Relevant bibliographies by topics / Educative data mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Educative data mining'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Educative data mining"

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Chen, Jianhui, and Jing Zhao. "An Educational Data Mining Model for Supervision of Network Learning Process." International Journal of Emerging Technologies in Learning (iJET) 13, no. 11 (November 9, 2018): 67. http://dx.doi.org/10.3991/ijet.v13i11.9599.

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To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.
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Зелінська, Сніжана, Альберт Азарян, and Володимир Азарян. "Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment." Педагогіка вищої та середньої школи 51 (December 13, 2018): 263–75. http://dx.doi.org/10.31812/pedag.v51i0.3674.

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Zelinska S.O., Azaryan A.A. and Azaryan V.A. Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment. The augmented reality technologies allow receiving the necessary data about the environment and improvement of the information perception. Application of the augmented reality technologies in the information and educative environment of the higher education establishment will allow receiving the additional instrumental means for education quality increasing. Application of the corresponding instrumental means, to which the platforms of the augmented reality Vuforia, ARToolKit, Kudan can be referred, will allow presenting the lecturers the necessary tools for making of the augmented reality academic programs.
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Kumar, Raj. "Data Mining in Education: A Review." International Journal Of Mechanical Engineering And Information Technology 05, no. 01 (January 26, 2017): 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.

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Zerlina, Dessy, Indarti Komala Dewi, and Sutanto Sutanto. "Feasibility analysis of lake ex-andesite stone mining as geo-tourism area at Tegalega Village, Cigudeg, Bogor." Indonesian Journal of Applied Environmental Studies 1, no. 1 (April 1, 2020): 40–47. http://dx.doi.org/10.33751/injast.v1i1.1974.

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The existence of large wallow which is an ex-mining of andesite stone that is not manage properly became the focus of this study. The objective of this study was to analyse the potential of geo-tourism object at the land of ex-andesite stone mining (Setu Jayamix), as well as to find out the feasibility value of geo-tourism object at the lake of ex-andesite stone mining (Setu Jayamix). Mix methods, which is a combination of qualitative and quantitative methods with the research design of sequential exploratory was used in this study. Sequential exploratory design is a research model where the qualitative data is collected and analyzed, then followed by the collection and analysis of quantitative data, which aims to strengthen the results of the study. The results showed that the potentials of geo-tourism in ex-andesite stone mining area i.e. lake waters, the uniqueness of andesitic stone outcrops, and the view of landscape that overgrown by various plantation crops. Based on the results of the feasibility analyses of geo-tourism, then obtained a feasible value for the geological criteria of physical components (score = 26.334), sustainable for the economic components (score = 20.114), sustainable for the conservation components (score = 10.971), and educative (score = 8.518). Meanwhile, for the accessibility component is declared to be less feasible (score = 61.446).Keberadaan kubangan besar yang merupakan area bekas penambangan batu andesit yang tidak terkelola secara maksimal menjadi fokus penelitian ini. Penelitian ini bertujuan untuk mengkaji potensi obyek geowisata pada lahan di kawasan bekas tambang batu andesit (Setu Jayamix), serta mengetahui nilai kelayakan obyek geowisata di kawasan danau bekas tambang batu andesit tersebut (Setu Jayamix). Metode kombinasi (mix methods), yaitu gabungan antara metode kualitatif dan kuantitatif dengan model penelitian sequential exploratory design digunakan dalam penelitian ini. Sequential exploratory design merupakan model penelitian dimana data kualitatif dikumpulkan dan dianalisis, kemudian diikuti dengan pengumpulan dan analisis terhadap data kuantitatif, yang tujuannya untuk memperkuat hasil penelitian. Hasil penelitian menunjukan bahwa potensi-potensi geowisata yang terdapat di kawasan lahan bekas tambang batu andesit (Setu Jayamix) adalah perairan setu, keunikan singkapan batu andesit, serta pemandangan lanskap kawasan yang ditumbuhi berbagai tanaman perkebunan. Berdasarkan hasil analisis kelayakan geowisata, maka diperoleh nilai layak untuk kriteria geologis komponen fisik (skor = 26,334), berkelanjutan untuk komponen ekonomi (skor = 20,114), berkelanjutan untuk komponen konservasi (skor = 10,971), serta edukatif (dengan skor = 8,518). Sedangkan untuk komponen aksesibilitas dinyatakan kurang layak (skor = 61,446).
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Bunkar, Kamal. "Educational Data Mining in Practice Literature Review." Journal of Advanced Research in Embedded System 07, no. 01 (March 26, 2020): 1–7. http://dx.doi.org/10.24321/2395.3802.202001.

