scholarly journals Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1252
Author(s):  
Jie Yang ◽  
Shimin Hu ◽  
Qichao Wang ◽  
Simon Fong
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Attribute Reduction ◽  
Warning System ◽  
Attribute Selection ◽  
Activity Data ◽  
Online Activity ◽  
Teacher Student ◽  
Important Relationship ◽  
Real World Datasets

The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.

scholarly journals A Secure Based Preserving Social Media Data Mangement System

2021 ◽  
Vol 10 (4) ◽  
pp. 210-214
Author(s):  
Geetha V. ◽  
Gomathy C.K. ◽  
Maddu Pavan Manikanta Kiran ◽  
Rajesh, Gandikota
Keyword(s):  
Social Media ◽  
Main Idea ◽  
Data Publishing ◽  
Activity Data ◽  
Online Activity ◽  
Social Media Data ◽  
Private Data ◽  
User Activity ◽  
Real World Datasets ◽  
Media Data

Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.

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 Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 201
Author(s):  
Qinfeng Xiao ◽  
Jing Wang ◽  
Youfang Lin ◽  
Wenbo Gongsa ◽  
Ganghui Hu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Multivariate Data ◽  
Failure Detection ◽  
Superior Performance ◽  
Detection Algorithms ◽  
Teacher Student ◽  
Model Complex ◽  
Unsupervised Anomaly Detection ◽  
Real World Datasets ◽  
Complex Features

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.

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.

Perceptions of Teacher, Student, and Family Factors Influencing Student Performance Measure

2016 ◽  
Author(s):  
Meagan M. Patterson ◽  
Natasha Kravchenko ◽  
Li Chen-Bouck ◽  
Jenna A. Kelley
Keyword(s):  
Student Performance ◽  
Performance Measure ◽  
Family Factors ◽  
Teacher Student ◽  
Factors Influencing

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.

Sign in / Sign up

Export Citation Format

Share Document