Towards a DSL for Educational Data Mining

Nowadays, most companies and organizations rely on computer systems to run their work processes. Therefore, the analysis of how these systems are used can be an important source of information to improve these work processes. In the era of Big Data, this is perfectly feasible with current state-of-art data analysis tools. Nevertheless, these data analysis tools cannot be used by general users, as they require a deep and sound knowledge of the algorithms and techniques they implement. In other areas of computer science, domain-specific languages have been created to abstract users from low level details of complex technologies. Therefore, we believe the same solution could be applied for data analysis tools. This article explores this hypothesis by creating a Domain-Specific Language (DSL) for the educational domain.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Marta E. Zorrilla,et al.  A service oriented architecture to provide data mining services for non-expert data miners , 2013, Decis. Support Syst..

[3]  William Rice,et al.  Moodle 1.9 E-Learning Course Development , 2008 .

[4]  Manuel Filipe Santos,et al.  Binding data mining to final business users of business intelligence systems , 2012 .

[5]  Thomas Kühne,et al.  Matters of (Meta-) Modeling , 2006, Software & Systems Modeling.

[6]  José Luis Sierra Language-Driven Software Development (Invited talk) , 2014, SLATE.

[7]  Frank Budinsky,et al.  EMF: Eclipse Modeling Framework 2.0 , 2009 .

[8]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[9]  Bart Baesens,et al.  Analytics in a Big Data World: The Essential Guide to Data Science and its Applications , 2014 .

[10]  M Mernik,et al.  When and how to develop domain-specific languages , 2005, CSUR.

[11]  Robert Wrembel,et al.  Data Warehouses And Olap: Concepts, Architectures And Solutions , 2006 .

[12]  Anneke Kleppe,et al.  Software Language Engineering: Creating Domain-Specific Languages Using Metamodels , 2008 .

[13]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[14]  Marta E. Zorrilla,et al.  Enabling Non-expert Users to Apply Data Mining for Bridging the Big Data Divide , 2013, SIMPDA.

[15]  Heiko Behrens,et al.  Xtext: implement your language faster than the quick and dirty way , 2010, SPLASH/OOPSLA Companion.

[16]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[17]  Frank Budinsky,et al.  Eclipse Modeling Framework , 2003 .

[18]  Richard F. Paige,et al.  Integrated Model Management with Epsilon , 2011, ECMFA.

[19]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[20]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[21]  William H. Dutton,et al.  Clouds, big data, and smart assets: Ten tech-enabled business trends to watch , 2010 .

[22]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.