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Educational Data Mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.
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Koedinger, Kenneth R., Sidney D'Mello, Elizabeth A. McLaughlin, Zachary A. Pardos, and Carolyn P. Rosé. "Data mining and education." Wiley Interdisciplinary Reviews: Cognitive Science 6, no. 4 (April 29, 2015): 333–53. http://dx.doi.org/10.1002/wcs.1350.

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Romero, Cristobal, and Sebastian Ventura. "Data mining in education." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 1 (December 14, 2012): 12–27. http://dx.doi.org/10.1002/widm.1075.

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K, Shilpa, and Krishna Prasad K. "A Study on Data Mining Techniques to Improve Students Performance in Higher Education." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 1287–92. http://dx.doi.org/10.21275/sr231014155301.

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Алисултанова, Э. Д., Л. К. Хаджиева, and З. А. Шудуева. "DATA MINING TECHNIQUES IN EDUCATION." Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no. 2(28) (August 26, 2022): 47–54. http://dx.doi.org/10.34708/gstou.2022.16.83.006.

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В данной статье проводится обзор и обосновывается актуальность применения методов интеллектуального анализа данных в образовании. Изложены особенности и основные методы, применяемые для анализа данных в исследуемой области. Описанные методы наиболее актуальны и употребимы в системах поддержки принятия решений. На современном этапе развития информационного объема данных рынок труда требует новых инструментов и методов для поддержки больших хранилищ данных для оптимальной выборки и получения необходимой информации. Интеллектуальный анализ данных (Data mining) направлен на выявление и обработку информации из большого массива, требуемой для принятия решений в определенных сферах деятельности человека. На сегодняшний день области применения Data mining включают такие сферы, как бизнес, образование, сельское хозяйство, медицину и другие. В данном аспекте использование искусственного интеллекта, машинного обучения и методов визуализации данных имеют колоссальное значение для цифровой экономики РФ. This article reviews and substantiates the relevance of the application of data mining methods in education. The features and main methods used to analyze data in the study area are outlined. The described methods are the most relevant and usable in decision support systems. At the present stage of development of the information volume of data, the labor market requires new tools and methods to support large data warehouses for optimal sampling and obtaining the necessary information. Data mining is aimed at identifying and processing information from a large array required for decision making in certain areas of human activity. To date, the areas of application of Data mining include such areas as business, education, agriculture, medicine and others. In this aspect, the use of artificial intelligence, machine learning and data visualization methods are of great importance for the digital economy of the Russian Federation.
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Dwivedi, Shivendra, and Prabhat Pandey. "Efficient Data Mining Technique in Higher Education System: Analysis with Reference to Madhya Pradesh." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (July 1, 2018): 96–102. http://dx.doi.org/10.29070/15/57537.

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Dissertations / Theses on the topic "Educative data mining"

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Войцун, О. Є. "Перспективи educational data mining в Україні." Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47901.

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Актуальність дослідження полягає в тому, що сучасний стан освіти вимагає використання сучасних методів для імплементації вказаних вище потреб, і educational data mining (EDM) надає унікальні можливості для дослідників і практиків. Метою роботи було виявлення перективних напрямків ЕDM для України.
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Manspeaker, Rachel Bechtel. "Using data mining to differentiate instruction in college algebra." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8542.

