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.
The student's academic development, retention, and attainment gap are considered as the common key factors that influence the institutional academic performance. In this regard, educational institutions are focusing to reduce the attainment gap between good, average, and poor performing students. Two different datasets are taken for this study. Students' data is collected through questionnaire, and Dataset 1 (D1) is created. The second dataset (D2) is taken from the repository. Both the datasets have been preprocessed followed by attribute selection and predictive modeling. In this study, predictive models have been built, and the learners are classified as high, average, and low performers based on their academic scores as well as on their demographic characters. The three classifier models are applied on the datasets. Based on the evaluation measures, the best classifier is identified. This early identification of low performance students will help the educators as well as the learners to put a special care to enhance the learning process as well as to improve the academic performance.
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.
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