Relevant bibliographies by topics / Educational data mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Educational data mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Educational data mining"

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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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Shrestha, Sushil, and Manish Pokharel. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (April 1, 2021): 9. http://dx.doi.org/10.11591/ijict.v10i1.pp9-18.

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<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click, and Wiki.Click was used as the independent features and Grade as the dependent feature. Five classification algorithms such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine (SVM), Random Forest, and CART decision tree were applied in the moodle data. The finding shows that SVM has the highest accuracy in comparison to other algorithms. It suggested that File.Click and System.Click was the most significant feature. This type of research helps in the early identification of students’ performance. The growing popularity of the teaching-learning process through an online learning system has attracted researchers to work in the field of Educational Data Mining (EDM). Varieties of data are generated through several online activities that can be analyzed to understand the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platforms for the delivery of the course contents and the learners who use these platforms are highly benefited from this research.</p>
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Bilal Zorić, Alisa. "Benefits of Educational Data Mining." Journal of International Business Research and Marketing 6, no. 1 (2020): 12–16. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.61.3002.

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We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.
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Sharma, Pragati, and Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.
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Hussain, Sadiq, and Hazarika G.C. "Educational Data Mining Using JMP." International Journal of Computer Science and Information Technology 6, no. 5 (October 31, 2014): 111–20. http://dx.doi.org/10.5121/ijcsit.2014.6509.

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Bachhal, P., S. Ahuja, and S. Gargrish. "Educational Data Mining: A Review." Journal of Physics: Conference Series 1950, no. 1 (August 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/1950/1/012022.

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Mohamad, Siti Khadijah, and Zaidatun Tasir. "Educational Data Mining: A Review." Procedia - Social and Behavioral Sciences 97 (November 2013): 320–24. http://dx.doi.org/10.1016/j.sbspro.2013.10.240.

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Slater, Stefan, Srećko Joksimović, Vitomir Kovanovic, Ryan S. Baker, and Dragan Gasevic. "Tools for Educational Data Mining." Journal of Educational and Behavioral Statistics 42, no. 1 (September 24, 2016): 85–106. http://dx.doi.org/10.3102/1076998616666808.

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In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.
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M. D. A. Ali, Felermino, and S. C. Ng. "Moodle Data Retrieval for Educational Data Mining." International Journal of Scientific Engineering and Technology 4, no. 11 (November 1, 2015): 523–25. http://dx.doi.org/10.17950/ijset/v4s11/1105.

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Nguyen, Thanh Ngoc Dan, and Vi Thi Thuy Ha. "AN OVERVIEW OF EDUCATIONAL DATA MINING." Scientific Journal of Tra Vinh University 1, no. 1 (June 13, 2019): 56–60. http://dx.doi.org/10.35382/18594816.1.1.2019.88.

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Higher education data is growing, but the exploitation and extraction of meaningful knowledge for management have not been paid much attention. The existing mining tools are not effective. This study aims to introduce three techniques for educational data mining: (1) Classification techniques, (2) Predictive models, (3) Clustering techniques. Simultaneously, the study also proposes some solutions to analyze and visualize data, predict students’ learning capacity and assemble learners. Thereby, education managers could choose appropriate data mining solutions for effective management and training.
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Dissertations / Theses on the topic "Educational 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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Melgueira, Pedro Miguel Lúcio. "Educational data mining applied to Moodle data from the University of Évora." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/21346.

