scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

scholarly journals A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

2021 ◽  
Vol 11 (23) ◽  
pp. 11534
Author(s):  
Mostafa Zafari ◽  
Abolghasem Sadeghi-Niaraki ◽  
Soo-Mi Choi ◽  
Ali Esmaeily
Keyword(s):  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Student Performance ◽  
Support Vector ◽  
School Students ◽  
Online Questionnaire ◽  
Factors Affecting ◽  
Original Dataset ◽  
Artificial Neural Network Ann

The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

scholarly journals Student Performance Prediction Model Based on Discriminative Feature Selection

2018 ◽  
Vol 13 (10) ◽  
pp. 55
Author(s):  
Haixia Lu ◽  
Jinsong Yuan
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Model Based ◽  
Density Peaks ◽  
Validation Experiment

It is a hot issue to be widely studied to determine the factors affecting students' performance from the perspective of data mining. In order to find the key factors that significantly affect students' performance from complex data, this paper pro-poses an integrated Optimized Ensemble Feature Selection Algorithm by Density Peaks (DPEFS). This algorithm is applied to the education data collected by two high schools in China, and the selected discriminative features are used to con-struct a student performance prediction model based on support vector machine (SVM). The results of the 10-fold cross-validation experiment show that, com-pared with various feature selection algorithms such as mRMR, Relief, SVM-RFE and AVC, the SVM student performance prediction model based on the fea-ture selection algorithm proposed in this paper has better prediction performance. In addition, some factors and rules affecting student performance can be extracted from the discriminative features selected by the feature selection algorithm in this paper, which provides a methodological and technical reference for teachers, edu-cation management staffs and schools to predict and analyze the students’ per-formances.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuancang Wang ◽  
Jing Zhao ◽  
Qiqi Li ◽  
Naren Fang ◽  
Peicheng Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Pavement Performance ◽  
Support Vector ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Pavement Performance Prediction ◽  
Grey Relation

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

Does the SAT Help Colleges Make Better Selection Decisions?

1985 ◽  
Vol 55 (2) ◽  
pp. 195-220 ◽  
Author(s):  
James Crouse
Keyword(s):  
High School ◽  
High School Students ◽  
Student Performance ◽  
Entrance Examination ◽  
Educational Testing ◽  
College Entrance Examination ◽  
School Students ◽  
Grade Point ◽  
Testing Service ◽  
Educational Testing Service

The College Entrance Examination Board and the Educational Testing Service claim that the Scholastic Aptitude Test (SAT) improves colleges' predictions of their applicants' success. James Crouse uses data from the National Longitudinal Study of high school students to calculate the actual improvement in freshman grade point averages, college completion,and total years of schooling resulting from colleges' use of the SAT. He then compares those predictions with predictions based on applicants' high school rank. Crouse argues that the College Board and the Educational Testing Service have yet to demonstrate that the high costs of the SAT are justified by its limited ability to predict student performance.

scholarly journals Una mirada a los efectos de la pandemia global Covid19: Realidades y desafíos actuales en el Bachillerato y la Educación Superior

2020 ◽  
pp. 30-35
Author(s):  
Pedro Alfonso Guadal Ortiz-Sánchez ◽  
Patricia Gpe. Sánchez-Iturbe ◽  
Pedro T. Ortiz-Y Ojeda ◽  
Limberth Agael Peraza-Pérez
Keyword(s):  
High School ◽  
High School Students ◽  
Rural Communities ◽  
Student Performance ◽  
Online Survey ◽  
School Students ◽  
Internet Service ◽  
Bachelor Degree ◽  
Virtual Classes ◽  
Learning Factors

The COVID-19 health emergency has brought a new and unforeseen situation in education in Mexico and around the world. This report shows the results of an online survey applied to high school (CETYS abbreviated in Spanish) and bachelor degree (TecNM abbreviated in Spanish) students at the Mérida and Tuxtla Gutiérrez campus. Out of 846 students, 501 answered the survey. Most of them live in municipal seat, some of them in rural communities 28.5 are high school students and 71.5% are bachelor degree students. 48.1% of students responded that they would not like to continue receiving classes online and 44.1% that in this modality their learning was the same as face -to- face lessons. They face organizational, technological, pedagogical and, to a lesser extent, information issues. They mainly use the WhatsApp application as well, as Microsoft Teams for synchronous work and virtual interviews, they consider it suitable for their virtual sessions. The responses to the coverage of the educational program were similar, the fifth part reached between 90 and 100%; The students showed irregularities during the setting of this educational modality and the main problem was the failure of the Internet connection, and the least was the lack of sensitivity of the teacher. Objectives: Need to obtain information on the opinion of students regarding their academic development during the school period that occurred during the Covid-19 pandemic, that would allow knowing the problems they are facing and proposing alternatives to support students Methodology:A survey was applied to undergraduate and high school students to find out their opinion regarding their problems in school development, the data were analyzed using predetermined scales and the SPSS program to determine the possible correlation between the mentioned variables. Contribution: Problems related to student performance were verified, most have their own computer and have internet service, they still do not adapt to taking virtual classes and express disapproval to continue with this form of learning, factors to be taken into consideration in the immediate future.

scholarly journals The impact of teacher bonuses on student performance in full-time schools: The case of the Espírito Santo

2021 ◽  
Vol 19 ◽  
pp. 374-385
Author(s):  
Heber Gonçalves Guedes ◽  
Aziz Xavier Beiruth
Keyword(s):  
High School Students ◽  
Student Performance ◽  
Financial Incentives ◽  
Assessment System ◽  
Full Time ◽  
Tobit Regression ◽  
School Students ◽  
Espírito Santo ◽  
Espirito Santo ◽  
The Impact

The goal of the study was to analyze the effect of granting financial incentives to teachers in relation to the performance of students from full-time schools of Espírito Santo. A method of matching was used by means PSM (propensity score matching) and then a Tobit regression to analyze the exam scores of the SAEB (National Basic Education Assessment System) in 2017. The results found showed that there is a positive association and related between the BD (Bonificação por Desempenho) program and the Portuguese and mathematics grades of third-year high school students from full-time schools in Espírito Santo. The work concludes by explaining that the success of the program to the reduction in absenteeism and the decrease in teacher turnover.

scholarly journals Role of FCBF Feature Selection in Educational Data Mining

2020 ◽  
Vol 39 (4) ◽  
pp. 772-778
Author(s):  
Maryam Zaffar ◽  
Manzoor Ahmad Hashmani ◽  
K.S. Savita ◽  
Syed Sajjad Hussain Rizvi ◽  
Mubashar Rehman
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Action Plans ◽  
Factors Affecting ◽  
Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

Sign in / Sign up

Export Citation Format

Share Document