Personal Contribution Of The Team

1175 WordsDec 10, 20145 Pages
Rigorous Self-assessment 1. Personal contribution to the team In this class, the team projects involve class presentations on assigned textbook topics and the final paper. I routinely actively participated in the discussion and prepared for our deliverables with high quality. I also actively looked for and suggested solutions to problems. Even when I was busy, I would make sure to deliver what I promised to the team on time. For every project, I am the person who did the most research. However, in the presentation, my voice was usually considered to be too soft, which may not have an optimal demonstration on our findings. 2. Personal contribution to the class I attended every class and thought actively in class. I focused on topic,…show more content…
He brought in speakers based on our research topic and interest. The external speakers’ background varies. They work in companies such as the public accounting companies, the corporate and the consulting firms. However, they all hold a position in accounting and use accounting systems every single day. Thus, I could learn the practical experience in different environment, which complemented my academic experience. This class is very different from the traditional lecture classes. The professor did not teach the concept and cases from the textbook. Instead, students needed to study on their own and took quizzes to test their understandings. This saved time and left more time to discuss in class. Usually, students would bring up new topics and knowledge accordingly, which motivated us to learn more and deeper. The topics regarding accounting systems include: • Integrated enterprise systems and cloud computing • Accounting and business intelligence • Accounting and sustainability intelligence • XBRL: intelligent business reporting • Fraud and internal control • Cybersecurity • The risk intelligent enterprise: enterprise risk management • Business reporting, visual analytics, and business performance management • Data mining • Techniques for predictive modeling • Text analytics, text mining, and sentiment analytics • Model-based decision making: optimization and multi-criteria
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