Dr. Simran Setia Assistant Professor (SG) (91) 01792-239375 firstname.lastname@example.org For More Information Click here
Dr. Simran Setia is currently working as an Assistant Professor in the department of Computer Science and Engineering at JUIT. She is an alumnus of JUIT (Btech 2015 batch). She has completed her masters from Thapar University. In addition to this, she has completed her PhD, thesis entitled "Synergizing Collective Knowledge Building Portals" under the guidance of Dr. Sudarshan Iyengar at IIT Ropar. Her areas of interests are Human Computer Interaction, Collective Intelligence, Social Computing, and E-learning. She has published in various journals and international conferences during her PhD. She has experience in online teaching and has served as a Teaching Assistant for various NPTEL courses like The Joy of Computing Using Python, Discrete Mathematics, Social Networks.
She has also delivered online lectures for the NPTEL MOOC "The Joy of Computing using Python". She has qualified GATE twice (2015 and 2017) and is also a UGC-JRF awardee (2019) with 99.84 percentile.
Open Project Titles:
1. Predicting MOOC Engagement using Data Collected from Coursera and EdX
With the unprecedented rise of MOOCs, the distance based approach of E-learning has become highly popular. The fact that it provides accessible education at much affordable cost is one of the main reasons for its popularity among the masses. Also, the above factors like its affordable prices also lead to other problems such as high drop-out ratio. The learners tend to lose interest in the course with the passage of time and drop-out from the course. Hence, there is a need to study MOOC engagement which will help us to solve the issue of high drop-out ratio. For this, we have collected data from the popular MOOC providers like Coursera and EdX. The data can be used to study the factors that lead to high-drop-out ratio in MOOCs.
2. Calculating the Readability of a Text using NLP
Wikipedia has emerged to be one of the most prominent sources of information available on the Internet today. It provides a collaborative platform for editors to edit and share their information, making Wikipedia a valuable source of information. The Wikipedia articles have been duly studied from an editor’s point of view. But, the analysis of Wikipedia from the reader’s perspective is yet to be studied. Since Wikipedia serves as an encyclopaedia of information for its users, its role as an information securing tool must be examined. The readability of a written text plays a major role in imparting the intended comprehension to its readers. Readability is the ease with which a reader can understand the underlying piece of text. In this study, we study the readability of various Wikipedia articles.
3. Opinion Mining using Twitter Data
With the popularity of social networking site ‘Twitter’, it has become a breeding ground for opinions related to controversial societal and political issues. People from all over the world express their opinions on various subjects ranging from elections to movie reviews. With such a large amount of data where we have opinionated views of people round the globe, we can investigate and learn new ways of mining opinions from the text.
4. Predicting the GDP of a Country using the Wikipedia Data
Wikipedia is undoubtedly one of the most exhaustive sources of information. Every search query on Google points to Wikipedia as one of its top hits. However, it should be noted that the application of Wikipedia is not only restricted to acquiring information from this exhaustive encyclopaedia. We can also use the Wikipedia data to find a number of other parameters related to a country’s growth like GDP, literacy rate, inflation rate by just mining their Wikipedia articles.