EFFICIENCY OF BIOMETRIC RECOGNITION TECHNOLOGY BASED ON TYPING DYNAMICS IN MOOC
| Autor: | Manuel Medina-Labrador, Lorena Yadira Alemán de la Garza, Marcela Georgina Gómez-Zermeño |
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| Rok vydání: | 2020 |
| Předmět: |
biometrics
lcsh:LC8-6691 Authentication lcsh:Special aspects of education Biometrics Computer science business.industry Behavioral methods Biometrics identification MOOC pulsations Keystroke logging Machine learning computer.software_genre Education Parameter identification problem Identification (information) Social Dynamics (music) identification mooc pulsations Typing Artificial intelligence business Sosyal computer |
| Zdroj: | The Turkish Online Journal of Distance Education, Vol 21, Iss Special, Pp 79-87 (2020) Volume: 21, Issue: Special Issue-IODL 79-87 Turkish Online Journal of Distance Education |
| ISSN: | 1302-6488 |
| DOI: | 10.17718/tojde.770922 |
| Popis: | One of the problems that require a solution in Massive Open Online Courses (MOOC) is the lack of identification and authentication of the students. Different investigations have been carried out through several navigation, physiological and behavioral methods, achieving different recognition scales. Biometric authentication by keystroke patterns (Ups&Downs) has been implemented in several MOOCs for the ease of the digital platforms of the offeror to solve the identification problem. The objective of this research is to analyze the independence of the keystroke tool of the other demographic, sociographic and behavioral variables within a MOOC, establishing an initial pattern, and two authentication measurements throughout the course. The results show that the keystroke is independent of the analyzed variables, and it is reliable to identify the students in qualitative tests with extension answers. |
| Databáze: | OpenAIRE |
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