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|Title:||Learning from big data||Authors:||Le, Trung Hieu||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems||Issue Date:||2015||Abstract:||In the digital age, data is generated at an exponential rate due to the increasing trend of user-generated content and social networks. A lot of corporation are making use of this data set in order to track, understand, and predict outcomes in a wide range of applications and industries such as healthcare, commerce, education, and art. Therefore, big data analytics has become an increasingly popular trend since it enables the discovery of useful knowledge from complex data sets. However, the application of big data analytic is still very much limited in education. There are large quantities of data generated daily within big educational institutions like universities, polytechnic, junior college, high school, etc…. They can come from student’s submission of homework/assignment/report, intranet email communication, download/view of lecture materials (notes, video recordings). The objective of this project is to develop, through collecting large data input, an effective way to better assess academic performance of students. The project will start with a student performance estimation model which takes in various parameters and predict the possible outcome based on certain criteria.||URI:||http://hdl.handle.net/10356/63860||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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