Providing effective recommendation of learning materials for students

• Main Author: Devia Nitin Kumar
• Other Authors: School of Computer Engineering
• Format: Final Year Project
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/50916
• Institution: Nanyang Technological University

In these modern days, everyone is equipped with the basic knowledge in technology and with that learning methods have been transformed. With this knowledge in hand, students are no longer limited to the traditional ways of gaining knowledge from classes. Instead, students can now acquire knowledge from the different types of resources available across the Internet. However, this is a rather time consuming task as the relevant material may not be found easily. E-learning systems are becoming the prototype of today’s academic curriculum and to resolve this issue, the team came up with a one stop learning environment education system which hosts relevant resources (videos, website links etc. stored in the database) related to a particular course’s concept(e.g. Software Process Model concept in the Software Engineering course). This enables students to save time and also make learning a lot more convenient, easier and fun. The idea of obtaining relevant resources based on a course’s concept can be improvised with the integration of an intelligent web crawler. The proposed system must be able to predict underperforming students so that course managers can render aid to the students as soon as possible should they need help. Students can then use the recommendation page to seek help from the learning materials recommended by the system. Hopefully with the additional help, students can attain an improved grade at the end of the semester. In this report, the author describes some of the related works and research done with respect to the project. The author also discusses the system architecture as well as touch on the database creation and design, basic function implementations, integration of the recommendation algorithms in the recommendation module, enhancement of the web interface and the development of the initial phase of the intelligent web crawler. Finally, the author concludes the report with a summary and possible future works of the system