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Doctor of Philosophy
Department of Mathematics
Andrew G. Bennett
The main objective of the study is to identify the general characteristics of groups within a typical Studio College Algebra class and then adapt aspects of the course to best suit their needs. In a College Algebra class of 1,200 students, like those at most state funded universities, the greatest obstacle to providing personalized, effective education is the anonymity of the students. Data mining provides a method for describing students by making sense of the large amounts of information they generate. Instructors may then take advantage of this expedient analysis to adjust instruction to meet their students’ needs. Using exam problem grades, attendance points, and homework scores from the first four weeks of a Studio College Algebra class, the researchers were able to identify five distinct clusters of students. Interviews of prototypical students from each group revealed their motivations, level of conceptual understanding, and attitudes about mathematics. The student groups where then given the following descriptive names: Overachievers, Underachievers, Employees, Rote Memorizers, and Sisyphean Strivers. In order to improve placement of incoming students, new student services and student advisors across campus have been given profiles of the student clusters and placement suggestions. Preliminary evidence shows that advisors have been able to effectively identify members of these groups during their consultations and suggest the most appropriate math course for those students. In addition to placement suggestions, several targeted interventions are currently being developed to benefit underperforming groups of students. Each student group reacts differently to various elements of the course and assistance strategies. By identifying students who are likely to struggle within the first month of classes, and the recovery strategy that would be most effective, instructors can intercede in time to improve performance.
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Alsuwaiket, Mohammed. "Measuring academic performance of students in Higher Education using data mining techniques." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/34680.

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Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge. Student attendance in Higher Education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student s performance. On other hand, the choice of an effective student assessment method is an issue of interest in Higher Education. Various studies (Romero, et al., 2010) have shown that students tend to get higher marks when assessed through coursework-based assessment methods - which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of Educational Data Mining (EDM) studies that pre-processed data through the conventional Data Mining processes including the data preparation process, but they are using transcript data as it stands without looking at examination and coursework results weighting which could affect prediction accuracy. This thesis explores the above problems and tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. In term of transcripts data, this thesis proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled module s assessment methods; rather they must be investigated thoroughly and considered during EDM s data pre-processing phases. More generally, it is concluded that Educational Data should not be prepared in the same way as exist data due to the differences such as sources of data, applications, and types of errors in them. Therefore, an attribute, Coursework Assessment Ratio (CAR), is proposed to use in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR and SAC on prediction process using data mining classification techniques such as Random Forest, Artificial Neural Networks and k-Nears Neighbors have been investigated. The results were generated by applying the DM techniques on our data set and evaluated by measuring the statistical differences between Classification Accuracy (CA) and Root Mean Square Error (RMSE) of all models. Comprehensive evaluation has been carried out for all results in the experiments to compare all DM techniques results, and it has been found that Random forest (RF) has the highest CA and lowest RMSE. The importance of SAC and CAR in increasing the prediction accuracy has been proved in Chapter 5. Finally, the results have been compared with previous studies that predicted students final marks, based on students marks at earlier stages of their study. The comparisons have taken into consideration similar data and attributes, whilst first excluding average CAR and SAC and secondly by including them, and then measuring the prediction accuracy between both. The aim of this comparison is to ensure that the new preparation process stage will positively affect the final results.
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Burley, Keith Martin. "Data mining techniques in higher education research : the example of student retention." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/19412/.

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Data Mining has been used for more than a decade in a variety of differing environments. It takes an inductive approach to data analysis in that it is concerned with the extraction of patterns from the data often without preconceived ideas. Data mining is part of the field of Business Intelligence, a subject area that the author is familiar with and has taught for many years. He believes that the application of data mining techniques has much to offer within the context of higher education. However, there is little evidence that these well established techniques have previously been applied to the sphere of higher education. Student retention is a hot issue in higher education at the moment. It is for this reason that the author chose to establish the power of data mining techniques in higher education using the examination of student retention issues as a vehicle. The field of student retention has been well documented over the years. Contemporary authors such as McGivney (1996), Moxley et al (2001), Yorke (1999) and Yorke & Longden (2004) have examined strategies and derived intervention techniques aimed at assisting students to adapt to university life. As the proportion of students entering Higher Education has increased there has been an increasing awareness that universities need to adapt to the changing profile of these students. The data was collected via an online questionnaire administered to a large group of computing students at Sheffield Hallam University and similar institutions. The collected data was explored using Data Mining techniques including Decision Trees, Market Basket Analysis and Cluster Analysis. This study sought to explore interrelationships between factors that contribute to student attrition and hence establish the demographics of at-risk students. The use of data mining techniques was found to be highly effective, having found most of the primary issues established in previous research. It went on to find the strongest relationships between them, corresponding well to findings from previous research using standard statistical techniques. The author believes that he has established the power of data mining techniques in higher education and recommends further areas where it could be used profitably.
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Кузіков, Борис Олегович, Борис Олегович Кузиков, and Borys Olehovych Kuzikov. "Сучасний стан та напрями розвитку Education Data Mining в Сумському державному університеті." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/37991.

