In an e-learning environment in which a teacher cannot interact directly with a student, it can be difficult to ascertain a student’s difficulty with a subject. In this study, machine learning was used to estimate the level of difficulty of problems experienced by a student to ensure that problems of appropriate difficulty are provided. JINS MEME smart eyewear was used to measure the head movements of students and their results were used to estimate the subjective difficulty that they experienced. Our experimental tests demonstrate the F1-scores of machine learning for 10 users who were given calculation, kanji (Chinese characters), and programming problems. The feature importance scores of the random forest (RF) were calculated, and the dependence of F1-score on the type of user was examined. It was found that the mean of the yaw angle was the most important feature in all cases, indicating that the horizontal rotation of the head may depend on the difficulty of the problem.