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E-Learning tem vindo a ganhar popularidade como forma de transmissão de conhecimentos a nível educacional graças aos avanços nas tecnologias, como por exemplo, a Internet. Instituições como universidades e empresas têm vindo a usar E-Learning para a transmissão de conteúdos educacionais para locais remotos estendendo o seu alcance a estudantes e colaboradores que estão fisicamente distantes. Sistemas chamados “Learning Management Systems”, como o Moodle, existem para organizar E-Learning. Eles oferecem plataformas online onde professores e educadores podem publicar conteúdo, organizar actividades, fazer avaliações, e outro tipo de ações relacionadas, de modo a que os estudantes possam aprender e serem avaliados. Estes sistemas geram e guardam muitos dados relacionados não só com o seu uso, mas também relacionados com notas de estudantes. Este tipo de dados são frequentemente chamados de Dados Educacionais. Métodos de Data Mining são aplicados a estes dados de modo a fazer suposições não triviais. As técnicas aplicadas tomam inspiração de projectos semelhantes no campo da Data Mining Educacional. Este campo consiste na aplicação de métodos de Data Mining a Dados Educacionais. Neste projecto, um repositório de dados do Moodle da Universidade de Évora foi explorado. Técnicas de aprendizagem supervisionada são aplicadas aos dados de modo a mostrar como é possível prever o sucesso de estudantes a partir do seu uso do Moodle. Métodos de aprendizagem não supervisionada são também aplicados de modo a mostrar como há divisões nos dados; Abstract: E-Learning has been rising in popularity as a way to deliver training due to the advancements of technologies, like the Internet. Institutions such as universities and companies have been making use of E-Learning to deliver training to remote locations extending their reach to students and employees who are physically distant. Systems called Learning Management Systems, like Moodle, exist to organize E-Learning. They provide online platforms where professors and educators can publish content, organize activities, perform evaluations, and so on, in order for students to learn and get evaluated. These systems generate and store lots of data regarding not only their usage, but also regarding the grades of students. This kind of data is often referred too as Educational Data. Data Mining techniques are applied to this data in order to make non trivial assumptions. The techniques applied take inspiration from similar projects within the field of Educational Data Mining. This field consists in applying Data Mining Techniques to Education Data. In this project, a data repository from the Moodle of the University of Évora is explored. Supervised learning techniques are applied to this data in order to show how it is possible to make predictions about the success of students based on their usage of Moodle. Unsupervised learning techniques are also applied in order to show how data is divisible.
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Borg, Olivia. "Educational Data Mining : En kvalitativ studie med inriktning på dataanalys för att hitta mönster i närvarostatistik." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16987.

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Studien fokuserar på att hitta olika mönster i närvarostatistik hos elever som inte närvarar i skolan. Informationen som resultatet ger kan därefter användas som ett beslutsunderlag för skolor eller till andra organisationer som är intresserade av EDM inom närvarostatistik. Arbetet genomförde en kvalitativ metodansats med en fallstudie som bestod utav en litteraturstudie samt en implementation. Litteraturstudien användes för att få en förståelse över vanliga tillvägagångssätt inom EDM, som därefter låg till grund för implementationen som använde arbetssättet CRISP-DM. Resultatet blev fem olika mönster som definieras genom dataanalys. Mönstren visar frånvaro ur ett tidsperspektiv samt per ämne och kan ligga till grund för framtida beslutsunderlag.
The study focuses on finding different patterns in attendance statistics for students who are not present at school. The information provided by the results can thereafter be used as a basis for decision-making for schools or for other organizations interested in EDM within attendance statistics. The work carried out a qualitative method approach with a case study that consisted a literature study and an implementation. The literature study was used to gain an understanding of common approaches within EDM, which subsequently formed the basis for the implementation that used the working method CRISP-DM. The project resulted in five different patterns defined by data analysis. The patterns show absence from a time perspective and per subject and can form the basis for future decision-making.
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Mavrikis, Manolis P. "Modelling students' behaviour and affect in ILE through educational data mining." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/15294.

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The long-term objective behind the research presented in this thesis is the improvement of ILEs and particularly those components that take into account students’ behaviour, as well as emotions and motivation. In related research, this is often attempted based on intuition, theoretical perspectives, or guided by results from studies in the isolation of a research lab. In this thesis, an attempt is made to inform the design of adaptation and feedback components by collecting and analysing as realistic data as possible. Guided by the belief that qualitative data analysis results can be enriched by employing statistical and machine learning techniques, the focus of this research is to investigate (a) key aspects of students’ behaviour and their relation to their learning and (b) how their behaviour could be employed to predict students’ affective and motivational characteristics. The first step is to gain an in-depth understanding of students’ behaviours in ILEs when they interact on their own time and location, rather than during a study where the social dynamics are different. Based on results, components of an ILE are redesigned and two Bayesian models are machine-learned; one that predicts when students need help in answering a question and one that predicts if their interaction with the system is beneficial to their learning. In the next step, machine learning is employed in order to derive predictive models of students’ affective and motivational states based on their interaction with ILEs. This is achieved by deriving decision trees based on a dataset of students’ self-reports collected during replays of their interaction. In addition, in order to take tutors’ perspectives into account, two different approaches are followed: The first attempts to elicit tutors’ inferences while they are watching replays of students’ interactions. This was not entirely successful. In the second approach, decision trees are derived from a dataset of tutors’ inferences collected during one-to-one computer-mediated tutorials.
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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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Rajibussalim. "Data Mining for Studying the Impact of Reflection on Learning." Thesis, The University of Sydney, 2010. http://hdl.handle.net/2123/10589.

