Abstract Prediction of student dropout in high school is a significant concern in education that affects both a state's education system and its financial system. Early prediction of school student dropout is not an easy issue to resolve since many factors that can influence student retention. The traditional classification techniques were used to solve this problem normally but the higher accuracy was not obtained. In order to improve the accuracy, a novel technique called Tucker's Congruence Regressive Target Feature Matching‐based Tversky Discriminant MIL Boost Data Classification (TCRTFM‐TDMBDC) is introduced. The proposed TCRTFM‐TDMBDC technique consists of four different processes namely data preprocessing, feature extraction, feature selection, and classification. At first, the data preprocessing is carried out for cleaning and altering the raw input data into a valuable and understandable format to minimize the complexity of the classification. After the preprocessing, the feature extraction is carried out by applying Modified Tucker's congruence correlative regression. Thirdly, the feature selection process is performed using Gaussian kernelized target projection feature matching to select the feature subset for accurate classification with minimum time consumption. Finally, the ensemble technique called Tversky Indxive generalized discriminant MIL boost is applied for classifying the given input student data with help of the weak learners. Based on the classification results, the student dropout prediction is accurately performed with minimum time. Experimental results reveal that the proposed technique noticeably predicts student dropout by means of prediction accuracy, precision, recall, F‐measure, and prediction time with respect to the number of student data. The discussed results illustrate that the proposed TCRTFM‐TDMBDC technique achieves higher accuracy with minimum prediction time than the state‐of‐the‐art methods.

Summary Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka–Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS‐BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS‐BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka–Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student‐Drop‐India2016 dataset with different metrics such as prediction accuracy, precision, recall, F‐measure, as well as time. The result of proposed RBOOSEFS‐BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state‐of‐the‐art methods.

Abstract The performance of student in the academic field reveals the consideration over researchers to enhance student's weakness. With the consumption of high potential factors from the dataset, accurate student performance prediction is carried out. Targeted projection pursuit similarity based attribute selection (TPPS‐AS) technique is designed to improve the student academic performance prediction. TPPS is a machine learning technique that observes the given input. The relevant attributes from the multidimensional space is determined by TPPS. Several research works were recognized recently to conclude the high potential factors for observing student academic performances. A novel technique is designed in this research work to improve the student academic performance prediction in a taken dataset by choosing more relevant attributes. To detect the student academic performance with better accuracy and lesser time, proposed TPPS‐AS technique is employed. The performance of student academic performance prediction is improved by TPPS‐AS technique through the attribute selection with higher accuracy. With this proposed technique, prediction accuracy of the student academic performance is increased after the relevant attributes selection process.

Summary Student academic performance prediction plays a major role in the current educational systems to improve the quality of education. The conventional single classifier‐based predictive analysis is not efficient to provide accurate results. In this paper, a novel technique called Minkowski Sommon Feature Map Densely connected Deep Convolution Network with LSTM (MSFMDDCN‐LSTM) is introduced to predict the academic performance of students with higher accuracy and lesser time consumption. The MSFMDDCN‐LSTM technique uses a densely connected deep convolution network to learn the given input for accurate prediction. The student activities are collected and stored in the organization dataset. The MSFMDDCN‐LSTM technique starts with the data collection followed by performing the attributes selection and classification. The collected data are given to input layer to predict the students' academic achievement at the end of study program. Secondly, the importance of numerous dissimilar attributes or “features” is considered for student performance prediction using steepest descent Minkowski sommon mapping. After that, the classification is performed using LSTM to classify the input instances for accurate prediction. Finally, the classification results are observed in the output layer. The quantitative outcomes inferred that MSFMDDCN‐LSTM technique performs well in terms of achieving higher precision, recall, f‐measure, and lesser time consumption than the state‐of‐the‐art methods.