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.
Hadioui, A., El Faddouli, N. E., Touimi, Y. B., & Mohammed, S. B. (2017). Machine learning based on big data extraction of massive educational knowledge. International Journal of Emerging Technologies in Learning, 12(11), 151–167. https://doi.org/10.3991/ijet.v12i11.7460