM AUTHOR]

Artificial neural networks are being used in many fields for different purposes. Medical diagnosis is one of the major purposes. In the field of medical, classification plays an important role as the main aim of the doctor is to classify whether a person is suffering from the disease or not. The objective of this paper is to evaluate neural network for the detection of alopecia in human beings and to find the accuracy. With the help of proposed model, the clinical experts will be able to get a second opinion that will help them to take proper decisions for diagnosing the presence of this disease in patients. This second opinion is crucial due to many factors while doing disease identification. These factors include increased population, environmental pollution, growing demands for proper medication, and less availability of medical experts to cope up with this increasing demand. Also, the dynamic nature of disease symptoms plays an important role in correct diagnosis of a certain disease. The proposed system uses a feedforward artificial neural network and backpropagation algorithm to classify patients with alopecia and without alopecia. The evaluation of the proposed system is done with the help of performance plot as well as regression plot. Experimental results show that the accuracy of proposed system is 91% which is reliable enough for a clinical expert to make his decisions. [ABSTRACT FROM AUTHOR]