Detecting Concentration of Students Using Kinect in E-learning

Igarashi, Takashi; Kotake, Shuhei; Takahashi, Keníchi; Inaba, Inaba; Inaba, Michimasa

doi:10.12792/icisip2017.082

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Results of the questionnaire on the feeling of use of the system after the experiments for groups A and C are shown in Table 2, where the group of students with warning in the experiment of the previous study (13) is referred to as group C. Firstly, looking at the average of item 1 in Table 2, we see that students in this experiments are not so good at English as students in the students. The authors believe that this affects the scores of the questionnaire.…”

Section: Questionnairementioning

confidence: 93%

“…When the final degree of concentration falls below the predetermined threshold value, a message prompting concentration to the learner is displayed along with the beep sound. In this research, we set the threshold value to 60% according to the previous research (13) .…”

Section: Face Trackingmentioning

confidence: 99%

“…We have introduced "Face Tracking" (face recognition function), one of the functions of Kinect, to the e-learning system, in order to check whether the learner's face and eye direction are facing forward and whether the eyes are open. We have constructed a system to determine whether the learner is properly and intensively studying using those information, and warn the learner to concentrate if the system determines that the learner does not concentrate (13) . In this research, we conducted experiments to improve the accuracy of detection by changing the threshold to determine whether a student is concentrated or not.…”

Section: Introductionmentioning

confidence: 99%

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A Method to Detect Concentration of Students for E-learning Using Kinect

Kamiya

Igarashi

Takahashi

et al. 2019

Proceedings of the 7th International Conference on Intelligent Systems and Image Processing 2019

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These days, e-learning systems have become widespread.Many educational organizations such as universities and enterprises use e-learning systems for education of students and new employees. In universities, some lectures use e-learning systems. In that case, it is often the case that some students do not concentrate properly on learning, and teachers may not be able to grasp situations whether students intensively studied or not. As one of solutions to the problem and to help teachers grasp students' behavior, we constructed an e-learning system that judges whether or not students study appropriately with concentration using Kinect and warns them to concentrate when they do not. In the system, "Face Tracking" (face recognition function), one of the functions of Kinect, is employed for the e-learning system to check whether the learner's face and eye direction are looking forward and whether the eyes are open. In this research, experiments are conducted to improve the accuracy of detection by changing the threshold to determine whether a student is concentrated or not. Additionally, whether the mouse does not move for a predetermined time or more is checked. In order to show the usefulness of the system through experiments, the accuracy of detecting concentration is examined, and the growth rate of the grading points with warning is compared with that without warning. Furthermore, to see the feeling of students, the questionnaire about the system after the experiments is conducted.

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Section: Questionnairementioning

confidence: 93%

Section: Face Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method to Detect Concentration of Students for E-learning Using Kinect

Kamiya

Igarashi

Takahashi

et al. 2019

Proceedings of the 7th International Conference on Intelligent Systems and Image Processing 2019

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“…In order to challenge the problem, various systems have been proposed, e.g., a system with a function to praise and scold students with images according to their learning performance, a system that measures student concentration and learning states during learning using Kinect, a system introducing game-based learning, intelligent collaborative e-learning systems, an e-learning platform, and portfolio assessment in elearning environment [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Effects of Resting Actions Using Smart Toys During Break Times on Concentration in E-learning

Igarashi¹,

Takahashi²,

Kajiyama³

2020

Adv. sci. technol. eng. syst. j.

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This study conducted experiments to investigate the effects of resting actions of e-learners during break times on keeping and improving the concentrations of learners in e-learning.Two smart toys (a dog-type robot "aibo" and a toy drone) were used for the resting actions.Two types of experiments were conducted to examine the effects: one is a work type experiment (simple mathematical calculation) and the other is a memorizing type experiment (learning of English words). In those experiments, the learners played with one of the smart toys during the resting times between the learning sessions and performed certain actions accordingly. The experimental results were combined with the previous study using a humanoid robot "RoBoHoN." A questionnaire was also employed to investigate the feeling of the learners. The experiments showed that the "playing with aibo" action refreshed the learners effectively in the mathematical experiment, while the "playing with RoBoHoN" action was effective in the learning experiment.

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“…Their willingness to learn gradually declines and they cannot maintain the concentration. In order to solve these problems and promote student concentration in e-learning, we constructed a system with a function to praise and scold students, and a system using Kinect to measure the concentration of users and learning states while they learn [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Effects of Various Behaviours in the Break Times between Learning

Igarashi

Takahashi

2020

Information

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E-learning provides users with the experience of learning at their own pace, without being restricted by time and place. Learners tend to get tired gradually during learning, and therefore, it is important to have breaks between studies to maintain learning performance. Furthermore, the break time has to be effective so that learners can refresh. This study compares three types of breaks: quiet, active, and communication breaks. In the experiment, university students in their twenties are asked to solve as many mathematical calculation problems as possible for 15 min. The experiment comprises three learning sessions and two three-minute breaks between them. Learners are assigned one of the three types of breaks to refresh themselves. The effect of those resting behaviours on learning is examined, and the results are evaluated with regard to the accuracy of their calculations and their answering time. The effects of these behaviours will be discussed based on the experimental results and the questionnaire.

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Detecting Concentration of Students Using Kinect in E-learning

Cited by 5 publications

References 4 publications

A Method to Detect Concentration of Students for E-learning Using Kinect

A Method to Detect Concentration of Students for E-learning Using Kinect

Effects of Resting Actions Using Smart Toys During Break Times on Concentration in E-learning

Effects of Various Behaviours in the Break Times between Learning

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