Predicting cognitive load in acquisition of programming abilities - So Asai; Dinh Thi Dong Phuong; Fumiko Harada; Hiromitsu Shimakawa (International Journal of Electrical and Computer Engineering (IJECE), 2019)
In this paper, we propose a method to predict cognitive load and its factors affecting the learning efficiency in programming learning from the learning behavior of learners. Generally, since the concepts of programming are difficult for learners, some of them suffer inappropriate cognitive load to understand them. Although teachers must keep cognitive load of such learners appropriate, it is difficult for them to find learners who has inappropriate cognitive load from a large number of learners. To find learners with inappropriate cognitive load, we construct models with the random forest algorithm, using learning behavior collected from learners solving fill-in-the-blank tests. An experiment shows the models can detect cognitive load for IL and GL along with their factors. Teachers must address adjustment of cognitive load of learners. This result clarifies the learning factors affecting cognitive load of learners, which enables teachers to address the adjustment with small burdens.