Assurance Of Safety Of Computer-Based Testing System For Midterm Knowledge Assessment

The article actualizes the need for teaching students to work with Big Data technologies. Big Data is a promising and fundamental industry that requires a large number of qualified specialists in various fields. The aim of the work is to describe the concept of determining a set of hardware, software, algorithmic and methodological tools (taking into account the contingent of students and the capabilities of the educational institution) for building a methodology for teaching a discipline related to the study of Big Data processing methods. There are two main sectors of stakeholders who need specialists in the field of Big Data. A detailed comparative analysis of software solutions that support Big Data processing is carried out. The article describes the methodology for constructing a course for teaching students technologies for processing and analyzing Big Data. A plan for organizing a lecture course and laboratory practice with consideration of subtasks is proposed for students to perform during training. The composition and methodology of independent work of students in the discipline related to the study of Big Data, using a learning management system such as Moodle, are discussed. An example of implementing data processing by means of the RapidMiner Studio package using a multi-layer neural network training algorithm using the error back propagation method is presented.

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Approaches to the choice of tools for constructing a methodology for learning to work with Big Data

Stepanov

Plotnikov

Vasilyeva

2021

Informatika i obrazovanie

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Assurance Of Safety Of Computer-Based Testing System For Midterm Knowledge Assessment

Abstract: This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 Unported License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Approaches to the choice of tools for constructing a methodology for learning to work with Big Data

Approaches to the choice of tools for constructing a methodology for learning to work with Big Data

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