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Educational Data Mining pp 29–64Cite as

A Survey on Pre-Processing Educational Data

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 524))

Abstract

Data pre-processing is the first step in any data mining process, being one of the most important but less studied tasks in educational data mining research. Pre-processing allows transforming the available raw educational data into a suitable format ready to be used by a data mining algorithm for solving a specific educational problem. However, most of the authors rarely describe this important step or only provide a few works focused on the pre-processing of data. In order to solve the lack of specific references about this topic, this paper specifically surveys the task of preparing educational data. Firstly, it describes different types of educational environments and the data they provide. Then, it shows the main tasks and issues in the pre-processing of educational data, Moodle data being mainly used in the examples. Next, it describes some general and specific pre-processing tools and finally, some conclusions and future research lines are outlined.

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Notes

  1. 1.

    The full name column covers the identification of subjects.

  2. 2.

    The student column covers the identification of subjects.

Abbreviations

AIHS:

Adaptive and intelligent hypermedia system

ARFF:

Attribute-relation File Format

CBE:

Computer-based education

CSV:

Comma-separated values

DM:

Data mining

EDM:

Educational data mining

HTML:

Hypertext Markup language

ID:

Identifier

IP:

Internet Protocol

ITS:

Intelligent tutoring system

KDD:

Knowledge discovery in databases

LMS:

Learning management system

MCQ:

Multiple choice question

MIS:

Management information system

MOOC:

Massive Open Online Course

OLAP:

Online Analytical Processing

SQL:

Structured Query Language

WUM:

Web Usage Mining

WWW:

World Wide Web

XML:

Extensible Markup Language

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Acknowledgments

This research is supported by projects of the Regional Government of Andalucía and the Ministry of Science and Technology, P08-TIC-3720 and TIN-2011-22408, respectively, and FEDER funds.

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  1. Department of Computer Science and Numerical Analysis, University of Córdoba Campus de Rabanales, Edificio C2-Albert Einstein, Córdoba, Spain

    Cristóbal Romero, José Raúl Romero & Sebastián Ventura

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Correspondence to Cristóbal Romero .

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    Alejandro Peña-Ayala

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Romero, C., Romero, J.R., Ventura, S. (2014). A Survey on Pre-Processing Educational Data. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_2

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