Mastering the Academic Writing Mindset: A Guide to Crafting Computer Science Papers

In the undergraduate study of computer science, a lecturer only teaches somethings that are in the literature (most likely in a open access textbook). Those knowledges may have been discovered before in several decades ago. A student is deemed to be good if they have perfectly finished assignments and have prepared well for their examinations. As an example, those students can easily get high grades for all fundamental courses (e.g., programming courses, linear algebra, probability and statistics, data structures, and design and analysis of algorithms) if they have worked extremely hard for the exercises that are provided in those open access textbooks or in class. Therefore, the undergraduate students do not need to have creativity (e.g., establish new knowledges) for obtaining an undergraduate degree. All they need to do is to consolidate their foundation. However, the most critical transition from undergraduate study to postgraduate study is to create new knowledges, which advance the state of the art in the computer science field. Moreover, postgraduate students need to write papers in a logical way (by telling a great story) so that other reviewers can accept them. In order to accomplish these two tasks, students need to change their mindsets for adapting to this new environment. In this open access book, we discuss this main theme in detail for analyzing the common mistakes that are easily made by new students and show the correct methodology for reading/writing papers. With this methodology, we believe that those students who are dedicated to computer science research can be very productive for publishing top-tier papers.

Tsz Nam Chan is currently a distinguished professor at Shenzhen University. His main research interests include (1) large-scale spatiotemporal data management and (2) large-scale data visualization. He is a productive researcher, who has already published over 30 papers in prestigious conferences and journals in the database, data management, and data mining fields, including SIGMOD, PVLDB, ICDE, SIGKDD, and TKDE. He also acts as the first author in 17 of these papers, demonstrating his incredible academic writing skills. He also has the experience for teaching the PhD-level course “Professional English” in the College of Computer Science and Software Engineering of Shenzhen University, which educates those PhD students to think for presenting/writing academic papers. He is an IEEE senior member and a recipient of the National Science Fund for Excellent Young Scholars (Overseas) in China (with age 32 at that time).

Dingming Wu is currently an associate professor at Shenzhen University. Her general research interests are in data analytics and management, and much of her research concerns foundations for value creation from spatio-temporal, geo-textual, and graph data, including data models and query processing, data mining, and machine learning.