We are now in the era of 'big data'.
Big data has become a common term in our daily lives.
If the catchphrase "knowledge and information society" helped us understand the key factors that will transform our lives, big data allows us to predict how these key factors—knowledge and information—will function and drive the future.
Big data processing and analysis not only serve as a foundation for interpreting and understanding the current state, but also for predicting and preparing for the future.
The problem is the standards and tools to process and analyze big data.
As someone who studies language education, how to process the vast amount of language data has always been a major concern.
When I was preparing my doctoral dissertation, I studied corpus linguistics.
Corpus linguistics enables us to scientifically verify and supplement the intuitions of native speakers, thereby securing a more valid explanation of the characteristics of language.
In some respects, big data analysis and corpus linguistics share a data-driven and analytical research approach in that they both attempt to solve research problems based on actual communication data.

As a big data analysis method, especially as a method for processing a wide range of language data, 'language network analysis' has recently received a lot of attention.
By focusing on vocabulary and understanding the network between words, it is possible to interpret the characteristics of the data from various perspectives.
Another method that is attracting attention from a language education perspective is the topic modeling technique.
Topic modeling is a technique that automatically extracts 'topics' from the data through machine learning.
This book analyzes research trends in Korean language education across the areas of speaking, reading, writing, vocabulary, integration, and culture, in collaboration with several students who participated in graduate school classes in 2019.

A search of the Korea Institute of Information Science (KCI) shows that research on Korean language education began around the 1980s.
This period seems to be the embryonic stage of Korean language education research.
As a result of analyzing research trends in each field, it was found that research has been actively conducted since the 2000s.
As Korean language education has developed rapidly, the number of papers related to Korean language education has grown enormously.
It is impossible for one person to review all the numerous papers.
Scientifically processing such a wide range of linguistic data requires scientific criteria and valid analytical tools.
Language network analysis and topic modeling techniques are useful for processing such data.
In this book, we aimed to scientifically analyze research trends in Korean language education by identifying the networks among words and extracting key topics based on the vocabulary revealed in papers related to Korean language education.
I sincerely hope that this book will be of small but great help to Korean language education researchers.
Comprehensively understanding the trends in Korean language education research over the years is a fundamental task that Korean language education researchers must undertake at least once.
I hope this book will be of great help in understanding the current state of Korean language education research and setting future research directions.