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Journal articles on the topic 'Educational Algorithms'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Petrosyan, H., and H. Petrosyan. "EDUCATIONAL RESEARCH EXPLORATION TECHNOLOGIES IN VOCATIONAL EDUCATIONL INSTITUTIONS." Main Issues Of Pedagogy And Psychology 1, no. 1 (2013): 27–34. http://dx.doi.org/10.24234/miopap.v1i1.342.

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The paper represents the formation of professional competence of students by implementing exploratory research technology in vocational educational institutions, as well as the specific algorithm lesson in research training and some models are characterized by the use of learning processes investigation, project methods, problem–based learning algorithms.

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Priyanka, Saini. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 1, April (2021): 31–41. https://doi.org/10.5281/zenodo.5105645.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Priyanka, Saini1 Sweta Rai2 and Ajit Kumar Jain3 1. 2. M.Tech Student Banasthali University Tonk Rajasthan. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 01, dec (2014): 01–11. https://doi.org/10.5281/zenodo.1450276.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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B. Siyambalampitiya, Sarath. "Educational Management: Timetable Scheduling Algorithms." i-manager's Journal of Educational Technology 2, no. 4 (2006): 45–51. http://dx.doi.org/10.26634/jet.2.4.759.

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Kim, Young-Hoon, and Kyoung-Eun Yang. "Exploring Social Computing Approaches for Future Education and Convergence Education with Social Algorithm Perspectives." Korean Journal of Teacher Education s (January 31, 2023): 5–20. http://dx.doi.org/10.14333/kjte.2023.39.s.01.

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Purpose: This paper discusses convergence education and future education perspectives of computing and algorithms applied to humanistic and social sciences with artificial intelligence. These algorithms are referred to as social algorithms. Methods: This paper considers the social scientific implications of social algorithms from an educational perspective, examining the issues and topics associated with the use of these algorithms for educational practices. Results: This discussion of social algorithms explicitly focuses on examples and case studies of educational practices rather

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Chojak, Małgorzata. "Algorithms in the Educational Process – Opportunities and Limitations." Lubelski Rocznik Pedagogiczny 43, no. 4 (2025): 75–89. https://doi.org/10.17951/lrp.2024.43.4.75-89.

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Introduction: Algorithm application is now a common topic in scientific and popular science publications. More and more practitioners and theoreticians associated with teaching, upbringing, and therapy are turning to it, hoping to make their activities more effective and improve their organization. Research Aim: This article presents the possibilities and limitations of introducing algorithms to the educational process. Evidence-based Facts: Over the past years, the number of publications dedicated to AI and the process of algorithmization, has increased significantly. Defining the concept of

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Wothke, Werner, George Burket, Li-Sue Chen, Furong Gao, Lianghua Shu, and Mike Chia. "Multimodal Likelihoods in Educational Assessment." Journal of Educational and Behavioral Statistics 36, no. 6 (2011): 736–54. http://dx.doi.org/10.3102/1076998610381400.

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It has been known for some time that item response theory (IRT) models may exhibit a likelihood function of a respondent’s ability which may have multiple modes, flat modes, or both. These conditions, often associated with guessing of multiple-choice (MC) questions, can introduce uncertainty and bias to ability estimation by maximum likelihood (ML) when standard Newton solutions are used. This article evaluates the performance of several maximization methods, including initial (grid) searches probing the function slopes, simulated annealing, exhaustive likelihood evaluation, and the standard N

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Cai, William, Josh Grossman, Zhiyuan Jerry Lin, et al. "Bandit algorithms to personalize educational chatbots." Machine Learning 110, no. 9 (2021): 2389–418. http://dx.doi.org/10.1007/s10994-021-05983-y.

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Domínguez Figaredo, Daniel. "Data-driven educational algorithms pedagogical framing." RIED. Revista Iberoamericana de Educación a Distancia 23, no. 2 (2020): 65. http://dx.doi.org/10.5944/ried.23.2.26470.

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Los datos procedentes de los estudiantes y de las prácticas de aprendizaje son esenciales para alimentar los sistemas de inteligencia artificial empleados en educación. Asimismo, los datos generados recurrentemente son fundamentales para entrenar los algoritmos, de manera que puedan adaptarse a nuevas situaciones, ya sea para mejorar el ciclo de aprendizaje en su conjunto o para gestionar tareas repetitivas. A medida que los algoritmos se propagan en diferentes contextos de aprendizaje y se amplía su capacidad de acción, se requieren marcos pedagógicos que ayuden a interpretarlos y que amparen

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Janota, Ales, V. ŠimÁk, and J. Hrbček. "Learning Search Algorithms: An Educational View." TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation 8, no. 4 (2014): 565–70. http://dx.doi.org/10.12716/1001.08.04.11.

