An important research problem for Educational Data Mining is to expedite the
cycle of data leading to the analysis of student learning processes and the
improvement of support for those processes. For this goal in the context of
social interaction in learning, we propose a three-part pipeline that includes
data infrastructure, learning process analysis with behavior modeling, and
intervention for support. We also describe an application of the pipeline to
data from a social learning platform to investigate appropriate goal-setting
behavior as a qualification of role models. Students following appropriate goal
setters persisted longer in the course, showed increased engagement in hands-on
course activities, and were more likely to review previously covered materials
as they continued through the course. To foster this beneficial social
interaction among students, we propose a social recommender system and show
potential for assisting students in interacting with qualified goal setters as
role models. We discuss how this generalizable pipeline can be adapted for
other support needs in online learning settings.Comment: in The 9th International Conference on Educational Data Mining, 201
