Abstract
Big Data is configured as a technological element and of increasing educational interest. The need to advance the quality of academic inclusion has led to an unprecedented expansion of educational processes and features. Thus, collecting massive data on educational information is part of teachers’ daily lives and educational institutions themselves. There is an intense debate about the potential of Big Data in the educational context, especially through learning analytics that favor the appropriate, responsible, and inclusive use of the data collected. The main aim of this article is to analyze user profiles and the tendency to use Big Data and see what factors influence its applicability. This study employs an incidental sample of 265 students of Educational Sciences from Andalusian Universities, (Spain), using an ad-hoc survey. A cluster analysis was conducted together with ordinal regression analysis and decision tree. The results allow us to confirm the existence of two different student profiles, in terms of their perceptions and appraisal of Big Data and its implications in education. Consequently, a higher score is found for that profile that contemplates and positively conceives Big Data in terms of learning opportunities and improvement of educational quality. The research demonstrates the need to promote Big Data training within the context of university, aiding the acquisition of digital and transversal skills.
Reference30 articles.
1. Learning optimal classification trees: Strong max-flow formulations
https://arxiv.org/abs/2002.09142
2. Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools
https://bit.ly/2VweSsw7
3. Stimulating Informal Learning Activities Through Perceptions of Performance Appraisal Quality and Human Resource Management System Strength: A Two-Wave Study
4. An Economic Appraisal of MOOC Platforms: Business Models and Impacts on Higher Education
5. VABIDAE 2019
https://vabidae.gitlab.io/vabidae/
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