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Однією з переваг автоматизації бізнес процесів є можливість їх глибокого аналізу з метою пошуку прихованих зв’язків між параметрами моделі і ефективністю процесів. Дослідження у цьому напрямку не пройшли осторонь систем дистанційного навчання, особливо з появою Massive Open Online Courses (MOOC). На ряду із терміном Data Mining – видобуток знань з масивів інформації, розглядають поняття «Education Data Mining» (EDM).
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Pepe, Julie. "STUDENT PERCEPTION OF GENERAL EDUCATION PROGRAM COURSES." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3545.

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The purposes of this study were to: (a) determine, for General Education Program (GEP) courses, what individual items on the student form are predictive of the overall instructor rating value; (b) investigate the relationship of instructional mode, class size, GEP foundational area, and GEP theme with the overall instructor rating value; (c) examine what teacher/course qualities are related to a high (Excellent) overall evaluation or a low (Poor) overall evaluation value. The data set used for analysis contained sixteen student response scores (Q1-Q16), response number, class size, term, foundational area (communication, cultural/historical, mathematics, social, or science), GEP theme (yes/no), instructional mode (face-to-face or other), and percent responding (calculated value). All identifying information such as department, course, section, and instructor was removed from the analysis file. The final data set contained 23 variables, 8,065 course sections, and 294,692 student responses. All individual items on the student evaluation form were related to the overall evaluation item score, measured using Spearman s correlation coefficients. None of the examined course variables were selected as significant when the individual form items were included in the modeling process. This indicated students employed a consistent approach to the evaluation process regardless of large or small classes, face-to-face or other instructional modes, foundational area, or percent responding differences. Data mining modeling techniques were used to understand the relationship of individual item responses and additional course information variables to the overall score. Items one to fifteen (Q1 to Q15), class size, instructional mode, foundational area, and GEP theme were the independent variables used to find splits to create homogenous groups in relation to the overall evaluation score. The model results are presented in terms of if-then rules for  Excellent or  Poor overall evaluation scores. The top three rules for  Excellent or  Poor based their classifications on some combination of the following items: communication of ideas and information; facilitation of learning; respect and concern for students; instructor s overall organization of the course; instructor s interest in your learning; instructor s assessment of your progress in the course; and stimulation of interest in the course. Proportion of student responses conforming to the top three rules for  Excellent or  Poor overall evaluation ranged from 0.89 to .60. These findings suggest that students reward, with higher evaluation scores, instructors who they perceive as organized and strive to clearly communicate course content. These characteristics can be improved through mentoring or professional development workshops for instructors. Additionally, instructors of GEP courses need to be informed that students connect respect and concern and having an interest in student learning with the overall score they give the instructor.
Ph.D.
Department of Educational and Human Sciences
Education
Education PhD
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Franco, Gaviria María Auxiliadora. "Principled design of evolutionary learning sytems for large scale data mining." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/14299/.

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Currently, the data mining and machine learning fields are facing new challenges because of the amount of information that is collected and needs processing. Many sophisticated learning approaches cannot simply cope with large and complex domains, because of the unmanageable execution times or the loss of prediction and generality capacities that occurs when the domains become more complex. Therefore, to cope with the volumes of information of the current realworld problems there is a need to push forward the boundaries of sophisticated data mining techniques. This thesis is focused on improving the efficiency of Evolutionary Learning systems in large scale domains. Specifically the objective of this thesis is improving the efficiency of the Bioinformatic Hierarchical Evolutionary Learning (BioHEL) system, a system designed with the purpose of handling large domains. This is a classifier system that uses an Iterative Rule Learning approach to generate a set of rules one by one using consecutive Genetic Algorithms. This system have shown to be very competitive so far in large and complex domains. In particular, BioHEL has obtained very important results when solving protein structure prediction problems and has won related merits, such as being placed among the best algorithms for this purpose at the Critical Assessment of Techniques for Protein Structure Prediction (CASP) in 2008 and 2010, and winning the bronze medal at the HUMIES Awards for Human-competitive results in 2007. However, there is still a need to analyse this system in a principled way to determine how the current mechanisms work together to solve larger domains and determine the aspects of the system that can be improved towards this aim. To fulfil the objective of this thesis, the work is divided in two parts. In the first part of the thesis exhaustive experimentation was carried out to determine ways in which the system could be improved. From this exhaustive analysis three main weaknesses are pointed out: a) the problem-dependancy of parameters in BioHEL's fitness function, which results in having a system difficult to set up and which requires an extensive preliminary experimentation to determine the adequate values for these parameters; b) the execution time of the learning process, which at the moment does not use any parallelisation techniques and depends on the size of the training sets; and c) the lack of global supervision over the generated solutions which comes from the usage of the Iterative Rule Learning paradigm and produces larger rule sets in which there is no guarantee of minimality or maximal generality. The second part of the thesis is focused on tackling each one of the weaknesses abovementioned to have a system capable of handling larger domains. First a heuristic approach to set parameters within BioHEL's fitness function is developed. Second a new parallel evaluation process that runs on General Purpose Graphic Processing Units was developed. Finally, post-processing operators to tackle the generality and cardinality of the generated solutions are proposed. By means of these enhancements we managed to improve the BioHEL system to reduce both the learning and the preliminary experimentation time, increase the generality of the final solutions and make the system more accessible for end-users. Moreover, as the techniques discussed in this thesis can be easily extended to other Evolutionary Learning systems we consider them important additions to the research in this field towards tackling large scale domains.
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Allègre, Olivier. "Adapting the Prerequisite Structure to the Learner in Student Modeling." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS116.

