Special aspects of using Big Data in the learning process

ABSTRACT

The paper examines the special aspects of using Big Data technology in education. The population was made up of 356 third-year university students. To study Big Data technology, a questionnaire was used where respondents rated: cloud technology; apps; Massive Open Online Courses (MOOCs) and digital learning platforms. The study suggested that the education sector is ambitiously applying Big Data technology, both online and offline. All surveyed respondents use apps in Big Data learning and analysis: 73.03% use Moodle, 67.13% use Zoom, 65.17% use Quizlet, 50.84% use Skype, and 35.11% use Slack. MOOCs in education are used by 75% of respondents. Digital learning platforms are used by all respondents. All students use cloud technology. When dealing with Big Data technologies, students preferred apps (8.9 ± 1.33) instead of the cloud (6.9 ± 0.11). Students believe that the important factors for using Big Data in the learning process include: quality of information (85.96%); interest (77.81%); instructor’s support (66.85%). The research findings make it possible to integrate Big Data technology into the learning process, thus improving learning outcomes and providing greater speed in processing reliable and meaningful data.

KEYWORDS:

• Apps
• Big Data
• cloud technology
• digital learning platforms
• education/learning
• MOOCs

Acknowledgments

The authors thank the Russian Foundation for Basic Research for the financial support of the grant project № 19-29-14016 Methodology for the analysis of bulk data in education and its integration into training programs for teachers and heads of educational institutions in the logic “Pedagogy based on data”, “Management of education based on data”. The article was prepared as part of the research work of the state task of the RANEPA under the President of the Russian Federation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Data will be available on request.

Additional information

Funding

This work was supported by the Russian Foundation for Basic Research (RFBR): [Grant Number 19-29-14016].