摘要：This paper aims to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students’ and teachers’ opinions. Recommender systems, assuming a ‘technology-driven’ approach, have been designed with the primary goal of predicting user interests based on the implicit analysis of their actions and interactions. The results of this study reveal that although there is some confusion and unawareness about the recommender system, the participants consider that SAPO Campus recommendations are useful and they often find interesting people and content through the results provided by the system. The results also reveal that there is a negative correlation between finding and following people through the platform recommendations and the level of education, that is, the higher the level of education, the lower is the frequency regarding finding and following people suggested by the platform recommendation system. Keywords: education; SAPO Campus; recommendations; interaction (Published: 29 August 2014) Citation: Research in Learning Technology 2014, 22 : 22921 - http://dx.doi.org/10.3402/rlt.v22.22921
其他摘要：This paper aims to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students’ and teachers’ opinions. Recommender systems, assuming a ‘technology-driven’ approach, have been designed with the primary goal of predicting user interests based on the implicit analysis of their actions and interactions. The results of this study reveal that although there is some confusion and unawareness about the recommender system, the participants consider that SAPO Campus recommendations are useful and they often find interesting people and content through the results provided by the system. The results also reveal that there is a negative correlation between finding and following people through the platform recommendations and the level of education, that is, the higher the level of education, the lower is the frequency regarding finding and following people suggested by the platform recommendation system.
