Assignment is an integral part of the continuous assessment in engineering curriculum. Assignments could be homework problems, in-class quizzes, take-home quizzes, projects, oral presentation, lab exercises, etc. However, writing a home assignment normally is viewed by the students as a routine and insignificant curriculum activity. It is our common observation that very few students are serious about properly completing and submitting take-home assignments and majority indulge in mass copying, making proper grading almost impossible. On the other hand, in-class assignments motivate the students to do in-depth study, discuss and interpret the concepts from the study material. They also facilitate the teacher to interact and remove misconception among the students. In our work, we investigated the impact of class assignment on undergraduate computer engineering students' higher order thinking skills (HOTS). Ninety seven students from two sections of the same computer engineering course participated in this study. The two sections were given both class assignments and take home assignments on chosen topics. Assignments consisted of questions based on HOTS. A knowledge survey was conducted for the students after the assignment submission to know how much confidence they had in solving similar questions in their end semester term work test. The results of independent sample test suggested that there was a significant difference in HOTS of students who took CA compared with students who took HA. Also, the knowledge survey indicated that students who took CA showed higher confidence in solving questions with HOTS and the confidence level increasing as the cognitive level of the questions climb up the Bloom's hierarchy. A qualitative analysis of students' responses to an open-ended question in CA indicated positive and encouraging feedback from students about their perceptions about solving an In-class assignment instead of takehome assignment.
Malaria caused by the Plasmodium parasites, is a blood disorder, which is transmitted through the bite of a woman Anopheles mosquito. With almost 240 million cases mentioned each year, the sickness puts nearly forty percentage of the global populace at danger. Macroscopic usually take a look at thick and thin blood smears to identify a disease or a cause and figure it out what weakens them. However, the accuracy depends upon smear quality and awareness in classifying and counting parasite and non-parasite cells. Manual evaluation, which is the gold standard for diagnosis requires various steps to be performed. Moreover, this process leads to overdue and misguided analysis, even when it comes to the hands of expertise. In our project, we aim at building a robust, minimized reliance of humans, sensitive model for automated analysis of Malaria. A category of deep learning models, namely Convolutional Neural Networks, guarantee especially versatile and advanced outcome with end-to-cease attribute extraction and categorization. The precision, unwavering quality, velocity and cost of the methods utilized for malaria examination are key to the diseases’ cure and ultimate eradication. In this study, we compare the overall performance of pre- trained CNN primarily based DL model as characteristic extractors closer to classifying parasite and non-parasite cells to aid in progressed sickness screening. The highest quality model layers for attribute extraction from the underlying records, is determined experimentally. The dataset has a variety of Parasite and Non-Parasite pictures of blood samples. To achieve accurate outcome, we have selected certain dominating features such as size, color, shape and cell count from the images which will help in the categorization process. Pre-trained CNNs are used as a promising tool for attribute extraction; this can be determined by the outcome of its statistical validation. Given these developments, automated microscopy could be a very good deal in the chase towards a low-priced, effortless, and dependable method for diagnosing malaria
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