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Koldehofe, Boris, Marina Papatriantafilou, and Philippas Tsigas. "Distributed algorithms visualisation for educational purposes." ACM SIGCSE Bulletin 31, no. 3 (1999): 103–6. http://dx.doi.org/10.1145/384267.305884.

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Ying-Hong Liao and Chuen-Tsai Sun. "An educational genetic algorithms learning tool." IEEE Transactions on Education 44, no. 2 (2001): 20 pp. http://dx.doi.org/10.1109/13.925863.

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Domínguez, Figaredo Daniel. "Data-driven educational algorithms pedagogical framing." RIED. Revista Iberoamericana de Educación a Distancia 23, no. 2 (2020): 65–84. https://doi.org/10.5281/zenodo.10200652.

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Data from students and learning practices are essential for feeding the artificial intelligence systems used in education. Recurrent data trains the algorithms so that they can be adapted to new situations, either to optimize coursework or to manage repetitive tasks. As the algorithms spread in different learning contexts and the actions which they perform expand, pedagogical interpretative frameworks are required to use them properly. Based on case analyses and a literature review, the paper analyses the limits of learning practices based on the massive use of data from a pedagogical approach

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Amartya Ghosh. "Evaluating Clustering Algorithms for Educational Performance." Dandao Xuebao/Journal of Ballistics 37, no. 1 (2025): 132–37. https://doi.org/10.52783/dxjb.v37.184.

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Analyzing student performance is a vital undertaking within the realm of educational data mining (EDM). This process empowers academic institutions to uncover significant trends, pinpoint students who may be struggling, and formulate effective support strategies. This scholarly article delves into the application of clustering methodologies to classify students based on various performance metrics, such as academic grades, attendance records, engagement levels, and involvement in extracurricular activities. By segmenting students into distinct groups, educators can gain a clearer understanding

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Dyulicheva, Y. Yu. "The swarm intelligence algorithms and their application for the educational data analysis." Open Education 23, no. 5 (2019): 33–43. http://dx.doi.org/10.21686/1818-4243-2019-5-33-43.

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The purpose of the paper is the investigation of the modern approaches and prospects for the application of swarm intelligence algorithms for educational data analysis, as well as the possibility of using of ant algorithm modifications for organizing educational content in adaptive systems for conducting project seminars.Materials and methods. The review of the modern articles on the educational data analysis based on swarm intelligence algorithms is provided; the approaches to solving problem of the optimal learning path construction (optimal organization of the learning objects) based on the

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Lungu, Alexandru-Ioan, Vlad Teodorescu, Andrei Zaborila, Oana Andrei, and Dorel Lucanu. "Alk: A Formal-Methods-based Educational Platform for Enhancing Algorithmic Thinking." Scientific Annals of Computer Science XXXIV, no. 1 (2024): 39–66. https://doi.org/10.47743/SACS.2024.1.39.

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Algorithm design courses are fundamental to computer science curricula, but fostering algorithmic thinking in students is challenging due to the diverse skills and creativity required. Dedicated teaching support tools can help both course instructors and students in this effort. We have developed the Alk platform to promote algorithmic thinking, leveraging the theoretical foundations of Matching Logic. Alk features an intuitive algorithm language that provides a flexible computational model suitable for analysis, symbolic execution, and checking properties of algorithms. In this paper, we pres

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Fahmi, Ismu Rijal, and Dwi Joko Suroso. "A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes." Journal of Robotics and Control (JRC) 3, no. 1 (2021): 48–54. http://dx.doi.org/10.18196/jrc.v3i1.12241.

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The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study

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Agung, Triayudi, and Fitri Iskandar. "Comparison of the feature selection algorithm in educational data mining." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1865–71. https://doi.org/10.12928/telkomnika.v19i6.21594.

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Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely educational data mining (EDM). How the feature selection (FS) algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the FS algorithm from the student dataset. The results received from other FS algorithms and classifi

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Wadhai, Prajwal Ashok. "Algolizer Using ReactJS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30733.

