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IEEE DOT NET PROJECT ABSTRACT 2016-2017 ONLINE PROJECT AND ASSIGNMENT SUBMISSION PORTALABSTRACT:- Online Project and Assignment Submission System is a system that enable the student to submit their assignment or project online without submitting any physical file. This system is integrated with Turnitin system to check the plagiarism percentage. Before the submission, the student needs to update their progress to the system and the lecturer able to view the progress and give comments online. OPAS is providing an online discussion, document sharing for student and lecturer and web real time communication technology. Any project that behind the schedule, the system will be able to send an alert to the student to notify the status. This system is reviewing several similar system and technologies that going to be used in developing the prototype. The system design for assignment and project submission process is being discussed. The working prototype was developed and some functionality is highlighted. The impact of the system to students, lecturers and university are discussed.Objective:- To increase student and supervisor accessibility and availability. To ensure projects are always on-track and on-time by proving project management and monitoring. To facilitate file-sharing (increase accessibility and availability of files) as well as reducing redundancy through online repository. To facilitate communication and collaboration between supervisor and student by use of collaboration tools.Existing System:- Manual Errors can occur Time Wastage Process Cost Expensive process Performance is lowProposed System:- The proposed system helps reducing and minimizing human error, capable to assist supervisors in process controlling and managing students. Supervisors can check the student projects’ statuses, the uploaded files online and assist them while they are working in the project if necessary. The proposed system decreases the complexity of managing projects for student by providing them with the current status of their projects and the progresses with their supervisors. Moreover, the proposed system allows supervisors to share documents and files with their students and communicate with them through file sharing and text sharing principles.
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IEEE DOT NET PROJECTS ABSTRACT 2016-2017 MINING SOCIAL MEDIA DATA FOR UNDERSTANDING STUDENTS’ LEARNING EXPERIENCES ABSTRACT:- 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 un-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, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.INTRODUCTION:- To learn student experience from social media like twitters using workflow. To integrate both qualitative analysis and large-scale data mining techniques. To explore engineering students’ informal conversations on Twitter. In order to understand issues and problems students encounter in their learning experiences. Twitter about problems in their educational experiences they are:- Heavy study load, Lack of social engagement, Negative emotion, Sleeping problems. Learning analytics and educational data mining has focused on analyzing structured data obtained from course management systems (CMS). Classroom technology usage or controlled online learning environments to inform educational decision-making. Twitter is a popular social media site. Its content is mostly public and very concise (no more than 140 characters per tweet). Twitter provides free APIs that can be used to stream data. Therefore, I chose to start from analyzing students’ posts on twitter. Naive Bayesian Classification algorithm to be used in these concepts. I found Naive Bayes classifier to be very effective on dataset compared with other state-of-the-art multi-label classifiers. EXISTING SYSTEM:-• Data sets they collect do not unproblematic model or mirror the world events.• Aggression level of the network as high or as low as desired.• The mass media in Twitter, unlike the traditional media networks.PROPOSED SYSTEM:-• Foursquare, a popular location check-in service, the importance of analyzing social media as a communicative rather than representational system.• A quantitative analysis to maximize the relevance of information in networks with multiple information providers.• Develop a computational framework that checks, for any given topic, how necessary and sufficient each user group is in reaching a wide audience.REQUIREMENT SPECIFICATION:-5.1 System Requirements:-5.1.1 Hardware Requirements:- System : Pentium IV 2.4 GHz Hard Disk : 40 GB Floppy Drive : 1.44 Mb Monitor : 15 VGA Colour Mouse : Logitech Ram : 512 Mb Software Requirements:- Operating system : Windows XP Technology Used : Microsoft .NET Backend Used : SQL Server CONCLUSION: Our study is beneficial to researchers in learning analytics, educational data mining, and learning technologies. It provides a workflow for analyzing social media data for educational purposes that overcomes the major limitations of both manual qualitative analysis and large scale computational analysis of user-generated textual content. Our study can inform educational administrators, practitioners and other relevant decision makers to gain further understanding of engineering students’ college experiences. As an initial attempt to instrument the uncontrolled social media space, we propose many possible directions for future work for researchers who are interested in this area. We hope to see a proliferation of work in this area in the near future. We advocate that great attention needs to be paid to protect students’ privacy when trying to provide good education and services to them.
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