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.

中文翻译：

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基于Moo型动力学的生物识别技术的效率。

大规模在线公开课程（MOOC）中需要解决的问题之一是缺乏对学生的识别和认证。通过几种导航，生理和行为方法进行了不同的研究，以实现不同的识别等级。为了简化要约人的数字平台来解决身份识别问题，已在多个MOOC中实现了通过击键模式（上下）进行生物特征认证。这项研究的目的是分析MOOC内其他人口统计学，社会学和行为变量的击键工具的独立性，建立一个初始模式，并在整个课程中进行两次认证测量。结果表明，按键与分析的变量无关，