ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Using Machine Learning to Estimate Difficulty Levels of Problems

Makoto Koshino and Takuya Koizumi

(Received January 22, 2020; Accepted May 12, 2020)

Keywords: subjective difficulty, wearable device, machine learning, head movement

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 thus estimate the subjective difficulty that they experienced. Our experimental tests demonstrate the accuracy of the machine-learning-based estimation for 10 users who were given calculation, kanji (Chinese characters), and programming problems. The importance of key features was estimated using the random forest algorithm, and the dependence of estimation accuracy 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.

Corresponding author: Makoto Koshino




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