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Intellectualization of distance education systems
Predicting student performance and proctoring using machine learning methods and biometric technologies

NeuroNet and NeuroEducation
One of the key NTI markets is NeuroNet - a market for human-machine communications based on advanced developments in neurotechnology and increasing the productivity of human-machine systems and the performance of mental and thought processes. NeuroEducation services and products are developing in such segments as distance learning, massive open online courses (MOOCs), blended learning, as well as innovative models of additional education. Modern MOOC platforms allow the collection of complete records of students' online course activity on the Internet, which makes it possible to more closely study student behavior while mastering the course than was previously possible. Students with different motivations for learning MOOCs have very different learning behaviors, and online learning may also differ from the traditional learning process. Therefore, there is a clear need to understand user behavior in MOOCs and, more importantly, to develop effective mechanisms for increased participation in both learning and social interaction. The development of online platforms and other educational services determines the new potential of big data produced by participants in such courses and poses a new range of tasks for researchers, which are mainly focused on two aspects:
  1. Studying a student's cognitive behavior pattern by analyzing his learning process and therefore predicting the next step (for example, whether he will leave the course or what grade he will ultimately receive).
  2. Providing new features for both students and teachers.
Predicting student performance

A student’s behavior model is a set of features (patterns) that can be extracted by analyzing the student’s behavior with educational content - videos, electronic documents (text data), chats between students on the portal, etc.

We studied the key patterns of MOOC students that influence the effectiveness of the educational process, and also developed a model for predicting final results based on Bayesian probabilistic inference networks.

Key patterns include: student activity on forums (the number of questions to forum participants regarding the course content was taken into account), video viewing time (the number of stops in the video lecture was taken into account), the presence of certificates of completion of other online courses, gender, age, etc.

proctoring using biometrics
Proctoring is a procedure for monitoring and controlling a remote test

During the exam, broadcasting and recording are carried out from a webcam or desktop, and violations are automatically monitored. The student's face is photographed, and the student is authenticated. Throughout the exam, a continuous verification of the person’s identity on the computer is carried out. This process is based on methods for automatic face detection, keyboard signature registration, and the application and pre-training of Bayesian networks. After completing the exam, an assessment of the degree of confidence in the exam results is made as a percentage.

Key publications
on the topic "Intellectualization of distance learning systems"
research is in its early stages
Experience in creating an integration platform for distance education at Omsk State Technical University
distance education, software package, distance education system, integration platform
view the work on
Kogan, I.R. Experience in creating an integration platform for distance learning at Omsk State Technical University / I.R. Kogan // Young Russia: advanced technologies into industry: – 2015. – no. 2. – pp.245-247.
The article discusses the prospects for the development of distance education systems. An overview of modern applications that allow the use of individual distance educational technologies is provided. The features of creating an integration platform for distance education at Omsk State Technical University are described.
Analysis of modern distance education systems
distance learning, web-technologies, software
Kogan, I.R. Analysis of modern distance learning systems / I.R. Kogan // Visual culture: design, advertising, information technology: collection. Proceedings of the XIV International scientific-practical Conf. – Omsk: Omsk State Technical University, 2015. – P.128-131.
The article describes the main benefits of distance learning. The criteria analysis of distance education systems. The analysis of the characteristics and shortcomings of modern distance learning systems.
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