Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction

2021 ◽  
pp. 408-423
Author(s):  
Thanh-Nhan Huynh-Ly ◽  
Huy-Thap Le ◽  
Nguyen Thai-Nghe
Keyword(s):  
Deep Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Matrix Factorization

scholarly journals Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

2020 ◽  
Vol 9 (3) ◽  
pp. 2058-2066
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Matrix Factorization ◽  
Intelligent Tutoring Systems ◽  
Mean Squared Error ◽  
Mutual Influence ◽  
Intelligent Tutoring ◽  
Essential Elements ◽  
Tutoring Systems ◽  
Predicting Student Performance

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

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.

Deep learning and non-negative matrix factorization in recognition of mammograms

2017 ◽  
Author(s):  
Bartosz Swiderski ◽  
Jaroslaw Kurek ◽  
Stanislaw Osowski ◽  
Michal Kruk ◽  
Walid Barhoumi
Keyword(s):  
Deep Learning ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals Preference Cognitive Diagnosis for Student Performance Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 219775-219787
Author(s):  
Peichao Jiang ◽  
Xiaodong Wang
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Cognitive Diagnosis

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

The Best Ensemble Learner of Bagged Tree Algorithm for Student Performance Prediction

2020 ◽  
Author(s):  
Afiqah Zahirah Zakaria ◽  
Ali Selamat ◽  
Hamido Fujita ◽  
Ondrej Krejcar
Keyword(s):  
Feature Selection ◽  
Student Performance ◽  
Performance Prediction ◽  
Early Prediction ◽  
Massive Datasets ◽  
Tree Algorithm ◽  
Prediction Technique ◽  
Responsible Person

Student performance is the most factor that can be beneficial for many parties, including students, parents, instructors, and administrators. Early prediction is needed to give the early monitor by the responsible person in charge of developing a better person for the nation. In this paper, the improvement of Bagged Tree to predict student performance based on four main classes, which are distinction, pass, fail, and withdrawn. The accuracy is used as an evaluation parameter for this prediction technique. The Bagged Tree with the addition of Bag, AdaBoost, RUSBoost learners helps to predict the student performance with the massive datasets. The use of the RUSBoost algorithm proved that it is very suitable for the imbalance datasets as the accuracy is 98.6% after implementing the feature selection and 99.1% without feature selection compared to other learner types even though the data is more than 30,000 datasets.

Personalized Student Performance Prediction Using Multivariate Long Short-Term Memory

2021 ◽  
pp. 238-247
Author(s):  
Tran Thanh Dien ◽  
Pham Huu Phuoc ◽  
Nguyen Thanh-Hai ◽  
Nguyen Thai-Nghe
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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