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Finally, 'Big data architecture for adaptive learning systems' section describes a scalable, cloud-based learning analytics platform which runs generalized adaptive and analytical models on educational data in parallel.
The experiments were performed on educational data including study results of the undergraduate students enrolled in 2005 2008 following the program in Computer Science in the academic credit system at Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam, [3].
Previous research on educational data has demonstrated that Rasch fit statistics (mean squares and t-statistics) are highly susceptible to sample size variation for dichotomously scored rating data, although little is known about this relationship for polytomous data.
The high lower age limit is due to the reliance on educational data from the 1990 census, implying that generally even the youngest individuals included in our analyses should have reached their highest educational level by 1990.
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The recent experience of educational data collection on a census basis in Orissa through the e-Shishu project suggests that it is feasible to think of a national template of meaningful statistics that could be developed and refreshed regularly.
Based on the kind of data, our analysis was therefore based on published educational data in light of the public private partnership.
So, our work in this paper is dedicated to a clustering task on incomplete educational data.
As shown through the experimental results on real educational data sets, the clusters from our solution have better cluster quality as compared to some existing approaches.
Via the experimental results on real educational data sets with internal clustering validation measures, VQ_fk_nps is confirmed to outperform several existing approaches and thus, be effective for incomplete educational data clustering.
Generally speaking, VQ_fk_nps can perform the data clustering task effectively on incomplete educational data and produce the non-spherical clusters with higher compactness and better separation in the data space through the internal clustering validation measures such as Xie_Beni and S_Dbw.
Cristóbal Romero et al. (2014) survey the literature on pre-processing educational data to provide a guide or tutorial for educators and DS practitioners.
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