@inProceedings{
 title = {Educational data mining: A literature review},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Clustering,EDM,Learning management systems,Prediction,Relationship mining},
 pages = {87-94},
 volume = {520},
 publisher = {Springer Verlag},
 id = {20fd6368-ef41-3e0f-bd9a-2aaebb7a538c},
 created = {2020-02-03T14:16:32.690Z},
 accessed = {2020-02-03},
 file_attached = {false},
 profile_id = {bfdabac2-d7f2-3c5b-aa7a-06431c0ae35e},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-03T14:16:33.102Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {af1b8628-85f8-4733-9f52-121159400517},
 private_publication = {false},
 abstract = {The adoption of learning management systems in education has been increasing in the last few years. Various data mining techniques like prediction, clustering and relationship mining can be applied on educational data to study the behavior and performance of the students. This paper explores the different data mining approaches and techniques which can be applied on Educational data to build up a new environment give new predictions on the data. This study also looks into the recent applications of Big Data technologies in education and presents a literature review on Educational Data Mining and Learning Analytics.},
 bibtype = {inProceedings},
 author = {Silva, Carla and Fonseca, José},
 booktitle = {Advances in Intelligent Systems and Computing}
}

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