Student Academic Data Research Articles

Finite predicate-driven logic networks method for enhanced education data analysis

The subject matter of the study is intelligent data analysis in the field of academic information. The goal of the study is to create a mathematical model for analyzing students' academic information using the predicate-driven logic networks method, which allows taking into account both logical dependencies and probabilistic transitions between states. To achieve this goal, the following tasks were defined: analysis of the theoretical foundations of the logic networks method and predicate logic, integration of these approaches into a single mathematical model, development of approaches for its application in academic data analysis problems. The research used the methods of mathematical modeling, complex logical analysis, and method for constructing logic networks. The following results were obtained: a theoretical model was developed that integrates the principles of logic networks and predicate logic for analyzing student academic performance; the model accounts for both probabilistic transitions between states and logical dependencies among student parameters; the mathematical model also incorporates logical rules to enhance the accuracy of logical analysis within the academic context. The model was tested on a dataset of student performance, demonstrating its effectiveness in accurately predicting academic outcomes and confirming the validity of the integrated approach. Conclusions. The scientific novelty of the results obtained is as follows: 1) a theoretical model for analyzing student academic data was developed by integrating logic networks and predicate logic, allowing for the simultaneous consideration of probabilistic transitions and logical dependencies among student parameters; 2) the approach enhances the analysis process by incorporating logical rules into the probabilistic framework, providing a more nuanced and accurate tool for analyzing academic data; 3) this combined model offers a novel method for addressing complex logical analysis tasks in educational settings, paving the way for further research and practical applications in intelligent data analysis. The successful testing of the model on actual student data further underscores its potential as a powerful tool in educational data analysis.

Strengthening data integrity in academic document recording with blockchain and InterPlanetary file system

A diploma is a certificate or official document given by a school or college that is useful for continuing education, applying for jobs, and assessing student intelligence. The main problem with diplomas and other academic documents is that many are forged. This study aims to develop a prototype for recording student academic data using blockchain and blockchain and InterPlanetary file system (IPFS). The research stages were conducted with system conceptualization, data modeling, smart contract development, IPFS integration, data transaction development, user interface/user experience (UI/UX) development, and system testing. A blockchain is a permanent information structure formed by data blocks that are interconnected with transaction data blocks before and after it. The transaction data for each block are encrypted using asymmetric cryptography. IPFS is a peer-to-peer network protocol for storing and sharing data in a distributed file system applying the concept of decentralization to make the manipulation more difficult. The results show that student academic data and documents were successfully stored in a blockchain network using smart contracts and IPFS. Blockchain technology, smart contracts, and IPFS strengthen the value of these documents into documents that are safe, difficult to counterfeit, and easy to trace, such that authentication and integration are better preserved.

Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm

This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.

Student Academic Record Systems and their Security Issues

Student academic records are an invaluable resource for universities and colleges worldwide, yet, their safekeeping is still a major concern for many of these institutions. The security measures currently applied to protect academic record systems are deemed unsatisfactory. This paper surveys the security issues relating to students’ academic records delving into existing students’ academic records systems, cybersecurity challenges and measures to deal with the security concerns. The paper proposes a practical security model, the PIEM model, to address these security issues aimed at empowering academic institutions to build and maintain strongly protected academic record systems for capturing, storing and retrieving student academic data. The work underscores the urgent need of educational institutions to exert extra efforts to securely maintain students’ academic records and facilitate easy access and use by the various educational actors.

Educational Data Mining in Outcome-Based Education: An Analysis of Predictive Models for Program Learning Outcome Attainment

Outcome-based Education (OBE) is a student-centered strategy that focuses on students' performance in terms of knowledge, skills, and attitude to address academic gaps. Educational Data Mining (EDM) utilizes artificial intelligence and machine learning to analyze student data and boost academic achievements. Experimenting with student academic data of 397 first-year students of Mehran University of Engineering and Technology, covering nine courses and spanning two semesters, this research proposes a prediction mechanism to help anticipate student academic outcomes at an early stage during their university degree. The aim of this research is threefold. First, an exploration of EDM-based classification to predict OBE-based Program Learning Outcome (PLO) attainment. Second, the investigation of imbalanced class distribution and the benefits of using the Synthetic Minority Over-Sampling Technique on educational data. Third, a comprehensive performance evaluation of eleven classifiers is explored in this research. The evaluation entailed the use of accuracy, Kappa, recall, and precision to assess classifier performance on both balanced and unbalanced class distributions. Although several classifiers were found to be competent in handling educational data for OBE-PLO prediction, the Random Forest exhibited superior performance with an accuracy of 76.88% and a Kappa score of 0.727.