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Les modèles d'apprenant basés sur les données visent à représenter et comprendre les connaissances des élèves ainsi que leurs autres caractéristiques métacognitives pour soutenir leur apprentissage en faisant des prédictions sur leurs performances futures. La modélisation de l'apprenant peut être abordée en utilisant différentes modèles de système complexes, chacun offrant une perspective différente sur l'étudiant et le processus d'apprentissage. Les techniques d'apprentissage automatique améliorées par la représentation de la connaissance, telles que les réseaux bayésiens, sont particulièrement adaptées pour intégrer la connaissance de domaine dans le modèle de l'apprenant, ce qui en fait un outil précieux dans la modélisation des élèves. Ce travail explore la modélisation et les applications potentielles d'un nouveau cadre appelé E-PRISM (pour Embedding Prerequisite Relationships in Student Modeling), qui inclut un modèle d'apprenant basé sur les réseaux Bayésiens dynamiques. Il utilise une nouvelle architecture pour les réseaux bayésiens qui repose sur la clause d’indépendance des influences causales (ICI), qui réduit le nombre de paramètres dans le réseau et permet une interprétabilité améliorée. L'étude examine les points forts d'EPRISM, notamment sa capacité à considérer la structure préalable requise entre les composants de connaissances, son nombre limité de paramètres et son interprétabilité améliorée. L'étude introduit également une nouvelle approche pour l'inférence approximative dans les grands réseaux bayésiens basés sur la clause ICI, ainsi qu'un algorithme d'apprentissage de paramètres performant dans les réseaux bayésiens basés sur cette clause. Dans l'ensemble, l'étude démontre le potentiel d'E-PRISM comme outil prometteur pour découvrir la structure préalable requise des connaissances de domaine qui peuvent être adaptées à l'apprenant avec pour objectif d'améliorer l'adaptabilité de la boucle extérieure d’un tuteur intelligent
Data-driven learner models aim to represent and understand students' knowledge and other meta-cognitive characteristics to support their learning by making predictions about their future performance. Learner modeling can be approached using various complex system models, each providing a different perspective on the student and the learning process. Knowledge-enhanced machine learning techniques, such as Bayesian networks, are particularly well suited for incorporating domain knowledge into the learner model, making them a valuable tool in student modeling.This work explores the modeling and the potential applications of a new framework, called E-PRISM, for Embedding Prerequisite Relationships In Student Modeling, which includes a learner model based on dynamic Bayesian networks. It uses a new architecture for Bayesian networks that rely on the clause of Independence of Causal Influences (ICI), which reduces the number of parameters in the network and allows enhanced interpretability. The study examines the strengths of E-PRISM, including its ability to consider the prerequisite structure between knowledge components, its limited number of parameters, and its enhanced interpretability. The study also introduces a novel approach for approximate inference in large ICI-based Bayesian networks, as well as a performant parameter learning algorithm in ICI-based Bayesian networks. Overall, the study demonstrates the potential of E-PRISM as a promising tool for discovering the prerequisite structure of domain knowledge that may be adapted to the learner with the perspective of improving the outer-loop adaptivity
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Xu, Yonghong. "Using data mining in educational research: A comparison of Bayesian network with multiple regression in prediction." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280504.

