Greetings.

I am from IGSE’s Marketing Department.

English education is going through a rapid transition in all facets with new technologies being introduced every day in the era of the fourth Industrial Revolution. In particular, the use of AI and big data will soon play a pivotal role in English education. To engage with this crucial area, IGSE has introduced a new course, ‘English Education and Big Data’, starting from this semester. We had the chance to have an interview with the professor of this course, Yong Guk Won.

Q1. We would like to know more about the course, but, first, we would like to ask a little bit about you. How did you become an expert in this field? Please give us a self-introduction.

 Like most people in English education, I was initially not that interested in data-related work. I came to graduate school to study more about efficient methods for English teaching, and I started to become interested in data analysis little by little while learning about research methods. After finishing my master’s program, I worked at a corporate-related research institute of English education. While working there, I felt the need to learn more about data analysis and decided to study Statistics at an open university to learn some basic knowledge of data analysis.

After that, I took a doctoral course to study automatic language assessment. Back when I was working at a company, I became interested in customized learning. Since the learner’s level must be assessed prior to conducting customized learning, I wanted to further study language assessment, and so I chose it as my major. Among several options within the major, I was particularly interested in the automatic assessment of speaking and writing. Since there are limitations in assessing linguistic ability through an objective test and the number of professors available is not always sufficient, I thought getting help from a computer system based on automatic language assessment would be helpful to realize customized learning. Since coding and statistics are the foundations of conducting research on the automatic assessment of English language speaking and writing, I decided to study them through a doctoral program, but it was not easy. I took three courses related to natural language processing and barely passed them. My academic advisor at the time told me it would be hard to graduate if I tried to learn everything at school and that I should focus on the things I could do well. So, I put the coding aside and focused on statistics and assessment-based classes, which I found easier to study.

After getting a doctor’s degree with a thesis based on a study on speaking assessment, I began conducting research on automatic language assessment while also studying natural language processing and big data analysis. At first, I joined a study group with those in the natural language processing field and thought about possible ways to utilize AI technology in this field. After that, I took related courses offered by the government comprising about 1,000 hours of coding classes and a 10-month-long natural language processing course. During this period, I was able to get some practical experience in coding, which up to that point I had only learned about theoretically, and applied it in related studies. Now that I look back, I think it would have been even better, although challenging, if I had started learning coding earlier as I could have conducted more in-depth research.

Q2. What is the purpose of the course ‘English Education and Big Data’ and which students would gain the most from this course?

English Education and Big Data is a course specifically designed for teachers, study material developers, content planners, etc. who are interested in creating English learning materials. In particular, the course focuses on basic knowledge for those who plan to make various English learning courses while creating a large amount of content. To create English learning content, it is necessary to develop English texts (corpus) then refine and classify expressions as needed. In the past, this had to be done manually, sentence by sentence; however, it is difficult to create content in large volumes that are up-to-date and timely. So, this course would be helpful for people who are interested in the automation of text data collection, refinement, and classification and the utilization of these texts in content development and research.

Q3. Can you tell us more specifically about what students can learn from the course ‘English Education and Big Data’?

This course teaches text mining techniques that are required for language education. Students will learn and practice collecting and refining texts from movie commentaries, comments on YouTube videos, and various other roots using Python and R program. Students will learn to classify coding-based corpuses by subject and the statistical text analysis method that analyzes the text’s context. The class involves lectures and labs. Unlike other graduate institutes, we do not put much emphasis on reading thesis papers and doing presentations. There are many text analysis techniques to cover, and because most of the students are beginners, lectures mostly consist of the lecturer’s explanation and students’ coding practice. Students are able to get hands-on experience in data analysis using text mining techniques they have learned in class, and at the end of the semester the students are asked to write a report on their text analysis as a final assignment. This is a part of practical practice.

This course puts emphasis on getting students used to coding while practicing statistics techniques for text analysis. Students can acquire the basic knowledge needed for content development, which also helps them move to the next stage – AI-based language assessment methods, including automatic speaking and writing assessment.

Q4. You are also the 7th graduate of IGSE and a senior to the current students in the ELT Materials and Digital Content Development program. What would be your advice to students who are interested in the field of big data and AI.

I cannot say that I am an expert in AI. I only use some related technologies to conduct language evaluation research. If I dared to predict the future of English education, I would say I think machines will outdo human teachers when it comes to repetitive pattern-based teaching. What is important here is the expandability within the expression ‘repetitive pattern-based’. As I already mentioned in my answer to a previous question, individually customized learning is also analyzable by patterns. So, it has become easier for a computer system to replace the role of human teachers.

At graduate schools, students can take courses based on mathematics, such as statistics. It might be challenging to encounter math after years of neglecting it since high school, and it can be helpful if one approaches math (statistics) as a language. Once students have basic mathematics (statistics) knowledge, they can easily understand AI algorithms. If you are determined to study English education, I think it is a good idea to learn about mathematics (statistics) and coding, which are other types of language used to communicate in a different field. Starting is the hardest step. This is true for English education, and the same goes for mathematics (statistics) and coding. I am sure that all the students would do well as they have related experience. The good part is that you don’t have to solve mathematical problems in a time-limited situation. Being familiar with the concepts would be enough. If you have an opportunity to take such a course, you should enroll yourself without questioning – that, you can do later.