Complexity-based attentive interactive student performance prediction for personalized course study planning

2022 ◽  
Author(s):  
Peipei Wang ◽  
Lin Li ◽  
Ru Wang ◽  
Yifan Xie ◽  
Jianwei Zhang
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Study Planning

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 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

An AI based design of student performance prediction and evaluation system in college physical education

2020 ◽  
pp. 1-9
Author(s):  
Zhang Yangsheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Education ◽  
Student Performance ◽  
Internal Structure ◽  
Process Evaluation ◽  
Performance Prediction ◽  
Evaluation System ◽  
Evaluation Model ◽  
Good Effect

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

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