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The Algorithm Visualizer Project is an interactive and educational tool designed to illustrate various algorithms' functionality and efficiency through visual representations. Algorithms are fundamental to computer science, but their abstract nature can be challenging to comprehend. This project aims to bridge that gap by providing a user-friendly interface that visually demonstrates algorithms in action. The visualizer offers a platform where users can select from a range of algorithms, such as sorting (e.g., Bubble Sort, Merge Sort). Each algorithm is showcased step-by-step, allowing users t

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Tang, Wenkai, Shangqing Shi, Zengtong Lu, Mengying Lin, and Hao Cheng. "EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems." Biomimetics 10, no. 3 (2025): 176. https://doi.org/10.3390/biomimetics10030176.

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The Educational Competition Optimizer (ECO) is a newly proposed human-based metaheuristic algorithm. It derives from the phenomenon of educational competition in society with good performance. However, the basic ECO is constrained by its limited exploitation and exploration abilities when tackling complex optimization problems and exhibits the drawbacks of premature convergence and diminished population diversity. To this end, this paper proposes an enhanced educational competition optimizer, named EDECO, by incorporating estimation of distribution algorithm and replacing some of the best indi

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Xuemei Shi. "Character Data Mining in Educational Scene." Journal of Electrical Systems 20, no. 3 (2024): 57–63. http://dx.doi.org/10.52783/jes.2358.

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Teaching and learning have been completely transformed by the quick growth of information technologies like big data, artificial intelligence, and the Internet of Things. Traditional educational methods can no longer satisfy the demands of modern fast-paced and lifelong learning, making the mining of educational data more urgent. Character mining in images is increasingly applied in educational settings. Artificial intelligence and machine learning algorithms, learning behaviors, such as CNN(Convolutional Neural Networks and RNN()Recurrent Neural Networks), have been used, to predict student p

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Wang, Dong. "Educational data mining: Methods and applications." Applied and Computational Engineering 16, no. 1 (2023): 205–9. http://dx.doi.org/10.54254/2755-2721/16/20230892.

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Educational data mining is a rapidly growing field that applies various statistical and data mining techniques to analyze educational data. This paper provides a general review of the literature on educational data mining, focusing on the methods and applications. Methods used in education data mining include classification and clustering. A classification algorithm is a supervised learning technique that seeks to categorize a given set of data objects into specified categories, build a classification model using the input data that already exists, and then apply the model to categorize new da

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Lopateeva, Olga, Anatoly Popov, Alexey Ovsyankin, and Mikhail Satsuk. "Formation of the schedule at the university with the application of greedy algorithms." Informatization and communication 4 (November 2020): 91–96. http://dx.doi.org/10.34219/2078-8320-2020-11-4-91-96.

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A greedy resource allocation algorithm is understood as an algorithm according to which the resource allocation process can be represented as a sequence of steps. At each step, an optimal, under certain conditions, distribution of a part of the resources occurs, which does not change in the future. The problem of improving the quality of the organization of the educational process in a higher educational institution is solved on the basis of the use of greedy algorithms. A well-designed timetable should ensure an even workload of student groups and faculty. The purpose of this work is to devel

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DMITRIEV, М. Е. "DESIGN OF EDUCATIONAL SOFTWARE IN THE TOOL SYSTEM DOCENS." Herald of Technological University 27, no. 6 (2024): 111–15. http://dx.doi.org/10.55421/1998-7072_2024_27_6_111.

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The work is devoted to the technology of designing algorithms for interactive educational software using the DOCENS tool system. This tool system was created at the Department of Methodology of Engineering Activities of the Kazan National Research Technological University. With its help, teachers who do not have programming skills can independently create and autonomously use interactive software products for various purposes of the educational process of a technological university. Along with the possibility of creating frames with ergonomic feedback understandable to the student, including g

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Crompvoets, Elise A. V., Anton A. Béguin, and Klaas Sijtsma. "Adaptive Pairwise Comparison for Educational Measurement." Journal of Educational and Behavioral Statistics 45, no. 3 (2019): 316–38. http://dx.doi.org/10.3102/1076998619890589.

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Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark.

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I.K., Choriyev. "TECHNOLOGIES AND ALGORITHMS FOR USING ARTIFICIAL INTELLIGENCE IN EDUCATION." International Journal of Pedagogics 4, no. 12 (2024): 240–43. https://doi.org/10.37547/ijp/volume04issue12-51.

