Estimation of student understandings from pulse wave changes caused byload in preparatory

• Autorzy: Hagihara A. , Shimakawa H. , Kajiwara Y.

Identyfikatory

DOI

10.15439/2018F124

• Konferencja: Federated Conference on Computer Science and Information Systems (09-12.09.2018 ; Poznań, Poland)
• Języki publikacji: EN

Abstrakty

EN

We propose a method to estimate whether student hold knowledge on the contents and tasks of the class, using cognitive load and mental load. We estimate the cognitive load of the student using a pulse wave and the pulse change.The mental load can be estimated from the activity of the autonomic nerve. In order to clarify student knowledge on course contents, we estimate student schemata from the load on the student.We conducted an experiment, preparing 2 kinds of tasks; S1 task can be answered with student schemata, while S2 task cannot be answered with student schema. Through the experiment using the proposed method, we classified tasks given to students into S1 tasks and S2 tasks.It was possible to estimate the student schemata from the standard deviation of the heart rate.This method makes it possible to figure out student understanding earlier than traditional knowledge measurement method.

Słowa kluczowe

EN

cognitive load; flipped classroom; machine learning

PL

ładunek kognitywny; odwrócona klasa; nauczanie maszynowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 16
• Strony: 3--8
• Opis fizyczny: Bibliogr. 18 poz., tab., rys., wz.

Twórcy

autor

Hagihara A.

• Ritsumeikan University, Graduate School of Information Science and Engineering, Kusatsu-city, 525-8577 Shiga, Japan

autor

Shimakawa H.

• Ritsumeikan University, College of Information Science and Engineering

autor

Kajiwara Y.

• Komatsu University, Faculty of Production Systems Engineering and Sciences

Bibliografia

• [1] OnlineUniversities.com : "12 Inspiring Schools Using Khan Academy", http://www.onlineuniversities.com/blog/2012/04/12-inspiring-schoolsusing-khan-academy.
• [2] The Chronicle of Higher Education : "California State U. System Will Expand MOOC Experiment", https://www.chronicle.com/blogs/wiredcampus/california-state-usystem-will-expand-mooc-experiment.
• [3] Katsusuke Shigeta. : "Flipped Classroom : Educational reform utilizing information technology.", j-stage, Vol.56, No.10, pp.677-684, (2014).
• [4] Sweller, John : "Cognitive load theory, learning difficulty, and instructional design.", Learning and instruction, Vol.4, No.4, pp.295-312, (1994).
• [5] Schnotz, Wolfgang, and Christian KÃijrschner : "A reconsideration of cognitive load theory.", Educational psychology review, Vol.19, No.4, pp.469-508, (2007).
• [6] Sweller, John, Jeroen JG Van Merrienboer, and Fred GWC Paas : "Cognitive architecture and instructional design." Educational psychology review , Vol.10, No.3 , pp.251-296, (1998).
• [7] Paas, Fred GWC, and Jeroen JG Van Merriônnboer. "Instructional control of cognitive load in the training of complex cognitive tasks."Educational psychology review Vol.6, No.4, pp.351-37, (1994).
• [8] DeLeeuw, K. E., Mayer, R. E. : A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, Vol.100, No.1, pp.223, (2008).
• [9] Youichi Mizuno, et al. : ” Experimental Investigation of Relationship between Cognitive Loads and Cognitive Processes in Learning through Problem Solving”, SIG-ALST, Vol5, No.3, pp.7-11, (2016).
• [10] Galy, Edith, Magali Cariou, and Claudine MÃl’lan. "What is the relationship between mental workload factors and cognitive load types?."International Journal of Psychophysiology, Vol.83,No.3, pp.269-275, (2012).
• [11] Ikehara, Curtis S. Martha E. Crosby : "Assessing cognitive load with physiological sensors." , System Sciences, 2005, HICSS’05, Proceedings of the 38th Annual Hawaii International Conference on. IEEE, pp.295a-295a, (2005).
• [12] Mulder, Gijsbertus, Lambertus JM Mulder : "Information processing and cardiovascular control." Psychophysiology, Vol.18, No.4, pp.392-402, (1981).
• [13] Keisuke Tsunoda et al. : "Estimating Cognitive Performance Change using Heart Rate Variability". Journal of Information Processing Society of Japan, Vol.57, No.8, pp.1835-1844, (2016).
• [14] Kahneman, Daniel. : ” Thinking, fast and slow.” Macmillan, (2011).
• [15] Breiman, Leo : "Random forests.", Machine learning, Vol.45, No.1, pp.5-32, (2001).
• [16] Gerjets, Peter, Katharina Scheiter, and Richard Catrambone. "Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures." Instructional Science Vol.32,No.1-2, pp.33-58, (2004).
• [17] Pollock, Edwina, Paul Chandler, and John Sweller. "Assimilating complex information." Learning and instruction, Vol.12, No.1,pp.61-86, (2002).
• [18] Likert, Rensis. "A technique for the measurement of attitudes." Archives of psychology, (1932).

Uwagi

1. Track: Preface

2. Technical Session: 13th International Symposium Advances in Artificial Intelligence and Applications