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Title: Data Mining for Studying the Impact of Reflection on Learning Keywords: educational data mining, Reflect, learning behaviour, impact Abstract On-line Web-based education learning systems generate a large amount of students' log data and profiles that could be useful for educators and students. Hence, data mining techniques that enable the extraction of hidden and potentially useful information in educational databases have been employed to explore educational data. A new promising area of research called educational data mining (EDM) has emerged. Reflect is a Web-based learning system that supports learning by reflection. Reflection is a process in which individuals explore their experiences in order to gain new understanding and appreciation, and research suggests that reflection improves learning. The Reflect system has been used at the University of Sydney’s School of Information Technology for several years as a source of learning and practice in addition to the classroom teaching. Using the data from a system that promotes reflection for learning (such as the Reflect system), this thesis focuses on the investigation of how reflection helps students in their learning. The main objective is to study students' learning behaviour associated with positive and negative outcomes (in exams) by utilising data mining techniques to search for previously unknown, potentially useful hidden information in the database. The approach in this study was, first, to explore the data by means of statistical analyses. Then, popular data mining algorithms such as the K-means and J48 algorithms were utilised to cluster and classify students according to their learning behaviours in using Reflect. The Apriori algorithm was also employed to find associations among the data attributes that lead to success. We were able to group and classify students according to their activities in the Reflect system, and identified some activities associated with student performance and learning outcomes (high, moderate or low exam marks). We concluded that the approach resulted in the identification of some learning behaviours that have important impacts on student performance.
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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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Davoodi, Alireza. "User modeling and data mining in intelligent educational games : Prime Climb a case study." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45274.

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Educational games are designed to leverage students’ motivation and engagement in playing games to deliver pedagogical concepts to the players during game play. Adaptive educational games, in addition, utilize students’ models of learning to support personalization of learning experience according to students’ educational needs. A student’s model needs to be capable of making an evaluation of the mastery level of the target skills in the student and providing reliable base for generating tailored interventions to meet the user’s needs. Prime Climb, an adaptive educational game for students in grades 5 or 6 to practice number factorization related skill, provides a test-bed for research on user modeling and personalization in the domain of education games. Prime Climb leverages a student’s model using Dynamic Bayesian Network to implement personalization for assisting the students practice number factorization while playing the game. This thesis presents research conducted to improve the student’s model in Prime Climb by detecting and resolving the issue of degeneracy in the model. The issue of degeneracy is related to a situation in which the model’s accuracy is at its global maximum yet it violates conceptual assumptions about the process being modeled. Several criteria to evaluate the student’s model are introduced. Furthermore, using educational data mining techniques, different patterns of students’ interactions with Prime Climb were investigated to understand how students with higher prior knowledge or higher learning gain behave differently compared to students with lower prior knowledge and lower learning gain.
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Dailey, Matthew D. "Learning the Effectiveness of Content and Methodology in an Intelligent Tutoring System." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/684.

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Classroom instruction time is a valuable yet scarce resource to teachers, who must decide how to best meet their objectives by selecting which topics to spend time on and when to move forward. Intelligent Tutoring Systems (ITS) are a powerful tool for teachers in this regard, allowing them to measure their students' current level of knowledge, helping them gauge student knowledge acquisition, and providing them with valuable insight into learning methodologies. By using ITS to identify the effectiveness of proven methods of instruction, we can more effectively teach students both in and outside of the classroom. In this paper we review the results and contributions of a new Bayesian data mining method which can be used to identify what works in an ITS and how it can be used to learn from data which is not in the typical randomized controlled trial design. We then discuss modifications to this dataset which use more knowledge about the students to improve accuracy. Lastly we evaluate this model on detecting and predicting long term student retention, and discuss methods to improve its predictive accuracy.
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Xu, Beijie. "Clustering Educational Digital Library Usage Data: Comparisons of Latent Class Analysis and K-Means Algorithms." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/954.

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There are common pitfalls and neglected areas when using clustering approaches to solve educational problems. A clustering algorithm is often used without the choice being justified. Few comparisons between a selected algorithm and a competing algorithm are presented, and results are presented without validation. Lastly, few studies fully utilize data provided in an educational environment to evaluate their findings. In response to these problems, this thesis describes a rigorous study comparing two clustering algorithms in the context of an educational digital library service, called the Instructional Architect. First, a detailed description of the chosen clustering algorithm, namely, latent class analysis (LCA), is presented. Second, three kinds of preprocessed data are separately applied to both the selected algorithm and a competing algorithm, namely, K-means algorithm. Third, a series of comprehensive evaluations on four aspects of each clustering result, i.e., intra-cluster and inter-cluster distances, Davies-Bouldin index, users' demographic profile, and cluster evolution, are conducted to compare the clustering results of LCA and K-means algorithms. Evaluation results show that LCA outperforms K-means in producing consistent clustering results at different settings, finding compact clusters, and finding connections between users' teaching experience and their effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.
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Books on the topic "Educational data mining"

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Peña-Ayala, Alejandro, ed. Educational Data Mining. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02738-8.

