scholarly journals Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

2021 ◽  
Vol 11 (24) ◽  
pp. 11845
Author(s):  
Ansar Siddique ◽  
Asiya Jan ◽  
Fiaz Majeed ◽  
Adel Ibrahim Qahmash ◽  
Noorulhasan Naveed Quadri ◽  
...  
Keyword(s):  
Academic Performance ◽  
Early Stage ◽  
Educational Data Mining ◽  
Secondary Level ◽  
Classification Model ◽  
Student Academic Performance ◽  
Proposed Model ◽  
Ensemble Algorithms ◽  
Fusion Of Classifiers

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

scholarly journals Students' Personal and Academic Attributions of University Withdrawal

1995 ◽  
Vol 25 (2) ◽  
pp. 53-77
Author(s):  
Genevieve M. Johnson ◽  
George H. Buck
Keyword(s):  
Academic Performance ◽  
Undergraduate Students ◽  
Undergraduate Student ◽  
Psychological State ◽  
Full Time ◽  
Apparent Lack ◽  
Canadian Universities ◽  
Student Academic Performance ◽  
Wide Range

A Commission of Inquiry on Canadian University Education recently reported that approximately 42% of full-time undergraduate students who entered Canadian universities in 1985 failed to obtain a degree within five years. While this statistic is startling, perhaps, of greater concern is the apparent lack of interest shown by most Canadian universities in the subject of undergraduate student attrition. As an initial step toward addressing the issue of Canadian university attrition, a conceptual model of undergraduate student withdrawal is proposed. The model is based on the assumption that students are characterized by a wide range of personal and academic variables. Such characteristics interact or co-exist with institutional variables such as campus integration. This interaction results in the quality of student academic performance and the nature of student psychological condition. Poor quality of student academic performance results in institution-initiated undergraduate withdrawal; a variety of psychological variables (e.g., satisfaction, stress) result in student-initiated undergraduate withdrawal. The bases of this model were findings obtained from questioning 498 undergraduate students who had withdrawn from a large Western Canadian university. Personal student characteristics, institutional factors and societal variables frequently emerged as students' attributions of university withdrawal. Student academic performance was validated as the causal factor for institutional-based undergraduate withdrawal and student psychological state appeared critically related to student-based undergraduate withdrawal. From these findings, preadmission counseling, academic and personal student support and an increased commitment to accommodating students are recommended.

scholarly journals Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

2021 ◽  
Author(s):  
Chaka Chaka
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Overview Of Reviews ◽  
Student Demographics ◽  
Student Academic Performance ◽  
Online Databases ◽  
Subject Areas ◽  
Input Variables

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

scholarly journals Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

2021 ◽  
pp. 1-11
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Shamsul Alam ◽  
Md. Mizanur Rahman ◽  
Md. Abdul Matin
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Huge Amount ◽  
Novel Technique ◽  
Student Academic Performance ◽  
Academic Activities ◽  
Proposed Model ◽  
Prediction Systems

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

scholarly journals A novel academic performance estimation model using two stage feature selection

2020 ◽  
Vol 19 (3) ◽  
pp. 1610
Author(s):  
Pamela Chaudhury ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Estimation ◽  
Estimation Model ◽  
Proposed Model ◽  
Differential Evolutionary Algorithm ◽  
Performance Estimation Model ◽  
Using Data

<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s  academic performance (poor, average or outstanding).Computer</span>

scholarly journals Unsupervised Characterization and Visualization of Students’ Academic Performance Features

2019 ◽  
Vol 12 (2) ◽  
pp. 103
Author(s):  
Udoinyang G. Inyang ◽  
Uduak A. Umoh ◽  
Ifeoma C. Nnaemeka ◽  
Samuel A. Robinson
Keyword(s):  
Academic Performance ◽  
Early Stage ◽  
Educational Data Mining ◽  
First Year ◽  
Self Organizing Map ◽  
Cluster Validity ◽  
Validity Assessment ◽  
Scatter Plots ◽  
Highly Correlated ◽  
The Impact

The large nature of students&rsquo; dataset has made it difficult to find patterns associated with students&rsquo; academic performance (AP) using conventional methods. This has increased the rate of drop-outs, graduands with weak class of degree (CoD) and students that spend more than the minimum stipulated duration of studies. It is necessary to determine students&rsquo; AP using educational data mining (EDM) tools in order to know students who are likely to perform poorly at an early stage of their studies. This paper explores k-means and self-organizing map (SOM) in mining pieces of knowledge relating to the natural number of clusters in students&rsquo; dataset and the association of the input features using selected demographic, pre-admission and first year performance. Matlab 2015a was the programming environment and the dataset consists of nine sets of computer science graduands. Cluster validity assessment with k-means discovered four (4) clusters with correlation metric yielding the highest mean silhouette value of 0.5912.&nbsp; SOM provided an hexagonal grid visual of feature component planes and scatter plots of each significant input attribute. The result shows that the significant attributes were highly correlated with each other except entry mode (EM), indicating that the impact of EM on CoD varies with students irrespective of mode of admission. Also, four distinct clusters were also discovered in the dataset by SOM &mdash;7.7% belonging to cluster 1 (first class), and 25% for cluster 2 (2nd class Upper) while Clusters 3 and 4 had 35% proportion each. This validates the results of k-means and further confirms the importance of early detection of students&rsquo; AP and confirms the effectiveness of SOM as a cluster validity tool. As further work, the labels from SOM will be associated with records in the dataset for association rule mining, supervised learning and prediction of students&rsquo; AP.

scholarly journals Assessment of the impact of non-architectural changes in the predictive model on the quality of ECG classification

2021 ◽  
Vol 33 (4) ◽  
pp. 87-98
Author(s):  
Vladislav Valerievich Ananev ◽  
Sergej Nikolaevich Skorik ◽  
Vsevolod Vladislavovich Shaklein ◽  
Aram Arutyunovich Avetisyan ◽  
Yurij Emilevich Teregulov ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Input Data ◽  
Early Stage ◽  
Training Sample ◽  
Classification Model ◽  
Optimal Estimate ◽  
Common Procedure ◽  
Ecg Leads ◽  
The Impact

Recording and analyzing 12-lead electrocardiograms is the most common procedure for detecting heart disease. Recently, various deep learning methods have been proposed for the automatic diagnosis by an electrocardiogram. The proposed methods can provide a second opinion for the doctor and help detect pathologies at an early stage. Various methods are proposed in the paper to improve the quality of prediction of ECG recording pathologies. Techniques include adding patient metadata, ECG noise reduction, and self-adaptive learning. The significance of data parameters in training a classification model is also explored. Among the considered parameters, the influence of various ECG leads, the length of the electrocardiogram and the volume of the training sample is studied. The experiments carried out show the relevance of the described approaches and offer an optimal estimate of the input data parameters.

An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance

2018 ◽  
Vol 8 (4) ◽  
pp. 67-79 ◽  
Author(s):  
Patrick Kenekayoro
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Academic Performance ◽  
Support Vector Machines ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Classification Model ◽  
Support Vector ◽  
Student Academic Performance ◽  
Vector Machines

Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.

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