Abstract
The main advancement in the field of engineering education is learning somewhat which is based on the problem. This term surely applied to any learning environment in which students drive learning. It is presented in such a way that students understand the problem before moving toward its solution and accordingly they need to gain new information. For the fourth year of the Computer Science and Engineering degree curriculum, this research introduces the knowledge of a formal problem-based learning procedure for educating a preliminary research component in the naive Bayesian method of machine learning. At the beginning of the module, the Naïve Bayes Algorithm design problem was introduced to students. For seven weeks, a small crowd of undergraduates were operated for this assignment, at the same time the instructor served as the information acquisition facilitator. Every week, brief, written information was composed as the learner evaluation, so that the learning environments were ensured. Due to COVID-19, most of the offline classes were suspended so this PBL experiment and the written reports are conducted /collected through the online mode. A list of guidelines to assist academic interest in pursuing PBL with a similar strategy is outlined in the report.
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Mishra, V.K., Mishra, M., Sheetlani, J., Sah, R.D., Mishra, A., Kurariya, S. (2022). Dynamic Education Background: Procure the Maximum Initiation from PBL for Education Naïve Bayes Algorithm for Machine Learning. In: Sharma, S., Peng, SL., Agrawal, J., Shukla, R.K., Le, DN. (eds) Data, Engineering and Applications. Lecture Notes in Electrical Engineering, vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_10
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