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

By Cristóbal Romero, Sebastián Ventura. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2010

2,096 citations recorded by the source corpus.

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/733943a2b851cd3c42414295440ef5cd1a038bc5

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Citations in the corpus

Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. (2016, methodology): “…and Means (2012) Campbell and Oblinger (2007) Clow (2012) Clow (2013) Dawson and Siemens (2014) Johnson, Levine, Smith, and Stone (2010) Reyes (2015) Romero and Ventura (2010) Learning Analytics Benefits Althubaiti and Alkhazim (2014) AlShammari, Aldhafiri, and Al-Shammari (2013) Arnold and…”
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, background): “Furthermore, indicative reviews on former work can be found in Romero and Ventura (2007) and Romero and Ventura (2010).”
Big Data and analytics in higher education: Opportunities and challenges (2015, methodology): “…aimed at automating the process for detecting interpretable patterns and carrying out predictions, educational data mining tends to focus on developing new tools for discovering patterns in data and applying tools and techniques to analyse large sets of data (Luan, 2002; Romero & Ventura, 2010).”
Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan (2012, background): “…learning analytics also draws on the related fields of web analytics, academic analytics (Goldstein & Katz, 2005), educational data mining (see Romero & Ventura (2010) for review) and action analytics (Norris, Baer, Leonard, Pugliese, & Lefrere, 2008) to support decision-making and strategic…”
Seven HCI Grand Challenges (2019, background): “…appropriate feedback, providing recommendations to students, predicting students ’ performance, detecting undesirable student behaviors, grouping students (according to personal characteristics or preferences), offering pedagogical support, or developing concept maps (Romero & Ventura, 2010).”
Learning analytics and educational data mining: towards communication and collaboration (2012, background): “A very comprehensive review of EDM research can be found in [6].”
A Systematic Review on Educational Data Mining (2017, background): “Mining (EDM) applies machine-learning, statistics, Data Mining (DM), psycho-pedagogy, information retrieval, cognitive psychology, and recommender systems methods and techniques to various educational data sets so as to resolve educational issues [1].”
Cosine similarity to determine similarity measure: Study case in online essay assessment (2016, background): “EDM focuses on academic data mining such as Intelligent Tutoring System (ITS), Learning Management System (LMS), Student Behavior Modeling, Predicting Performance and Assessment of student's learning performance [7].”
Web usage mining for predicting final marks of students that use Moodle courses (2013, neutral): “Educational DataMining(EDM)isanemerginginterdisciplinaryresearcharea thatdealswiththeapplicationofDataMining(DM)techniques toeducationaldata[14].”
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning (2014, methodology): “…while EDM is related to the development of methods for analysis of learning data (Baker & Yacef, 2009) from a mostly technical point of view – e.g. Romero and Ventura (2007, 2010) –, learning analytics has to do with the interpretation and contextualization of those data for the improvement of…”
Predicting academic performance of students from VLE big data using deep learning models (2020, background): “It is also interpreted as emphasizing on individually assessing the learners’ performance and its corresponding impact on the institutions performance (Baker & Journal Pre-proof Inventado, 2014; Daniel, 2017; Romero & Ventura, 2010).”
Preface to the special issue on data mining for personalised educational systems (2011, methodology): “In order to resolve this issue, data mining can be used to develop user/student models as well as automatic educational systems for personalisation and recommendation (Romero and Ventura 2010).”
Educational data mining: A survey and a data mining-based analysis of recent works (2014, background): “The fourth EDM review corresponds to Romero and Ventura (2010), who enhanced their prior EDM survey adding 225 works, keeping the former seven references of the 1990s, and including three papers published in 2010.”
Educational data mining and learning analytics: An updated survey (2020, background): “Analyzing this important type of papers, the first and most popular review of EDM research was published by Romero and Ventura (2007), and it was followed by a more complete (Romero and Ventura, 2010a) and a more comprehensible (Romero and Ventura, 2013) reviews by the same authors.”