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

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Sweta, Soni. Modern Approach to Educational Data Mining and Its Applications. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4681-9.

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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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Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia, and SpringerLink (Online service), eds. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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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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Romero, Cristobal, Sebastian Ventura, Mykola Pechenizkiy, and Ryan S. J. d. Baker, eds. Handbook of Educational Data Mining. CRC Press, 2010. http://dx.doi.org/10.1201/b10274.

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Romero, Cristobal. Handbook of Educational Data Mining. Taylor & Francis Group, 2010.

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Pechenizkiy, Mykola, Cristobal Romero, Sebastian Ventura, and Ryan S. J. d. Baker. Handbook of Educational Data Mining. Taylor & Francis Group, 2010.

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Book chapters on the topic "Educational data mining"

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Scheuer, Oliver, and Bruce M. McLaren. "Educational Data Mining." In Encyclopedia of the Sciences of Learning, 1075–79. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_618.

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Baker, Ryan S., Yuan Wang, Luc Paquette, Vincent Aleven, Octav Popescu, Jonathan Sewall, Carolyn Rosé, et al. "EDUCATIONAL DATA MINING." In Data Mining and Learning Analytics, 55–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch4.

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Dawson, Catherine. "Educational data mining." In A–Z of Digital Research Methods, 114–19. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351044677-18.

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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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Romero, Cristóbal, Rebeca Cerezo, Alejandro Bogarín, and Miguel Sánchez-Santillán. "EDUCATIONAL PROCESS MINING." In Data Mining and Learning Analytics, 1–28. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch1.

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Bousbia, Nabila, and Idriss Belamri. "Which Contribution Does EDM Provide to Computer-Based Learning Environments?" In Educational Data Mining, 3–28. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_1.

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Ivančević, Vladimir, Marko Knežević, Bojan Pušić, and Ivan Luković. "Adaptive Testing in Programming Courses Based on Educational Data Mining Techniques." In Educational Data Mining, 257–87. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_10.

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Amir, Ofra, Kobi Gal, David Yaron, Michael Karabinos, and Robert Belford. "Plan Recognition and Visualization in Exploratory Learning Environments." In Educational Data Mining, 289–327. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_11.

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Sun, Xiaoxun. "Finding Dependency of Test Items from Students’ Response Data." In Educational Data Mining, 329–42. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_12.

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Dascalu, Mihai, Philippe Dessus, Maryse Bianco, Stefan Trausan-Matu, and Aurélie Nardy. "Mining Texts, Learner Productions and Strategies with ReaderBench." In Educational Data Mining, 345–77. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_13.

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Conference papers on the topic "Educational data mining"

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da Silva, Victor Regis Lyra Beserra, Fabio de Albuquerque Silva, and Vanilson Buregio. "Characterizing Educational Data Mining." In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2019. http://dx.doi.org/10.23919/cisti.2019.8760815.

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Dol, Sunita M., and Pradeep M. Jawandhiya. "Use of Data mining Tools in Educational Data Mining." In 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE, 2022. http://dx.doi.org/10.1109/ccict56684.2022.00075.

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Mishra, Akansha, Rashi Bansal, and Shailendra Narayan Singh. "Educational data mining and learning analysis." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943201.

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Aleem, Abdul, and Manoj Madhava Gore. "Educational Data Mining Methods: A Survey." In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2020. http://dx.doi.org/10.1109/csnt48778.2020.9115734.

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Moscoso-Zea, Oswaldo, Andres-Sampedro, and Sergio Lujan-Mora. "Datawarehouse design for educational data mining." In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, 2016. http://dx.doi.org/10.1109/ithet.2016.7760754.

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Johnson, Julia Ann, Genevieve Marie Johnson, and Robert F. Cavanagh. "Rough Sets for Mining Educational Data." In 2012 Spring Congress on Engineering and Technology (S-CET). IEEE, 2012. http://dx.doi.org/10.1109/scet.2012.6341891.

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Baker, Ryan S. J. d., Simon Buckingham Shum, Erik Duval, John Stamper, and David Wiley. "Educational data mining meets learning analytics." In the 2nd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330601.2330613.

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Siemens, George, and Ryan S. J. d. Baker. "Learning analytics and educational data mining." In the 2nd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330601.2330661.

