Model prediction for accreditation of public junior high school in Bogor using spatial decision tree

Giri, Endang Purnama; Arymurthy, Aniati Murni

doi:10.1109/icacsis.2014.7065831

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S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 71

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2018

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“…In [25] included distance of schools from a district office to predict school's performance and accreditation within the vicinity of the district office. It is also noticeable that number of attributes in a model will affect the accuracy.…”

Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7mentioning

confidence: 99%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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This paper classify the various existing predicting models that are used for monitoring and improving students' performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students' performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.

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Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7mentioning

confidence: 99%