Explore Students' Metacognitive Thinking Process in Solving Compound Polarity Problems Based on The Level of Academic Ability

This study was designed to identify and describe students’ metacognitive thinking process in solving compound polarity based on academic ability level. Two hundred and eighty-four high school students in Indonesia participated in the study. Students are classified into high, middle and low academic ability groups based on field notes, student academic data, compound polarity problem-solving test (CPPST) scores and teacher suggestions. Six selected students from each group were designated as research subjects and then interviewed in depth to explore their metacognitive thinking processes. CPPST answer sheets and interview transcripts were analyzed using summative content analysis and tested for validity by data triangulation. The results showed that the students with high and medium academic ability level carry out metacognitive thinking processes in the dimensions of planning, monitoring and reflection in solving compound polarity problems, where the metacognitive thinking processes of students with high academic ability are more varied than students with middle academic ability. Students with low academic ability only carry out the dimensions of planning and monitoring. Therefore, teachers need to train students' metacognitive skills in solving problems and get used to applying metacognitively in each given problem solving so that students are trained to solve problems systematically, carefully and thoroughly, and manage time properly in order to obtain the expected learning achievement.

MISE EN PLACE D'UN SYSTEME DISTRIBUE DE PUBLICATION DES CURSUS ACADEMIQUES DES ETUDIANTS

The implementation of a distributed system for publishing students' academic courses aims to facilitate and standardize the dissemination of information on students' academic paths.
 The main objective of this system is to provide a secure and reliable platform where students can view their academic history. Relevant information includes degrees earned, grades, research projects, internships completed, publications, and skills acquired.
 To set up such a system, it is essential to take into account several aspects. First of all, the confidentiality and security of student data must be guaranteed. It is necessary to implement strict access control and data encryption mechanisms to protect sensitive information.
 Then, it is important to define a standardized publication framework that allows students to present their courses in a coherent and understandable way for third parties. This may include information presentation models, metadata tags and data interchange standards.
 In addition, collaboration between the various players in the system is essential. Universities should cooperate to share student academic data securely. Employers and other organizations should also be actively involved in using this system to verify and validate student academic background information.
 Finally, it is crucial to put in place data verification and validation processes to ensure the accuracy and credibility of the information published. Academic authorities can play a key role in verifying the validity of degrees and certifications.

PREDIKSI KELULUSAN SISWA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN MACHINE LEARNING

In recent years, there has been a number of students who graduated late at Lubuk Alung 1st State Junior Highschool. This statement is supported by graduation data from Lubuk Alung 1st Satet Junior Highschool. Therefore, it is necessary to predict students’ graduation status to identify which factors influence the student’s graduation, which will also consequently help the school to solve problem more easily. To solve this problem, the researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of the student’s parents. This research retrieved data on student graduation from schools that have been recapitulated. The classification algorithms used to predict students’ graduation are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. The prediction accuracy using the random forest algorithm outperforms the others with a value of 99.5%.

Analisis Layanan Tata Kelola Aplikasi Sistem Informasi Akademik dengan Menggunakan Cobit 5 pada STIK Bina Husada

Analyze the level of capability in the process of implementing information technology governance that runs at STIK Bina Husada according to national standards. The Academic Information System aims to improve the quality of academic services at the Bina Husada College of Health Sciences, including facilitating the management of student academic data such as student data, student grades/grade transcripts, lecturer data, Study Plan Cards (KRS), and Study Results Cards (KHS). . This is the reason that STIK Bina Husada must regularly carry out the process of monitoring, managing and evaluating the level of readiness of IT services. Based on this, Application Evaluation and Auditcarried out using the Cobit 5 framework, while on Quality Testing and Measurementwebsite using the Apache Jmeter tool. The Cobit 5 processes used are: Risk Optimization (EDM03), Security Management (APO13), Risk Management (APO12), Change Management (BAI06), Operations Management (DSS01), Service Requests (DSS02), Problem Handling (DSS03), Service Security (DSS05), Monitoring and Evaluation of Work (MEA01), Internal Control of Monitoring and Evaluation (MEA02). Gap analysis and Maturity Level are intended to obtain Audit results with good maturity. The results obtained from the average value based on the Gap Analysis of 10 Cobit 5 Processes are 1.2 and the Apache Jmeter test showsthroughput of 101,746/minute and the deviation is 3,671.

