19.12.2017 - CSE 700/7000 Seminars


Seminars given for CSE700 Seminars Course;


Izhan  Fakhruzi

Title:  A Review on Predicting Student Graduation in Higher Education Using Data Mining


Abstract: Data Mining is increasingly used in higher institution to discover valuable information particularly in student databases. It brings to the concept of Educational Data Mining (EDM). EDM is a field that analyzes educational data to resolve its research issues using statistical, machine learning, and data mining algorithms. The emergence of EDM creates a good environment to all stakeholders in higher institution. One of them is graduation information. The data of graduated students from each academic year is an important source of information used as indicators of the school effectiveness by state and federal governments and accrediting agencies. Therefore, data mining can be used as an effective tool to predict graduation information of students to give insights to all stakeholders in higher institution whether the students are able to graduate on time, fast, late or even drop out from school. As a result, it assists the students on selecting suitable courses or field of study to their successful academic paths. In addition, it also assists the instructors to organize the curricula more effective to improve student academic performance. The purpose of this presentation is to provide an overview on data mining techniques that have been used to predict graduation information in higher institution through a survey of literature.


Muhammet Alkan

Title:  Opinion Mining

Abstract:  "Opinion Mining" is an interested area since online shopping sites and also microblogs have become widespread. Many people use this shopping sites (hepsiburada, amazon, ..) to buy what he/she wants. And also there are nearly hundreds of comments almost every product which helps to people to decide whether or not to buy the item, product or service. This customer reviews describe the experiences with the product/service are includes many important data like the product's features. Feature selection is an important step in opinion mining (there are many steps in opinion mining and it is still growing). Customers express product opinions separately according to individual features of the product (for example: camera, ram, capacity can be features for the mobile phones). And try to find what customers say about of the features of a product or a service details about which aspects of a product people felt good or bad (or neutral).

This page updated by Computer Engineering on 02.06.2017 01:54:45