Educational Data Mining to Identify the Patterns of Use made by the University Professors of the Moodle Platform

Due to the events caused by the COVID-19 pandemic and social distancing measures, learning management systems have gained importance, preserving quality standards, they can be used to implement remote education or as support for face-to-face education. Consequently, it is important to know how teachers and students use them. In this work, clustering techniques are used to analyze the use, made by university professors, of the resources and activities of the Moodle platform. The CRISP-DM methodology was applied to implement a data mining process, based on the Simple K-Means algorithm; to identify associated groups of teachers it was necessary to categorize the data obtained from the platform. The Apriori algorithm was applied to identify associations in the use of resources and activities. Performance scales were established in the use of Moodle functionalities, the results show the use made by teachers was very low. Rules were generated to identify the associations between activities and resources. As a result the functionalities that need to be enhanced in the teacher training processes were identified. Having identified the patterns of use of the Moodle platform, it is concluded that it was necessary to use a Likert scale to transform the frequency of use of activities and resources and identify the rules of association that establish profiles of teachers and tools that should be promoted in future training actions.

@article{Calderon-Valenzuela2022,
  title     = {Educational Data Mining to Identify the Patterns of Use made by the University Professors of the Moodle Platform},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {1},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Johan Calderon-Valenzuela and Keisi Payihuanca-Mamani and Norka Bedregal-Alpaca},
  doi       = {10.14569/IJACSA.2022.0130140},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130140}
}