Decision Support System Design and Development for Determine Graduate Phase of College Students with Naïve Bayes Algorithm Web-Based in Indonesia Institute of Business and Technology

In the course of the lecture period, each student is different, various factors can affect the mental and academic achievement of students which have an impact on their graduation period. This things also impact on the campus of the Indonesian Institute of Business and Technology, until now there is no system that can determine the graduation period of students at the campus, therefore it is necessary to build a decision support system to determine the graduation period of students, especially students in the Informatics Engineering Study Program and Program Study of Computer Systems at the Indonesian Institute of Business and Technology (INSTIKI) using the Naïve Bayes algorithm that utilizes parameters, namely student academic data consisting of work status, Semester Achievement Index (IPS) scores semesters one untill four, and Grade Point Average (IPK). The system designed by using the GaussianNB library in python and website based. From the results of this study, the accuracy of the model made on the system produces an accuracy value of 87.5% with a dataset rule of 800 data and the data is divided and used as test data by 20%, while the accuracy value of classifying 250 test data on the system is 80.8%.

Prediction of Student Graduation Predicts using Hybrid 2D Convolutional Neural Network and Synthetic Minority Over-Sampling Technique

Abstract. With the rapid growth of technology, educational institutions are constantly looking for ways to improve their services and enhance student performance. One of the significant challenges in higher education is predicting the graduation outcome of students. Predicting student graduation can help educators and academic advisors to provide early intervention and support to students who may be at risk of not graduating on time. In this paper, we propose a hybrid 2D convolutional neural network (CNN) and synthetic minority over-sampling technique (SMOTE) to predict the graduation outcome of students.
 Purpose: Knowing the results and how the Hybrid 2D Convolutional Neural Network (CNN) and Synthetic Minority Over-sampling Technique (SMOTE) algorithms work in predicting student graduation predicates. This algorithm uses a dataset based on family background variables and academic data.
 Methods/Study design/approach: This study uses the Hybrid 2D CNN algorithm for the classification process and SMOTE for the minority class over-sampling.
 Result/Findings: The prediction accuracy of the model using SMOTE is 96.31%. Meanwhile, the model that does not use SMOTE obtains an accuracy of 95.32%.
 Novelty/Originality/Value: This research shows that the use of a Hybrid 2D CNN algorithm with SMOTE gives better accuracy than without using SMOTE. The dataset used also proves that family background and student academic data can be used as a reference for predicting student graduation predicates.

Pembuatan Aplikasi Data Mining Untuk Memperediksi Masa Studi Mahasiswa Menggunakan Algoritma Naive Bayes

Data mining is a series of processes to obtain additional value information that is not known manually from a database. Data mining also manages experience or even mistakes in the past to improve the quality of the analysis model, one of which is the learning ability of data mining techniques, namely classification. Class is a learning task that is a new object into one of the class labels or categories on the old object that has been previously defined. This classification uses one method of data mining algorithm that is Naive Bayes.Naive Bayes algorithm works based on a certain distance between two objects by setting the value of k. The value of k is a parameter to determine the distance between the new object to the old object.By using the data mining technique, the university can obtain student academic data, namely the Achievement Index (IP) to predict the student's study period.In this data mining application consists of data testing and data training with NIM input.PHP and the database used is MySQL.The results of this data mining application this system can predict the results of the classification of student study period based on GPA 4 first semester, the average value of high school time, and grades in high school.

Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

The punctuality of students in completing their studies is an important aspect of the study program. Because there are still students who have not been able to complete their studies on time. The purpose of this study is to determine the factors that influence students in completing their studies by extracting student academic data to obtain a classification model that can be used to predict the accuracy of the study period. The classification method for predicting the accuracy of the student's study period uses the Naive Bayes algorithm using the Feature Forward Selection and SMOTE. The method for data processing in this study uses CRISP-DM. The results of this study are in the form of a classification model to predict the accuracy of the study period of students who obtain a fairly high accuracy value of 87.13%, a recall value of 83.82%, and a precision value of 89.76%, and an AUC value of 0.92. included in the category of Excellent Classification. The use of SMOTE has succeeded in handling Imbalanced Class on the data, and the application of Feature Forward Selection resulted in 5 factors that most influence the accuracy of the student's study period, namely the attributes of Gender, School Category, Year of Entry, Study Program and Grade Point Average for the third semester. The prediction model generated using the Naïve Bayes algorithm, Feature Forward Selection, and SMOTE is expected to help study programs to find out earlier the possibility of students completing their studies on time or not on time.

