의사결정나무를 활용한 실시간 온라인 한국어 강의에서의 학습자 만족도 분석

An Analysis of Learner Satisfaction with Real-time Online Korean Language Lectures Using a Decision Tree: Focusing on Factors of Interaction

This study identified factors of interaction influencing the satisfaction of 74 foreign learners with real-time online Korean lectures using a survey design. The study developed two types of prediction models for satisfaction with learning effects and satisfaction with lectures using a decision tree algorithm, which produced the best accuracies of 75.0% and 87.5%, respectively. Four common influential factors affected the two types of prediction models: gathering feedback on assessments, providing appropriate time, identifying the learner’s learning status, and providing interesting materials. In addition, this study developed four rules for satisfaction with learning effects and six rules for satisfaction with lectures from the prediction models. Using the derived rules, this study proposed helpful directions for enhancing foreign learners’ satisfaction in real-time online Korean language lectures.