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Moscoso-Zea, Oswaldo, and Sergio Lujan-Mora. "Educational data mining: An holistic view." In 2016 11th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2016. http://dx.doi.org/10.1109/cisti.2016.7521411.

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Jayanthi, M. Amala, R. Lakshmana Kumar, Abhijith Surendran, and K. Prathap. "Research contemplate on educational data mining." In 2016 IEEE International Conference on Advances in Computer Applications (ICACA). IEEE, 2016. http://dx.doi.org/10.1109/icaca.2016.7887933.

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Reports on the topic "Educational data mining"

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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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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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Balyk, Nadiia, Svitlana Leshchuk, and Dariia Yatsenyak. Developing a Mini Smart House model. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3741.

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The work is devoted to designing a smart home educational model. The authors analyzed the literature in the field of the Internet of Things and identified the basic requirements for the training model. It contains the following levels: command, communication, management. The authors identify the main subsystems of the training model: communication, signaling, control of lighting, temperature, filling of the garbage container, monitoring of sensor data. The proposed smart home educational model takes into account the economic indicators of resource utilization, which gives the opportunity to save on payment for their consumption. The hardware components for the implementation of the Mini Smart House were selected in the article. It uses a variety of technologies to conveniently manage it and use renewable energy to power it. The model was produced independently by students involved in the STEM project. Research includes sketching, making construction parts, sensor assembly and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Research includes sketching, making some parts, assembly sensor and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Approbation Mini Smart House researches were conducted within activity the STEM-center of Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University, in particular during the educational process and during numerous trainings and seminars for pupils and teachers of computer science.
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LI, Zhendong, Chengcheng Zhang, Hangjian Qiu, Xiaoqian Wang, and Yuejuan Zhang. Different Acupuncture Intervention Time-points for Rehabilitation of Post-Stroke Cognitive Impairment：Protocol For a Network Meta-analysis of Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0043.

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Review question / Objective: This study will provide evidence-based references for the efficacy of different acupuncture interventions time-point in the treatment of post-stroke cognitive impairment(PSCI). 1. Types of studies. Only randomized controlled trials (RCTs) of acupuncture for PSCI will be recruited. Additionally, Studies should be available in full papers as well as peer-reviewed and the original data should be clear and adequate. 2. Types of participants. All adults with a recent or previous history of ischaemic or hemorrhagic stroke and diagnosed according to clearly defined or internationally recognized diagnostic criteria, regardless of nationality, race, sex, age, or educational background. 3. Types of interventions and controls. The control group takes non-acupuncture treatment, including conventional rehabilitation or in combination with symptomatic support therapy. The experimental group should be treated with acupuncture on basis of the control group. 4. Types of outcomes. The primary outcomes are measured with The Mini-Mental State Examination (MMSE) and/or The Montreal Cognitive Assessment Scale (MoCA), which have been widely used to evaluate cognitive abilities.
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LI, Zhendong, Hangjian Qiu, xiaoqian Wang, chengcheng Zhang, and Yuejuan Zhang. Comparative Efficacy of 5 non-pharmaceutical Therapies For Adults With Post-stroke Cognitive Impairment: Protocol For A Bayesian Network Analysis Based on 55 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0036.

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Review question / Objective: This study will provide evidence-based references for the efficacy of 5 different non-pharmaceutical therapies in the treatment of post-stroke cognitive impairment(PSCI). 1. Types of studies. Only randomized controlled trials (RCTs) of Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) and Computer-assisted cognitive rehabilitation(CA) for PSCI will be recruited. Additionally, Studies should be available in full papers as well as peer reviewed and the original data should be clear and adequate. 2. Types of participants. All adults with a recent or previous history of ischaemic or hemorrhagic stroke and diagnosed according to clearly defined or internationally recognized diagnostic criteria, regardless of nationality, race, sex, age, or educational background. 3.Types of interventions and controls. The control group takes non-acupuncture treatment, including conventional rehabilitation or in combination with symptomatic support therapy. The experimental group should be treated with acupuncture on basis of the control group. 4.The interventions of the experimental groups were Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) or Computer-assisted cognitive rehabilitation(CA), and the interventions of the control group takes routine rehabilitation and cognition training or other therapies mentioned above that were different from the intervention group. 5.Types of outcomes. The primary outcomes are measured with The Mini-Mental State Examination (MMSE) and/or The Montreal Cognitive Assessment Scale (MoCA), which have been widely used to evaluate the cognitive abilities. The secondary outcome indicator was the Barthel Index (BI) to assess independence in activities of daily living (ADLs).
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