Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138042
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dc.contributor.authorMuhammad Fadhli Abdul Rahimen_US
dc.date.accessioned2020-04-22T07:18:47Z-
dc.date.available2020-04-22T07:18:47Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/138042-
dc.description.abstractThe rise of the internet, the ubiquity of computing devices and the surge of developments in the field of Machine Learning has created an opportunity for next generation learning. Beyond moving online, learning when combined with the power of machine learning can help make the experience more engaging to the student. This increase in engagement will help the student have a higher level of autonomy over his work thus, leading to a better academic performance and a more enjoyable learning experience overall.The purpose of this project is to create a web application coupled with the implementation of machine learning models to create an engaging interactive learning experience. With the use of Bayesian Modelling and Naïve Bayes Classifier, the web application would be able to respond to the input that the user gives and respond accordingly with customised content suited to the user.The limitations of the implementation of these machine learning models will also be explored further. Future work for the web application would include the implementation of diverse question sets and content delivery.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE19-0540en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleVirtual tutor for secondary students in science subjectsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWeichen Liuen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.researchParallel and Distributed Computing Centreen_US
dc.contributor.supervisoremailliu@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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