Student performance prediction (SPP) aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining (EDM). This paper provides a systematic review of the SPP study from the ...
Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the random forests, nearest ...
About this course. Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining ...
Data Mining is very useful in the field of education especially when examining students' learning behavior in online learning environment. This is due to the potential of data mining in analyzing and uncovering the hidden information of the data itself which is hard and very time consuming if to be done manually.
Educational Data Mining Approaches, Challenges and Goals: A Review Chitra Mehra1, Dr. Rashmi Agrawal2 DOI: 10.5958/2347-7202.2020.00008.0 ABSTRACT
Educational data mining (EDM) is a branch of data mining and machine learning research to develop new ways to analysis educational data from an educational system. Recently, EDM an rising field of data mining and educational systems. EDM uses traditional mining algorithms to analyses educational data in order to understand and improve the students' learning process. In this paper, we present a ...
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title "data mining in education". It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved ...
Educational data mining (EDM) is a methodology adopted to extract significant knowledge and patterns from academic database, . In the present study, data collection is accomplished using a questionnaire-based survey from students and data from the academic section of the chosen institute. Student features can be classified into personal ...
Educational data mining (EDM) is a branch of data mining and machine learning research to develop new ways to analysis educational data from an educational system (Pathan et al. 2014 ). It is the process of converting raw data from educational institution to usable patterns (Tan et al. 2016 ). In this paper, we introduce how educational data ...
Abstract and Figures. Educational Data Mining (EDM) is an emerging fieldexploring data in educational context by applyingdifferent Data Mining (DM) techniques/tools. It provides intrinsic ...
The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100; see indexing) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions.It is completely and permanently free and open-access to both authors and readers.
Educational data mining involves investigating the influence of the context as well as the temporal occurrence of events in relation to variables at the level of the session as well as student behavior and outcomes, for instance, through the use of sequence mining ( Bouchet, Kinnebrew, Biswas, & Azevedo, 2012; Kinnebrew & Biswas, 2012 ).
Educational data mining is an emerging field in the area of data mining. In this competitive world, the educational setting also uses data mining tools to explore and analyze student performance ...
Educational data mining (EDM) is a new field that arose from the use of data mining tools to analyze educational data . Data mining and machine learning, pattern recognition, psychometrics and other areas of statistics, artificial intelligence, information visualization, and computational modeling are all growing fields in educational data ...
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a ...
Educational Data Mining 2021 Shifting Landscape of Education: Improving Blended and Distance Learning. Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety ...
This paper will discuss how data mining can be utilized to improve the effectiveness of computer-based learning process. In its application the data will be processed in three stages: the collection, transformation, and analysis. ... Marisa, Fitri. "Educational Data Mining (Konsep Dan Penerapan)." Jurnal Teknologi Informasi: Teori, Konsep, dan ...
The mining of educational data can be defined as the application of techniques of traditional data mining on educational data analysis aimed at solving problems in the educational context [].To understand more about students, their academic performance, learning styles, and various issues directly linked to these, researchers are developing data mining methods to explore this context [].
Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings in which they learn. Such data types include student traces within interactive learning environments, learner ...
mining. Educational data mining (also referred to as "EDM") is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings, and using those methods to better understand students and the settings which they learn in ...
The 16th iteration of the conference, EDM 2023, will be held in the Indian Institute of Science Campus, Bengaluru, India, during July 11-14, 2023. The theme of this year's conference is " Educational data mining for amplifying human potential. " Not all students receive the education necessary to help them express their full potential be ...
Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information management systems, interactive learning ...
Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence.
Educational Data Mining is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students' behavior, interests and results in a better way. In recent years there have been various works related to this specialty and multiple data mining techniques derived from ...
Educational Data Mining. : This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first ...