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Advances in technology have altered data collection and popularized large databases in areas including education. To turn the collected data into knowledge, effective analysis tools are required. Traditional statistical approaches have shown some limitations when analyzing large-scale data, especially sets with a large number of variables. This dissertation introduces to educational researchers a new data analysis approach called data mining, an analytic process at the intersection of statistics, databases, machine learning/artificial intelligence (AI), and computer science, that is designed to explore large amounts of data to search for consistent patterns and/or systematic relationships between variables. To examine the usefulness of data mining in educational research, one specific data mining technique--the Bayesian Belief Network (BBN) based in Bayesian probability--is used to construct an analysis model in contrast to the traditional statistical approaches to answer a pseudo research question about faculty salary prediction in postsecondary institutions. Four prediction models--a multiple regression model with theoretical variable selection, a regression model with statistical variable extraction, a data mining BBN model with wrapper feature selection, and a combination model that used variables selected by the BBN in a multiple regression procedure--are expounded to analyze a data set called the National Survey of Postsecondary Faculty 1999 (NSOPF:99) provided by the National Center of Educational Services (NCES). The algorithms, input variables, final models, outputs, and interpretations of the four prediction models are presented and discussed. The results indicate that, with a nonmetric approach, the BBN can effectively handle a large number of variables through a process of stochastic subset selection; uncover dependence relationships among variables; detect hidden patterns in the data set; minimize the sample size as a factor influencing the amount of computations in data modeling; reduce data dimensionality by automatically identifying the most pertinent variable from a group of different but highly correlated measures in the analysis; and select the critical variables related to a core construct in prediction problems. The BBN and other data mining techniques have drawbacks; nonetheless, they are useful tools with unique advantages for analyzing large-scale data in educational research.
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Петренко, А. М. "Застосування методів EDM для розробки системи підтримки рішень." Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/82374.

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Дана робота присвячена проектуванню моделі, яка б допомогла адміністратору електронної системи прийняти рішення щодо продуктивності сервера та прогнозувала б максимальну кількість унікальних користувачів та запитів при якій сервер буде неспроможний обробляти запити клієнтів. У ході роботи проведено аналіз вхідних даних, літератури, обрані методи, алгоритм виконання поставленої задачі, реалізовані наглядні графіки, що показують масштабованість електронного ресурсу, розроблено модель для допомоги адміністратору ресурса прийняти рішення щодо його продуктивності. Об’єкт дослідження — Дистанційний навчальний ресурс https://mix.sumdu.edu.ua. В процесі виконання реалізації моделі було застосовано методи категорії прогнозування, закон Літтла та перевірка статистичної гіпотези щодо рівності середніх значень.
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Books on the topic "Educative data mining"

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Khan, Badrul H., Joseph Rene Corbeil, and Maria Elena Corbeil, eds. Responsible Analytics and Data Mining in Education. New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9780203728703.

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Handbook of educational data mining. Boca Raton: Taylor & Francis Group, 2011.

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Linking competence to opportunities to learn: Models of competence and data mining. [ New York]: Springer, 2009.

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service), SpringerLink (Online, ed. Modern Issues and Methods in Biostatistics. New York, NY: Springer Science+Business Media, LLC, 2011.

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Maine. Bureau of Employment Security., ed. Maine occupational staffing for selected nonmanufacturing industries: Mining, construction, finance, insurance, and real estate services, except hospital and education : data for second quarter 1990. Augusta, Maine (P.O. Box 309, Augusta 04332-0309): The Division, 1992.

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Rodrigues, Lopes Lia Carrari, Barretto Saulo Faria Almeida, and SpringerLink (Online service), eds. Digital Ecosystems: Third International Conference, OPAALS 2010, Aracuju, Sergipe, Brazil, March 22-23, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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United States. Congress. House. Committee on Homeland Security. Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies. How data mining threatens student privacy: Joint hearing before the Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies of the Committee on Homeland Security, House of Representatives and the Subcommittee on Early Childhood, Elementary, and Secondary Education of the Committee on Education and the Workforce, House of Representatives, One Hundred Thirteenth Congress, second session, June 25, 2014. Washington: U.S. Government Printing Office, 2015.

