Resumen

Recommender systems in educational contexts have proven effective to identify learning resources that fit the interests and needs of learners. Their usage has been of special interest in online self-learning scenarios to increase student retention and improve the learning experience. In current recommendation techniques, the quality of the recommendation is largely based on the explicit or implicit information obtained about the learners. On free massive online learning platforms, however, the information available about learners may be limited and based mostly on logs from website analytics tools such as Google Analytics. In this research, we address the challenge of recommending meaningful content with limited information from users by using rating estimation strategies from a log system.