Multifactor Naïve Bayes Classification for the Slow Learner Prediction Over Multiclass Student Dataset

Abstract

The high school students must be observed for their slow learning or quick learning abilities to provide them with the best education practices. Such analysis can be perfectly performed over the student performance data. The high school student data has been obtained from the schools from the various regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300 students obtained from one school in the regions has been undergone the test using the proposed model in this paper. The proposed model is based upon the naïve bayes classification model for the data classification using the multi-factor features obtained from the input dataset. The subject groups have been divided into the two primary groups: difficult and normal. The classification algorithm has been applied individually over data grouped in the various subject groups. Both of the early stage classification events have produced the almost similar results, whereas the results obtained from the classification events over the averaging factors and the floating factors told the different story than the early stage classification. The proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input data. The proposed model can be considered as the effective classification model when evaluated from the results described in the earlier sections.

Opinion mining for user experience evaluation model using kernel-naive bayes classification algorithm

“…The ML framework is integrated with the opinion-mining task to evaluate the UX model [20]. The naïve bayes classifier is used for slow learner prediction and Swati (2016) discussed it [16]. The statistical analysis is performed by MCMC technique and it a modelled with the graph approach.…”