Educational Data Mining: A Review of the State of the Art

Romero, Cristóbal; Ventura, Sebastián

doi:10.1109/tsmcc.2010.2053532

Cited by 1,465 publications

(796 citation statements)

References 134 publications

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“…The problem of predicting low performance or even the possible drop-out of students (Pierrakes, Xenos, Panagiotakopoulos, & Vergidis, 2004;Romero & Ventura, 2010;Pal, 2012) has long been recognized and is one of the hottest topics in EDM. A lot of research has been devoted on how to apply EDM techniques effectively, in order to create models that can predict dropout rates and school failure (Romero & Ventura, 2010).…”

Section: Related Workmentioning

confidence: 99%

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Student Admission Data Analytics for Open and Distance Education in Greece

et al. 2017

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Over the last few decades, distance learning has become very popular, as a result of the many pros it offers, along with its flexibility. The need for a better understanding of the data originating from such educational environments has led to the rise of the Educational Data Mining research field. However, most of the studies so far focus on the analysis of the data being collected during and/or after the distance learning courses. In this paper, we study the demographical data related to student applications for acceptance in distance learning programs offered by the Hellenic Open University, during the decade from 2003 to 2013. Our study aims at analyzing the data, and discovering patterns and knowledge that can be used to help the strategic placement of the university, and the improvement of the experience that offers to its students. Moreover, we attempt to correlate the discovered findings with the social and financial status of the applicants' environment.Keywords Data analytics, admission demographics, registration applications, distance education IntroductionThe widespread use of computers and the Internet contributed in making distance learning popular (Bozkurt et al., 2015). Nowadays, more and more people are willing to enroll in distance learning programs, as they have several attractive features, such as the option of a self-paced study, the freedom to study whenever and wherever you like, and the fact that it does not matter where you live, as you can gain a degree from anywhere in the world. Therefore, there are almost no restrictions, and most of the time their quality is as good as the conventional courses, which require the physical presence of the students in a classroom. What is more, distance learning courses are often cheaper to register. The Hellenic Open University (HOU) was officially established in 1997 and is the only university in Greece offering exclusively distance learning courses. It consists of four Schools (Humanities, Social Sciences, Science and Technology, and Applied Arts) and offers undergraduate and postgraduate distance learning courses to adult learners. Since its establishment, the HOU has evolved and has been attracting more and more students. People of all ages have shown great interest in studying through a distance learning program offered by the HOU. In Greece, the acceptance of a student in a public university is based on her performance in the Panhellenic exams. A lot of students may fail to obtain the required score or may have to move to a different city in order to study the subject of their choice. Thus, people are interested in the HOU not only due to financial reasons, but also because it provides a second chance towards studying the subject of their choice.In this paper, we analyze the demographical data originating from applications of prospective students to distance learning programs offered by the HOU. The data used were collected in the period from 2003 to 2013. Our study focuses on discovering patterns and knowledge that can elevate the educa...

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Section: Related Workmentioning

confidence: 99%

“…A lot of research has been devoted on how to apply EDM techniques effectively, in order to create models that can predict dropout rates and school failure (Romero & Ventura, 2010). Data mining techniques.…”

Section: Related Workmentioning

confidence: 99%

Student Admission Data Analytics for Open and Distance Education in Greece

et al. 2017

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“…Furthermore, issues of time, sequence, and context also play important roles in the study of educational data. The work in this area can be considered to be relevant to the domain of recommender systems for educational applications, since many recommender systems apply data mining techniques in order to cluster users, find correlations and improve their recommendations (Romero and Ventura 2010).…”

Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

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Manouselis¹,

Drachsler

Verbert

et al. 2013

SpringerBriefs in Electrical and Computer Engineering

106

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“…Disciplines such as Learning Analytics (LA) [8] and Educational Data Mining (EDM) [9] are studying the way learners perform online activities within Virtual Learning Environments (VLE). Their main goal is to better understand educational processes to find ways to improve them and assure an accurate assessment of the student.…”

Section: Introductionmentioning

confidence: 99%

Applying standards to systematize learning analytics in serious games

Serrano-Laguna

Martínez‐Ortiz

Haag

et al. 2017

Computer Standards & Interfaces

103

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Learning Analytics is an emerging field focused on analyzing learners’ interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

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Educational Data Mining: A Review of the State of the Art

Cited by 1,465 publications

References 134 publications

Student Admission Data Analytics for Open and Distance Education in Greece

Student Admission Data Analytics for Open and Distance Education in Greece

Recommender Systems for Learning

Applying standards to systematize learning analytics in serious games

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