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Kolo, Ibrahim A. Educational restoration in the Niger State College of Education: September 2001-2003 : being a speech at the 19th to 23rd convocation ceremony of the Niger State College of Education, Minna : date, Saturday, March 22nd 2003 : venue, college convocation ground. [Minna?: Niger State College of Education, 2003.

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Ilias, Maglogiannis, Papadopoulos Harris, and SpringerLink (Online service), eds. Artificial Intelligence Applications and Innovations: 8th IFIP WG 12.5 International Conference, AIAI 2012, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Ilias, Maglogiannis, Papadopoulos Harris, Karatzas Kostas, Sioutas Spyros, and SpringerLink (Online service), eds. Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Educative data mining"

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Guruler, Huseyin, and Ayhan Istanbullu. "Modeling Student Performance in Higher Education Using Data Mining." In Educational Data Mining, 105–24. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_4.

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Agrawal, Rakesh. "Enriching Education through Data Mining." In Machine Learning and Knowledge Discovery in Databases, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23780-5_1.

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Schönbrunn, Karoline, and Andreas Hilbert. "Data Mining in Higher Education." In Studies in Classification, Data Analysis, and Knowledge Organization, 489–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_56.

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Agrawal, Rakesh, Sreenivas Gollapudi, Anitha Kannan, and Krishnaram Kenthapadi. "Enriching Education through Data Mining." In Lecture Notes in Computer Science, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21786-9_1.

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Peña-Ayala, Alejandro, and Leonor Cárdenas. "How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study." In Educational Data Mining, 65–101. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_3.

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Contreras Bravo, Leonardo Emiro, Giovanny Mauricio Tarazona Bermudez, and José Ignacio Rodríguez Molano. "Big Data: An Exploration Toward the Improve of the Academic Performance in Higher Education." In Data Mining and Big Data, 627–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_59.

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Eubanks, David, William Evers, and Nancy Smith. "FINDING PREDICTORS IN HIGHER EDUCATION." In Data Mining and Learning Analytics, 41–53. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch3.

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Ogrezeanu, Andreea-Elena. "Data Mining in Smart Agriculture." In Education, Research and Business Technologies, 249–57. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8866-9_21.

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Agarwal, Sonali, Murli Dhar Tiwari, and Iti Tiwari. "Government Data Mining Case Studies on Education and Health." In E Governance Data Center, Data Warehousing and Data Mining, 155–201. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.

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Osorio-Acosta, Estefania. "Data Mining for Educational Management." In Encyclopedia of Education and Information Technologies, 487–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10576-1_124.

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Conference papers on the topic "Educative data mining"

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Ponelis, Shana. "Finding Diamonds in Data: Reflections on Teaching Data Mining from the Coal Face." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3313.

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Making sense of the exponentially expanding sources of structured electronic data collected by organizations is increasingly difficult. Data mining is the extraction of implicit, previously unknown, and potentially useful information from large volumes of such data to support decisionmaking in organizations and has led to an increase in demand for students who have an understanding of data mining techniques and can apply them to organizations’ data. Thus data mining is an increasingly important component of the Information Systems curriculum in order to meet this skills demand. This paper describes the development of a curriculum for an elective data mining course in an Information Systems graduate program based on the only available model curriculum from the ACM SIGKDD over a two year period and concludes with student feedback and lecturer reflection. This paper will be useful to educators responsible for developing curricula and teaching data mining to IS graduate students; in addition, it serves as instructor feedback to the authors of the ACM SIGKDD model curriculum.
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Nduwimfura, Philbert, Yassein Nkoma, and Zheng JianGuo. "Improving education through data mining." In 2013 International Conference on Information and Communication Technology for Education. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icte130301.

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Ying Wah, Teh, and Zaitun Abu Bakar. "Investigating the Status of Data Mining in Practice." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2719.

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This paper is based on a survey carried out in the Malaysian environment. The paper starts with a definition of data warehouses, data mining and this is followed by a description of its current status in the Malaysian data mining environment. This is followed by a discussion on why data mining is a great challenge for an implementation in the Malaysian environment. Based on the feedback obtained from the respondents, a conclusion is drawn on the appropriateness of the data mining techniques in the Malaysian environment
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Motoryn, Ruslan, Tetiana Motoryna, and Kateryna Prykhodko. "Impact of big data on development of the curriculums of training statisticians in Ukrainian university." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17702.

