Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

Authors

  • Abdelladim Hadioui RIME TEAM-Networking, Modeling and e-Learning- LRIE Laboratory- Research in Computer Science and Education Laboratory
  • Nour-eddine El Faddouli RIME TEAM-Networking, Modeling and e-Learning- LRIE Laboratory- Research in Computer Science and Education Laboratory
  • Yassine Benjelloun Touimi RIME TEAM-Networking, Modeling and e-Learning- LRIE Laboratory- Research in Computer Science and Education Laboratory
  • Samir Bennani RIME TEAM-Networking, Modeling and e-Learning- LRIE Laboratory- Research in Computer Science and Education Laboratory

DOI:

https://doi.org/10.3991/ijet.v12i11.7460

Keywords:

learning analytics, operational data, machine learning, big data analysis, knowledge management

Abstract


A learning environment generates massive knowledge by means of the services provided in MOOCs. Such knowledge is produced via learning actor interactions. This result is a motivation for researchers to put forward solutions for big data usage, depending on learning analytics techniques as well as the big data techniques relating to the educational field. In this context, the present article unfolds a uniform model to facilitate the exploitation of the experiences produced by the interactions of the pedagogical actors. The aim of proposing the said model is to make a unified analysis of the massive data generated by learning actors. This model suggests making an initial pre-processing of the massive data produced in an e-learning system, and it’s subsequently intends to produce machine learning, defined by rules of measures of actors knowledge relevance. All the processing stages of this model will be introduced in an algorithm that results in the production of learning actor knowledge tree.

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Published

2017-11-16

How to Cite

Hadioui, A., El Faddouli, N.- eddine, Benjelloun Touimi, Y., & Bennani, S. (2017). Machine Learning Based On Big Data Extraction of Massive Educational Knowledge. International Journal of Emerging Technologies in Learning (iJET), 12(11), pp. 151–167. https://doi.org/10.3991/ijet.v12i11.7460

Issue

Vol. 12 No. 11 (2017)

Section

Papers

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