Open AccessSystem Architecture of Big Data in Massive Open Online Courses (BD-MOOCs System Architecture)
Author(s) -
Withamon Khajonmote,
Kittipong Chinsook,
Sununta Klintawon,
Chaiyan Sakulthai,
Wicha Leamsakul,
Natchanok Jansawang,
Thada Jantakoon
Publication year - 2022
Publication title -
journal of education and learning
Language(s) - English
Resource type - Journals
eISSN - 1927-5269
pISSN - 1927-5250
DOI - 10.5539/jel.v11n3p105
Subject(s) - component (thermodynamics) , big data , architecture , computer science , spark (programming language) , python (programming language) , grading (engineering) , dropout (neural networks) , data architecture , world wide web , reference architecture , operating system , engineering , software architecture , software , art , physics , civil engineering , machine learning , visual arts , thermodynamics , programming language
The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four parts: EDM, ERS, AA, and S/II. The third component was a description of three basic elements of a big data system: data capture, management, and analysis. The fourth component was that MOOCs were classified as cMOOCs, xMOOCs, quasi-MOOCs, hMOOCs, and other related. The fifth component included the steps of MOOC development: design, delivery, and assessment. Finally, MOOCs present educational data science challenges such as analyzing student interactions, estimating dropout risk, grading, and making recommendations. Overall, the BD-MOOCs system architecture design was suitable at the highest level.