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The use of artificial intelligence in education represents a significant step in the evolution of educational technologies, providing new opportunities for personalized learning, improved quality of educational processes and more efficient management of educational institutions.

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Moglan, V. D. "Didactic potential of using systems for visualization of algorithms in the process of teaching programming." Open Education 23, no. 2 (2019): 31–41. http://dx.doi.org/10.21686/1818-4243-2019-2-31-41.

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The article is devoted to the didactic potential on the use of the visualizer of algorithms as software, which graphically demonstrates the work of algorithms for processing input data. There are described the difficulties arising during the study of the fundamentals of algorithmization and programming. The author proposed to use the algorithm visualizer, as an auxiliary visual means of teaching algorithmization, for more successful mastering the mechanism of the algorithms at lectures. The article discusses the functional requirements for the visualizer of algorithms, describes its main eleme

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Bryndin, Evgeniy. "Self-learning AI in Educational Research and Other Fields." Research on Intelligent Manufacturing and Assembly 3, no. 1 (2025): 129–37. https://doi.org/10.25082/rima.2024.01.005.

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There are several areas where self-learning AI is actively used. Machine learning and deep learning allow you to identify patterns and improve performance. Algorithms such as neural networks can adapt and improve based on experience. Self-learning GPTs are used to dialogue with humans. Computer vision recognizes and classifies images. Recommender systems analyze user preferences and offer personalized solutions. Adaptive robotic industrial control systems can optimize processes by adapting to changing conditions and data. Self-learning intelligent systems help detect and respond to new threats

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Abitov, Ruslan Nazilovich, and Rais Semigullovich Safin. "Analysis of the effectiveness of clustering algorithms for multimodal samples using computer simulation of an educational experiment." Science for Education Today 14, no. 2 (2024): 125–51. http://dx.doi.org/10.15293/2658-6762.2402.06.

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Introduction. The article is devoted to the problem of primary data processing of pedagogical experiments having a multimodal character. The purpose of the study is to identify the most effective and universal clustering algorithms for pedagogical experiments. Materials and Methods. The study used the method of modeling a pedagogical experiment. The analysis of 5 clustering algorithms is conducted. The effectiveness of clustering algorithms was evaluated based on the proportion of observations with clustering errors at various tolerance levels and the Jacquard similarity coefficient. Regressio

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Fruchard Muller, Clémentine, Sylvain Sagot, and Jean-Claude Domenget. "Educational Implications of SEO Courses on Algorithm Awareness." Education for Information 41, no. 3 (2025): 199–226. https://doi.org/10.1177/01678329251323453.

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This study investigated how teaching search engine optimization (SEO) influences students’ algorithmic awareness, but also help them distinguish between organic and paid (SEA) results. Pre-course and post-course surveys were conducted with 150 students, and focus group discussions were conducted with five students from three disciplines: business, multimedia, and languages. The SEO courses enabled students to better distinguish between sponsored and organic search engine results and to understand the impact of algorithms. This has led to a more informed consumption of online information, with

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Sariman, Guncel, and Ecir Ugur Kucuksille. "Web Based Educational Tool for Metaheuristic Algorithms." Pamukkale University Journal of Engineering Sciences 20, no. 2 (2014): 46–53. http://dx.doi.org/10.5505/pajes.2014.15870.

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Ambrose, Richard, Samuel Brown, David Wirtschafter, and Graciela Alarcon. "Construction of clinical algorithms for educational programs." Journal of Cancer Education 4, no. 3 (1989): 161–66. http://dx.doi.org/10.1080/08858198909527997.

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Briassoulis, George, Panagiotis Briassoulis, and Efrossini Briassouli. "Educational polymorphisms of basic life support algorithms." Journal of Evaluation in Clinical Practice 17, no. 3 (2010): 462–70. http://dx.doi.org/10.1111/j.1365-2753.2010.01450.x.

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Adeyanju, Tunde Mojeed, Azwa Adbul Aziz, and Suhailan Safei. "A Comparative Analysis of Classification Algorithms on Student Academic Performance." Semarak International Journal of Machine Learning 5, no. 1 (2025): 74–86. https://doi.org/10.37934/sijml.5.1.7486a.

