Course knowledge build-up and sharing via gathering of Q & A

The rapid advancement in technology has caused a shift in the education domain, from teacher-centric to learner-centric. Due to mass-adoption of technologies, educational institutions have been advocating the use of computer-mediated communication technologies to encourage self-learning among studen...

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Bibliographic Details
Main Author: Goh, wei Yang
Other Authors: Tan Kheng Leong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/137907
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Institution: Nanyang Technological University
Language: English
Description
Summary:The rapid advancement in technology has caused a shift in the education domain, from teacher-centric to learner-centric. Due to mass-adoption of technologies, educational institutions have been advocating the use of computer-mediated communication technologies to encourage self-learning among students. There is a myriad of Community Question and Answer (CQA) platforms available on the internet such as Yahoo! Answers, Stack Overflow and Quora that provide peer-topeer learning and cater for a wide spectrum of topics. Such CQA systems have also been proven to help students and technical professionals. However, since such platforms cater to such a diverse and wide range of topics, searching answers for a niche area like academic-related topics might be very difficult for students. In this study, a web based CQA platform that allows students to learn from one another, and functions as a knowledge database to allow the impartment of acquired knowledge was implemented. Firstly, some existing popular CQA systems were reviewed and comparisons on their existing features were made. Thereafter, some of those useful features were improved upon and replicated in the proposed QUASS system. Furthermore, an email parser engine was implemented to further extend the functionalities of the proposed system. Several strategies on the implementation were explored and discussed. The performance of each strategy was evaluated by comparing the similarity between their parsed content and the actual email content. Finally, conclusions and future enhancements were discussed and proposed.