Sistem Informasi Akademik Berbasis Web Pada Sekolah Menengah Kejuruan Negeri 2 Kota Bengkulu

The development of technology is currently very influential in the world of education. Inthis era of ever-increasing technology, it is required to work quickly and precisely. Likewise in the world of education, especially in vocational high schools, it is very necessary to have technology that is able to work in the field of school operations and for the smooth activities of students and teachers. Academic information system is a system built to assist educational institutions in managing student academic data, such as teacher data, student data, grade data, and other academic data. This system is used to make it easier to manage and display information related to academics. State Vocational High School 2 Bengkulu City is an institution engaged in the field of education that has utilized technology such as computers. However, at SMK Negeri 2 Bengkulu City, there is still no web-based academic information system. This study aims to create an Academic Information System at SMK Negeri 2 Bengkulu City. The research method used is the method of system design and development, such as the design of Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Flowcharts, Databases (Files), Menus, Interfaces (Input/Output). Academic information system is made using CodeIgniter Framework, Hypertext Preprocessor (PHP) and MySQL database. The result of the research is that the academic information system created can assist in simplifying the data collection process for teachers, students, classes, making lesson schedules and assessments using a website-based academic information system.

Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering

Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test, the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students.

SISTEM INFORMASI PENGOLAHAN DATAAKADEMIK SISWA BERBASIS WEB PADA SMA BUDI AGUNG

The information system application program used by Budi Agung High School to process student academic data to store all school academic data so the data will be stored neatly in the database so it will be easy to access easily, quickly, precisely, and accurately. This website also has a decision support system that can helping schools in prospective scholarship program with the Simple Additive Weighting selection method. This website is well designed using PHP programming language and uses MySQL and phpMyAdmin as database. The testing using whitebox method. The method used in developing software is the Rapid Application Development method. The results obtained are SMA Budi Agung uses a website-based application program to obtain information about student data, teacher data, class data, announcement data, grade data, attendance data, and schedule data in the form of reports. Admin also has full access rights to add, update, and delete existing data in the database.

WEB-BASED STUDENT THESIS INFORMATION SYSTEM OF ELECTRICAL ENGINEERING DEPARTMENT TRUNOJOYO UNIVERSITY MADURA

The management of student academic data in thesis files in the Electrical Engineering Department, Trunojoyo University Madura (UTM) is still manual, resulting in a waste of time and energy in delivering information. In this study, the current system flow in UTM electrical engineering is described, then carried out by designing an effective system. The system design consists of a database design process. The system design consists of the database design process, web-based user interface design, and HTML creation (HyperText Markup Language). Designing a web-based thesis information system is the purpose of this study. It is displayed through a 50” inch monitor using a mini PC to present thesis information, progress in thesis work, details of the load supervisors, facilities that support students, and admins in making thesis administrative completeness. Later the system is designed to have a database that is useful for storing data and a web-based user interface to enter and retrieve the required data to be accessed anywhere, by several users en masse via an internet connection. The information presented is up to date.

INTEGRATION OF STUDENT ACADEMIC INFORMATION SYSTEMS WITH TELEGRAM BOT AS AN AUTOMATIC ANSWERING MACHINE

The Student Academic Information System (SIAMA) is web-based software developed to manage students' academic data. In this system, student academic data is processed to make academic information useful for students. However, to access the system requires many steps so that time is less efficient if you have to browse through all the stages. Not to mention the need for internet data quotas that are calculated based on how much content the system downloads and uploads. To speed up access to that information, it can be done by integrating the Telegram Bot service as an automated answering service controlled by robots. Using the PPDIOO method, SIAMA has been successfully integrated as an automated answering machine through the utilization and development of the Telegram Bot PHP Library collaborated with Crontab Linux. From the test results, it is known that the response time from the Bot is indicated at the same hour and minute or < 1 minute which means that the bot execution time is relatively fast.

Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

Perancangan Sistem Informasi Pengolahan Data Akademik Pada Pendidikan Anak Usia Dini Berbasis Website

The use of information technology, especially in education, is very important today, because almost all educational institutions use technology to support the performance of employees and teachers. Academic data processing in educational institutions, especially in early childhood education needs to be considered because currently in implementing student data processing is still done manually. Manual data collection is considered quite slow because it requires very high accuracy to reduce the occurrence of errors, duplication of data, and to maintain the accuracy of the data. Data recording and reports on ledgers often experience loss because each year the data continues to increase and it is difficult to find and require long student data. The solution to the problems that occur in early childhood education is to build a website-based academic data processing information system that can adjust to needs and overcome problems that occur. So, the purpose of this study is to design a website-based academic data processing information system to facilitate PAUD in processing student academic data. In developing this system using the prototype method and the programming language PHP. The results of the study show that the use of the prototype method is very helpful in the design process where it functions in the relationship between the user and the designer in order to know what is needed so that the application built will be appropriate and can function as a medium of information and communication to parents of students with schools and to help PAUD processing student data