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Understanding the best use of machine learning algorithms, such as decision tree classifiers, in predicting academic performance is crucial for educational institutions to identify at-risk students and implement targeted interventions. It is essential to determine the best algorithm for academic achievement to ensure accurate predictions and effective support mechanisms for students. This study investigated the multifaceted factors influencing academic performance in computer science research, emphasizing the significance of benchmarking prediction and classification algorithms such as decisio

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Adeyanju, Tunde Mojeed, Azwa Adbul Aziz, and Suhailan Safei. "A Comparative Analysis of Classification Algorithms on Student Academic Performance." Semarak International Journal of Machine Learning 5, no. 1 (2025): 74–86. https://doi.org/10.37934/sijml.5.1.7486.

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Understanding the best use of machine learning algorithms, such as decision tree classifiers, in predicting academic performance is crucial for educational institutions to identify at-risk students and implement targeted interventions. It is essential to determine the best algorithm for academic achievement to ensure accurate predictions and effective support mechanisms for students. This study investigated the multifaceted factors influencing academic performance in computer science research, emphasizing the significance of benchmarking prediction and classification algorithms such as decisio

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Zhang, Peng, Zifan Ma, Zeyuan Ren, et al. "Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm." Mathematics 12, no. 8 (2024): 1127. http://dx.doi.org/10.3390/math12081127.

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With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification and archiving methods are inefficient and susceptible to subjective factors. Therefore, an automated method is needed to quickly and accurately classify and archive documents into their respective categories. Based on this, this paper proposes a design of an automatic document classification system for educational reform based on the Naive Bayes a

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Mohamed, Mohamed Hegazy, Sayed Abdelgaber, and Laila Abd-Ellatif. "Enhancing the Performance of Educational Systems using Efficient Opinion Mining Techniques." Journal of Education and e-Learning Research 10, no. 1 (2022): 19–28. http://dx.doi.org/10.20448/jeelr.v10i1.4335.

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Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective of our article is to enhance educational systems through the analysis of student comments, teacher comments and course comments. Furthermore, the proposed model uses a bundling task to make groups of packs for students from its comments. The datasets

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Jun-On, Nipa, and Watcharaporn Cholamjiak. "Enhanced Double Inertial Forward–Backward Splitting Algorithm for Variational Inclusion Problems: Applications in Mathematical Integrated Skill Prediction." Symmetry 16, no. 8 (2024): 1091. http://dx.doi.org/10.3390/sym16081091.

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This paper introduces a new algorithm that combines the forward–backward splitting algorithms with a double inertial technique, utilizing the previous three iterations. The weak convergence theorem is established under certain mild conditions in a Hilbert space, including a relaxed inertial method in real numbers. An example of infinite dimension space is given with numerical results to support our proposed algorithm. The algorithm is applied to an asymmetrical educational dataset of students from 109 schools, utilizing asymmetric inputs as nine attributes to predict the output as students’ ma

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Ashok., M. V., and Hareesh Kumar G. "Educational Data Mining A Blend of Heuristic and K Means Algorithm to Cluster Students to Predict Placement Chance." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 1401–6. https://doi.org/10.31142/ijtsrd18882.

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Educational data mining emphasizes on developing algorithms and new tools for identifying distinctive sorts of data that come from educational settings, to better understand students. The objective of this paper is to cluster efficient students among the students of the educational institution to predict placement chance. Data mining approach used is clustering. Ablend of heuristic and K means algorithm is employed to cluster students based on KSA knowledge, Communication skill and attitude . To assess the performance of the program, a student data set from an institution in Bangalore were col

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Wahyudi, Eko, Rian Farta Wijaya, and Khairul Khairul. "K-Means and Naive Bayes Algorithms for Evaluation of Education Personnel Performance Based on SPMI Standards." sinkron 8, no. 3 (2024): 1872–83. http://dx.doi.org/10.33395/sinkron.v8i3.13890.

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This research compares the K-Means and Naive Bayes algorithms in evaluating the performance of educational staff based on SPMI standards at STMIK Triguna Dharma. The main objective is to identify the effectiveness of the two algorithms in grouping performance evaluation data and determine the advantages and disadvantages of each method. Primary data was obtained through surveys and interviews, while secondary data came from institutional archives. The K-Means algorithm shows 100% accuracy with the ability to group educational staff into very good, good, quite good, poor and poor performance ca

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Astambaeva, Z. K., and А. Е. Zhumabaeva. "WAYS OF DEVELOPMENT OF ALGORITHMIC LITERAC." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (2020): 285–90. http://dx.doi.org/10.51889/2020-1.1728-7901.50.

