Using Big Data Technology For Prediction Of Quiz Difficulty Level In E-learning Systems

Processing massive data amounts and Big Data became nowadays one of the most significant problems in computer science. The difficulties with education on this field arise, the appropriate teaching methods and tools are needed. The processing of vast amounts of data arriving quickly requires the choice and arrangement of extended hardware platforms.In the paper we will show an approach for teaching students in Big Data and also the choice and arrangement of an appropriate programming platform for Big Data laboratories. Usage of an e-learning platform Moodle, a dedicated platform for teaching, could allow the teaching staff and students an improved contact with by enhancing mutually communication possibilities. We will show the preparation of Hadoop platform tools and Big Data cluster based on Cloudera and Ambari. The both solutions together could enable to cope with the problems in education of students in the field of Big Data.

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Integrating Big Data technologies in a dynamic environment EIAH dedicated to e-learning systems based on cloud infrastructure

Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems - ICCWCS'17 ◽

10.1145/3167486.3167506 ◽

2017 ◽

Author(s):

K. Dahdouh ◽

Ahmed Dakkak ◽

Lahcen Oughdir

Keyword(s):

Big Data ◽

Dynamic Environment ◽

Learning Systems ◽

Cloud Infrastructure ◽

Big Data Technologies ◽

E Learning

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Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.07 ◽

2018 ◽

Vol 12 ◽

pp. 85-98

Author(s):

Bojan Kostadinov ◽

Mile Jovanov ◽

Emil STANKOV

Keyword(s):

Machine Learning ◽

Data Collection ◽

Educational Policy ◽

Learning Systems ◽

Data Sources ◽

Or Education ◽

Student Activity ◽

The World ◽

E Learning ◽

Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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A New Rockburst Experiment Data Compression Storage Algorithm based on Big Data Technology

Intelligent Automation & Soft Computing ◽

10.31209/2019.100000111 ◽

2019 ◽

pp. 561-573

Author(s):

Yu Zhang ◽

Yan-Ge Wang ◽

Yan-Ping Bai ◽

Yong-Zhen Li ◽

Zhao-Yong Lv ◽

...

Keyword(s):

Big Data ◽

Data Compression ◽

Experiment Data ◽

Big Data Technology

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A code of practice for e-support in e-learning systems

10.3403/02878836u ◽

2015 ◽

Keyword(s):

Learning Systems ◽

Code Of Practice ◽

E Learning

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Validity of the “Big Data Tendency in Education” Scale as a Tool Helping to Reach Inclusive Social Development

Sustainability ◽

10.3390/su12135470 ◽

2020 ◽

Vol 12 (13) ◽

pp. 5470 ◽

Cited By ~ 2

Author(s):

Antonio Matas-Terrón ◽

Juan José Leiva-Olivencia ◽

Pablo Daniel Franco-Caballero ◽

Francisco José García-Aguilera

Keyword(s):

Big Data ◽

Inclusive Education ◽

Sustainable Development Goals ◽

Sustainable Society ◽

Education Students ◽

The Sustainable Development ◽

Research Goal ◽

Development Goals ◽

Nonprobability Sample ◽

Big Data Technology

Big Data technology can be a great resource for achieving the Sustainable Development Goals in a fair and inclusive manner; however, only recently have we begun to analyse its impact on education. This research goal was to analyse the psychometric characteristics of a scale to assess opinions that educators in training have about Big Data besides their related emotions. This is important, as it will be the educators of the future who will have to manage with Big Data at school. A nonprobability sample of 337 education students from Peru and Spain was counted. Internal consistency, as well as validity, were analysed through exploratory and confirmatory factorial analysis. The results show good psychometric values, highlighting as relevant a latent structure of six factors that includes emotional and cognitive dimensions. As a result, the profile defining the participants in relation to Big Data was identified. Finally, the implications of the Big Data for Inclusive Education in a sustainable society are discussed.

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