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Fumiko Harada
Connect Dot Ltd
Computer Science & IT Electrical & Electronics Engineering

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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) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (702.962 KB) | DOI: 10.11591/ijece.v9i4.pp3262-3271

Abstract

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.
Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor Atsushi Hagihara; Fumiko Harada; Hiromitsu Shimakawa
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1208-1222

Abstract

We propose an IoT support system for estimating the posture of the care recipient on the bed from the body pressure of the care recipient measured by a sheet-type body pressure sensor, and detecting the posture related to leaving the bed in real time. In addition, we propose a method that predicts getting out of the bed before the care recipient takes a posture related to getting out of the bed by considering the state transition. Intervention experiment showed that using body pressure features as an explanatory variable and applying machine learning, 16 types of postures on the bed of care recipients with an F value of 0.7 or more could be identified. From the experiment without intervention, by applying the hidden Markov model, we calculated the transition probability to each hidden state when the care recipient getting out of the bed and the transition probability to each hidden state when the care recipient not getting out of the bed. As a result, there was a difference of about 0.1 in the transition probability of the state related to raising upper body.
Co-Authors Atsushi Hagihara Dinh Thi Dong Phuong Hiromitsu Shimakawa So Asai