The State of Educational Data Mining in 2009: A Review and Future Visions.

By Ryan Shaun Joazeiro de Baker, Kalina Yacef. Educational Data Mining, 2009

1,616 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/438cd004c7fa0a5ed74acc28bff1ac3d11b8c80a

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Citations in the corpus

Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, methodology): “Data mining techniques are commonly applied to identify patterns in these trace data (Baker & Yacef, 2009).”
Multimodal learning analytics (2013, background): “In addition to their unimodality, traditional assessment privileges product over process, and are divorced from actual learning activities.”
Educational data mining: A survey and a data mining-based analysis of recent works (2014, background): “The first is the work made by Baker and Yacef (2009).”
Educational Data Mining: A Review of the State of the Art (2010, background): “For example, Baker [20], [21] suggests four key areas of application for”
Measuring Cognitive and Metacognitive Regulatory Processes During Hypermedia Learning: Issues and Challenges (2010, methodology): “For example, work on state-transition analysis is currently being performed with machine learning techniques (e.g., Baker & Yacef, 2009; Biswas, Leelawong, Schwartz, Vye, & the Teachable Agents Group at Vanderbilt, 2005; Rus, Lintean, & Azevedo, 2009; Witherspoon, Azevedo, & D’Mello, 2008).”
COLLABORATIVE PROBLEM SOLVING AND THE ASSESSMENT OF COGNITIVE SKILLS: PSYCHOMETRIC CONSIDERATIONS (2013, background): “This kind of information has so far been the grist of educational data mining (EDM; Baker & Yacef, 2009; Romero & Ventura, 2005), a domain which has been intimately connected to recent developments in educational 12 technology and especially ITS.”
A reference model for learning analytics (2012, methodology): “From a technical perspective, EDM is the application of data mining techniques to educational data, and so, its objective is to analyze this type of data in order to resolve educational research issues and understand the setting in which students learn (Baker and Yacef, 2009; Barnes et al., 2009).”
Data mining in education (2013, background): “4 EDM has emerged as a research area in recent years aimed at analyzing the unique kinds of data that arise in educational settings to resolve educational research issues (Baker and Yacef, 2009).”
A Systematic Review on Educational Data Mining (2017, background): “Educational data is typically multi-level hierarchical and non-independent in nature, as suggested by Baker&Yacef [6] therefore a researcher must carefully choose the clustering”
Mining Big Data in Education: Affordances and Challenges (2020, methodology): “Given the fast-growing nature of relevant research, our synthetic review is primarily based on the literature of the past 5 years (2014–2018), while building on several review and synthesis papers (e.g., Baker & Yacef, 2009; Baker & Siemens, 2014; Pardos, 2017).”
Big Data and analytics in higher education: Opportunities and challenges (2015, methodology): “These approaches draw on a variety of literatures, including data mining and machine learning, psychometrics and other areas of statistics, information visualisation, and computational modelling (Baker & Yacef, 2009).”