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In the modern era of Cybernetics, future primary school teachers should use the algorithms they use in everyday life creatively in the educational process. When performing various exercises that are considered in elementary school math lessons, the future specialist must use explicit algorithms himself and correctly implement them when teaching students. In primary school mathematics, future teachers use implicit algorithms such as: an algorithm for solving various types of problems, algorithms for solving complex equations and expressions, an algorithm for constructing certain geometric shape

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Tu, Chunlei, Yanjin Liu, and Lixiao Zheng. "Hybrid Element Heuristic Algorithm Optimizing Neural Network-Based Educational Courses." Wireless Communications and Mobile Computing 2021 (December 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/9581793.

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The rapid development of computer networks has enabled information technology to penetrate many fields, providing unprecedented opportunities for all aspects of our lives. In order to allow students to acquire necessary knowledge and skills through efficient learning, this article studies the design and development of educational technology courses based on hybrid metaheuristic algorithms to optimize neural networks. This paper proposes a metaheuristic algorithm and explains the simulated annealing algorithm and microregular annealing algorithm in detail. Using these algorithms, a mathematical

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Dake, Delali Kwasi, and Charles Buabeng-Andoh. "Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions." Mobile Information Systems 2022 (November 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/2670562.

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Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An at

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Tariq, Muhammad Arham. "A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction." Journal of information and organizational sciences 48, no. 1 (2024): 133–47. http://dx.doi.org/10.31341/jios.48.1.7.

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Education data mining (EDM) applies data mining techniques to extract insights from educational data, enabling educators to evaluate their teaching methods and improve student outcomes. Feature selection algorithms play a crucial role in improving classifier accuracy by reducing redundant features. However, a detailed and diverse comparative analysis of feature selection algorithms on multiclass educational datasets is missing. This paper presents a study that compares ten different feature selection algorithms for predicting student grades. The goal is to identify the most effective feature s

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Aung, Cho, and Si Thu Aung. "Educational Data Analysis by Applied SPSS." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 1378–80. https://doi.org/10.5281/zenodo.3591119.

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SPSS is powerful to analyze Educational data. This paper intends to support educational leaders the benefits of data analyzing with applied SPSS. It showed the data analysis of qualified rates such as bad, neutral, good and very good on the subjects. As SPSS's background algorithms, it showed the cross tabulation algorithm for cross tabulation tables. And then Sample data 'course evaluation.sav' was downloaded from Google and was analyzed and viewed. It used IBM SPSS statistics version 23 and PYTHON version 3.7. Aung Cho | Aung Si Thu "Educational Data Analysis by Applied SPSS

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Ovchinnikov, Alexander, and Mikhail Gitman. "Quality management of educational programs for students with corrective actions based on the negentropic approach." Applied Mathematics and Control Sciences, no. 2 (June 29, 2018): 97–108. http://dx.doi.org/10.15593/2499-9873/2018.2.06.

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The quality management algorithm of educational programs on the basis of a negentropic approach and learning curves is proposed. The mathematical model and algorithms of complex estimation of level of formation of competences of the student and adjusting educational programs to improve the quality of their implementation to the required level is presented.

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Costa. "A Free Educational Java Framework for Graph Algorithms." Journal of Computer Science 6, no. 1 (2010): 87–91. http://dx.doi.org/10.3844/jcssp.2010.87.91.

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D�, Barbara, N. A. bska, and Agnieszka Kubacka. "Classification algorithms in the personalisation of educational portals." International Journal of Continuing Engineering Education and Life-Long Learning 26, no. 1 (2016): 105. http://dx.doi.org/10.1504/ijceell.2016.075041.

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Gonthier, Rumi, and Maureen Habel. "Using Algorithms in Rehabilitation Nursing: An Educational Strategy." Rehabilitation Nursing 19, no. 3 (1994): 134–40. http://dx.doi.org/10.1002/j.2048-7940.1994.tb01571.x.

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Liu, Taotang, Zhongxin Gao, and Honghai Guan. "Educational Information System Optimization for Artificial Intelligence Teaching Strategies." Complexity 2021 (May 6, 2021): 1–13. http://dx.doi.org/10.1155/2021/5588650.

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Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried ou

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