Implementation of Machine Learning in the Education Sector

Author(s):  
Prayag Tiwari ◽  
Jia Qian ◽  
Qiuchi Li
Author(s):  
Dhruvil Shah ◽  
Devarsh Patel ◽  
Jainish Adesara ◽  
Pruthvi Hingu ◽  
Manan Shah

AbstractAlthough the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.


Author(s):  
Manoj L. Bangare ◽  
Pushpa M. Bangare ◽  
Elia Ramirez-Asis ◽  
Robert Jamanca-Anaya ◽  
Chirasak Phoemchalard ◽  
...  

2019 ◽  
Vol 1 (1) ◽  
pp. 103-110
Author(s):  
Ariyan Zubaidi ◽  
Ramdani Ramdani

Chatbot is considered as one of the hottest technology in recent years. It is used by various sector to serve its customer automatically. It gives benefits to business, primarily in customer care. Chatbot can be divided into 2 (two) types. One operates based on set of rules. It can be used with set of spesific command. The other types uses machine learning and artificial intelligence to provide its service. Chatbot can be utilised as well in education sector. Campus gives service to its students or faculty by providing information and academic service. Commonly, academic information and service has supported by information technology, usually in particular website.  But, not all of the services are available and newest information does not always accessed timely. Hence, this research built a chatbot based on Telegram to provide information and academic services in informatic engineering department of mataram university. Telegram provides API that can be used to develop bot. The bot is built using Python, SQLite as the database and React. Prototyping model is used as a development method. The bot prototype is able to broadcast newest information to its register user and provides academic service such as theses program and internship program.


2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  

Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols

Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  

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