scholarly journals Applying Deep Learning Neural Networks in Predicting Students' Cumulative Grades

2020 ◽  
Vol 10 (2) ◽  
pp. 293-297
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Academic Performance ◽  
Deep Learning ◽  
Student Performance ◽  
Quality Education ◽  
Data Set ◽  
Improve Student Performance ◽  
The Difference ◽  
Indirect Information

The distinguished universities aim to provide quality education to their students. One way to achieve the highest quality in university studies is to discover knowledge to predict student performance and grades in courses etc. Recently, the amount of data stored in educational databases is accumulating very quickly, as these databases contain indirect information that can be used to improve student performance. Academic performance is affected by many factors, so it is necessary to predict student performance to determine the difference between students who are excelling in studies and students who need to exert more effort to improve their performance and their level of achievement. Hidden or Indirect knowledge is part of the educational data set and can be extracted using various means, such as data mining techniques and the use of classification, and deep learning through neural networks. This paper has been designed to extract knowledge describing students' performance in the courses required for graduation, in a way that helps academic advisors in providing academic advice and guidance to students to improve their cumulative grades.

scholarly journals The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)

2021 ◽  
Vol 11 (1) ◽  
pp. 26-35
Author(s):  
Yulison Herry Chrisnanto ◽  
◽  
Gunawan Abdullah ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Educational Data Mining ◽  
Strategic Decision ◽  
Data Set ◽  
Study Results ◽  
Mining Model

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.

scholarly journals Prediction of Student Performance using Hybrid Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 6566-6570
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Data Types ◽  
Data Set ◽  
Hybrid Classification

Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

scholarly journals Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology Motivation

2019 ◽  
Vol 8 (S2) ◽  
pp. 83-87
Author(s):  
S. Peerbasha ◽  
M. Mohamed Surputheen
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Data Set ◽  
Knowledge Based ◽  
Data Mining Algorithms ◽  
Data Prediction ◽  
Clustering And Classification

The development of many educational institutions is based on the performance of students learning and understanding capabilities. Here, we analyzed their academic profile with their grades and various cumulative attributes. The academic performance in learning their subjects could be improved by motivational approach. The analysis of student performance is carried out through knowledge-based data mining process. But, the problem is arrived by a probability of information prediction accuracy from student data set which is not accurate. Here, we propose a novel machine learning algorithm based on subspace clustering and multi-perspective classification techniques to identify psychological motivation required students. Also, the extraction of relational patterns to form enhanced clustering classes is done. This discovers the innovative relations between students and their educational performance in the various attributes using surf scale nested clustering approach based on an intelligent predicting system from soft computing processing tasks. This improves the data prediction rate by considering the time factor analysis and complexity to design and develop an efficient clustering algorithm which maximizes the clustering and classification accuracy for improving academic performance.

scholarly journals Prediction of the Academic Achievement of Pupils Using Data Mining Techniques

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1355-1364
Author(s):  
Mokhalad Eesee Khudhur ◽  
Mohammed Shihab Ahmed ◽  
Saif Muhannad Maher
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Academic Success ◽  
Student Performance ◽  
Drop Out ◽  
Data Set ◽  
Perceptron Algorithm ◽  
Improve Student Performance ◽  
Using Data ◽  
Logistic Regression Algorithm

Introduction: During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous. Method: This study employs multiple methods for machine learning to forecast student progress. With its accurate data sample prediction, five integrated classification algorithms have been created to forecast students' academic success (support vectors, decision-making trees algorithm and perceptron algorithm, logistic regression algorithm and a random forest algorithm). Results: Students' academic achievement has been reviewed and assessed. The performance of five learning machines mentioned in Section 4 is discussed here. First, we displayed the data after pre-processing by simply displaying distributions to form the data packet and then evaluated 5 important learning methods and described the variables in the data set. The entire series of 480 characteristics were examined.

The Value of Handwritten Notes: A Failure to Find State-Dependent Effects When Using a Laptop to Take Notes and Complete a Quiz

2020 ◽  
pp. 009862832097989
Author(s):  
Roni M. Crumb ◽  
Ryan Hildebrandt ◽  
Tina M. Sutton
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Incongruent Condition ◽  
Note Taking ◽  
Sole Purpose ◽  
State Dependent ◽  
Improve Student Performance ◽  
Teaching Implications

Background: Many students use laptops in the classroom to take notes; however, even when laptops are used for the sole purpose of taking notes they can negatively impact academic performance. Objective: The current study examined state-dependent effects, and the potential for a match in note taking and quiz taking methods to improve quiz performance. Method: Participants were placed into a congruent (take notes by hand and complete the quiz by hand or take notes using a laptop and complete an online quiz) or an incongruent condition (take notes by hand and take an online quiz or take notes using a laptop and complete the quiz by hand). Results: The results revealed that participants who took notes by hand performed better on the quiz overall, and better on conceptual questions, then students who took notes using a laptop. We failed to find evidence for state-dependent effects. Conclusions: The current study suggests that taking notes by hand may improve how students encode material, and result in higher quality external storage used by students when studying for quizzes. Teaching Implications: Reinforcing the notion that taking notes by hand may benefit quiz performance for lecture-style information and could improve student performance in class.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?

2021 ◽  
Vol 4 ◽  
Author(s):  
Stefano Markidis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Performance ◽  
Critical Role ◽  
Low Frequency ◽  
Accurate Solution ◽  
Limiting Factor ◽  
Data Set ◽  
Linear Solvers ◽  
Computational Performance

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged as a new essential tool to solve various challenging problems, including computing linear systems arising from PDEs, a task for which several traditional methods exist. In this work, we focus first on evaluating the potential of PINNs as linear solvers in the case of the Poisson equation, an omnipresent equation in scientific computing. We characterize PINN linear solvers in terms of accuracy and performance under different network configurations (depth, activation functions, input data set distribution). We highlight the critical role of transfer learning. Our results show that low-frequency components of the solution converge quickly as an effect of the F-principle. In contrast, an accurate solution of the high frequencies requires an exceedingly long time. To address this limitation, we propose integrating PINNs into traditional linear solvers. We show that this integration leads to the development of new solvers whose performance is on par with other high-performance solvers, such as PETSc conjugate gradient linear solvers, in terms of performance and accuracy. Overall, while the accuracy and computational performance are still a limiting factor for the direct use of PINN linear solvers, hybrid strategies combining old traditional linear solver approaches with new emerging deep-learning techniques are among the most promising methods for developing a new class of linear solvers.

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

2019 ◽  
pp. 751-766
Author(s):  
Sajid Umair ◽  
Muhammad Majid Sharif
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Student Performance ◽  
Semantic Analysis ◽  
Vital Role ◽  
Support Vector ◽  
Data Set ◽  
Important Research Topic ◽  
Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

scholarly journals Prediction Model on Student Performance based on Internal Assessment using Deep Learning

2019 ◽  
Vol 14 (08) ◽  
pp. 4 ◽  
Author(s):  
Sadiq Hussain ◽  
Zahraa Fadhil Muhsion ◽  
Yass Khudheir Salal ◽  
Paraskevi Theodoru ◽  
Fikriye Kurtoğlu ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Student Performance ◽  
Recommendation System ◽  
Optimization Method ◽  
Recognition System ◽  
Kappa Statistics ◽  
Immune Recognition ◽  
Classification Methods ◽  
Classification Rate

Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.

Empirical study on the effect of using synthetic attributes on classification algorithms

2017 ◽  
Vol 10 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ali Hasan Alsaffar
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Student Performance ◽  
Software Tool ◽  
Real Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Past Performance ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

Sign in / Sign up

Export Citation Format

Share Document