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In the Soviet era in Ukraine, theoretical subjects dominated in the education curriculum. Since independence, there have been profound changes in the higher education system of Ukraine. The system has adapted to the market model, with an increasing number of applied disciplines such as data mining, software engineering and data visualization. Courses should cover both theoretical knowledge and practical skills. Recently, one of the top requested areas at the information technology market is the processing of large data sets (Big data). Many leading universities of Ukraine, including Taras Shevchenko National University of Kyiv and Kyiv-Mohyla Academy, plan to establish courses of data mining, statistics, and data visualization. For now, however, universities do not have enough teachers. Therefore, under the scope of the Ukrainian Distance Education Project "Prometheus", in 2017 an online course on the Big Data will be launched. This paper will summarize the curriculum developed by the authors, in courses on Business Analytics, Software Engineering, Data Analysis, and Data Visualization.
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Wang, Xiaodan. "Data Mining in Network Engineering'Bayesian Networks for Data Mining." In International Conference on Education, Management, Commerce and Society. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/emcs-15.2015.84.

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Hauke, Krzysztof, Mievzyslaw L. Owoc, and Maciej Pondel. "Building Data Mining Models in the Oracle 9i Environment." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2697.

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Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressing the critical steps of the whole process and presenting examples of practical usage of DM models. Verification techniques of the generated knowledge bases are discussed in the mentioned environment.
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Alawi, Sultan Juma Sultan, Izwan Nizal Mohd Shaharanee, and Jastini Mohd Jamil. "Profiling Oman education data using data mining approach." In THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17). Author(s), 2017. http://dx.doi.org/10.1063/1.5005467.

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R. P, Arya, and Anuja S. B. "Effectively Analysis and Predict Students Performance and Other Evaluation." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/gdhl6261/ngcesi23p2.

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The development of intelligent technologies gains popularity in the education field. Educational Data Mining (EDM) is a research field of data mining, which focuses on the application of data mining, machine learning and statistical methods. The clustering effect of K-means Algorithm is tested by discriminant analysis. K-means Algorithm improves the reliability of prediction results. The development of intelligent technologies gains popularity in the education field. The rapid growth of educational data indicates traditional processing methods may have limitations and distortion. Therefore, reconstructing the research technology of data mining in the education field has become increasingly prominent. In order to avoid unreasonable evaluation results and monitor the students’ future performance in advance, this paper comprehensively uses the relevant theories of clustering, discrimination and convolution neural network to analyze and predict students’ academic performance. Firstly, this paper proposes that the clustering-number determination is optimized by using a statistic which has never been used in the algorithm of K-means.
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Othman, El Harrak, Slimani Abdelali, and El Bouhdidi Jaber. "Education data mining: Mining MOOCs videos using metadata based approach." In 2016 4th IEEE International Colloquium on Information Science and Technology (CIST). IEEE, 2016. http://dx.doi.org/10.1109/cist.2016.7805106.

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Juškaite, Loreta. "DATA MINING IN EDUCATION: ONLINE TESTING IN LATVIAN SCHOOLS." In 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.86.

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The new research results on the online- testing method in the Latvian education system for a learning process assessment are presented. Data mining is a very important field in education because it helps to analyse the data gathered in various researches and to implement the changes in the education system according to the learning methods of students. The aim of the research was to analyze how much time students devote to each task depending on the task type and the cognitive activity level in the online national test. Research methods: 1) analysis of scientific literature; 2) descriptive statistics and dependency analysis for processing the data. Research results showed that the time spent on tasks depends not on the complexity of the task but on the form and formulation of it. Keywords: online test, data mining, cognitive actitity level.
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Reports on the topic "Educative data mining"

1

Zelinska, Snizhana O., Albert A. Azaryan, and Volodymyr A. Azaryan. Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2672.

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The augmented reality technologies allow receiving the necessary data about the environment and improvement of the information perception. Application of the augmented reality technologies in the information and educative environment of the higher education establishment will allow receiving the additional instrumental means for education quality increasing. Application of the corresponding instrumental means, to which the platforms of the augmented reality Vuforia, ARToolKit, Kudan can be referred, will allow presenting the lecturers the necessary tools for making of the augmented reality academic programs.
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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