An approachable analytical study on big educational data mining

Saeed Aghabozorgi; Hamidreza Mahroeian; Ashish Dutt; Teh Ying Wah; Tutut Herawan

doi:10.1007/978-3-319-09156-3_50

An approachable analytical study on big educational data mining

Saeed Aghabozorgi, Hamidreza Mahroeian, Ashish Dutt, Teh Ying Wah, Tutut Herawan

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

17 Citations (Scopus)

Abstract

The persistent growth of data in education continues. More institutes now store terabytes and even petabytes of educational data. Data complexity in education is increasing as people store both structured data in relational format and unstructured data such as Word or PDF files, images, videos and geo-spatial data. Indeed learning developers, universities, and other educational sectors confirm that tremendous amount of data captured is in unstructured or semi-structured format. Educators, students, instructors, tutors, research developers and people who deal with educational data are also challenged by the velocity of different data types, organizations as well as institutes that process streaming data such as click streams from web sites, need to update data in real time to serve the right advert or present the right offers to their customers. This analytical study is oriented to the challenges and analysis with big educational data involved with uncovering or extracting knowledge from large data sets by using different educational data mining approaches and techniques.

Original language	English
Title of host publication	Computational Science and Its Applications, ICCSA 2014
Subtitle of host publication	14th International Conference, Proceedings, Part V
Publisher	Springer
Pages	721-737
Number of pages	17
ISBN (Print)	9783319091556
DOIs	https://doi.org/10.1007/978-3-319-09156-3_50
Publication status	Published - 2014
Externally published	Yes
Event	International Conference on Computational Science and Its Applications, ICCSA 2014 - Guimaraes, Portugal Duration: 30 Jun 2014 → 3 Jul 2014 Conference number: 14th https://link.springer.com/book/10.1007/978-3-319-09156-3

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 5
Volume	8583 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Computational Science and Its Applications, ICCSA 2014
Abbreviated title	ICCSA 2014
Country/Territory	Portugal
City	Guimaraes
Period	30/06/14 → 3/07/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-09156-3

Keywords

Analytical Study
Big Data
Data Mining
Educational Data
Educational Data Mining

Access to Document

10.1007/978-3-319-09156-3_50

Cite this

Aghabozorgi, S., Mahroeian, H., Dutt, A., Wah, T. Y., & Herawan, T. (2014). An approachable analytical study on big educational data mining. In Computational Science and Its Applications, ICCSA 2014: 14th International Conference, Proceedings, Part V (pp. 721-737). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8583 LNCS, No. PART 5). Springer. https://doi.org/10.1007/978-3-319-09156-3_50

Aghabozorgi, Saeed ; Mahroeian, Hamidreza ; Dutt, Ashish et al. / An approachable analytical study on big educational data mining. Computational Science and Its Applications, ICCSA 2014: 14th International Conference, Proceedings, Part V. Springer, 2014. pp. 721-737 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 5).

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abstract = "The persistent growth of data in education continues. More institutes now store terabytes and even petabytes of educational data. Data complexity in education is increasing as people store both structured data in relational format and unstructured data such as Word or PDF files, images, videos and geo-spatial data. Indeed learning developers, universities, and other educational sectors confirm that tremendous amount of data captured is in unstructured or semi-structured format. Educators, students, instructors, tutors, research developers and people who deal with educational data are also challenged by the velocity of different data types, organizations as well as institutes that process streaming data such as click streams from web sites, need to update data in real time to serve the right advert or present the right offers to their customers. This analytical study is oriented to the challenges and analysis with big educational data involved with uncovering or extracting knowledge from large data sets by using different educational data mining approaches and techniques.",

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Aghabozorgi, S, Mahroeian, H, Dutt, A, Wah, TY & Herawan, T 2014, An approachable analytical study on big educational data mining. in Computational Science and Its Applications, ICCSA 2014: 14th International Conference, Proceedings, Part V. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 5, vol. 8583 LNCS, Springer, pp. 721-737, International Conference on Computational Science and Its Applications, ICCSA 2014, Guimaraes, Portugal, 30/06/14. https://doi.org/10.1007/978-3-319-09156-3_50

An approachable analytical study on big educational data mining. / Aghabozorgi, Saeed; Mahroeian, Hamidreza; Dutt, Ashish et al.
Computational Science and Its Applications, ICCSA 2014: 14th International Conference, Proceedings, Part V. Springer, 2014. p. 721-737 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8583 LNCS, No. PART 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - The persistent growth of data in education continues. More institutes now store terabytes and even petabytes of educational data. Data complexity in education is increasing as people store both structured data in relational format and unstructured data such as Word or PDF files, images, videos and geo-spatial data. Indeed learning developers, universities, and other educational sectors confirm that tremendous amount of data captured is in unstructured or semi-structured format. Educators, students, instructors, tutors, research developers and people who deal with educational data are also challenged by the velocity of different data types, organizations as well as institutes that process streaming data such as click streams from web sites, need to update data in real time to serve the right advert or present the right offers to their customers. This analytical study is oriented to the challenges and analysis with big educational data involved with uncovering or extracting knowledge from large data sets by using different educational data mining approaches and techniques.

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Aghabozorgi S, Mahroeian H, Dutt A, Wah TY, Herawan T. An approachable analytical study on big educational data mining. In Computational Science and Its Applications, ICCSA 2014: 14th International Conference, Proceedings, Part V. Springer. 2014. p. 721-737. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 5). doi: 10.1007/978-3-319-09156-3_50

An approachable analytical study on big educational data mining

Abstract

Publication series

Conference

Keywords

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