• LEEMA MERY
• Benita Deepthi

Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences opinions, feelings, and concerns about the learning process. Data from such an instrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, forth first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.

Social Listening, Predictive Analysis, Automatic algorithms, Na€ıve Bayes

"Social Listening Using Predictive Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.535-547, June-2018, Available :http://www.jetir.org/papers/JETIR1806083.pdf

"Social Listening Using Predictive Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp535-547, June-2018, Available at : http://www.jetir.org/papers/JETIR1806083.pdf