  Understanding which student support strategies mitigate dropout and improve
student retention is an important part of modern higher educational research.
One of the largest challenges institutions of higher learning currently face is
the scalability of student support. Part of this is due to the shortage of
staff addressing the needs of students, and the subsequent referral pathways
associated to provide timeous student support strategies. This is further
complicated by the difficulty of these referrals, especially as students are
often faced with a combination of administrative, academic, social, and
socio-economic challenges. A possible solution to this problem can be a
combination of student outcome predictions and applying algorithmic recommender
systems within the context of higher education. While much effort and detail
has gone into the expansion of explaining algorithmic decision making in this
context, there is still a need to develop data collection strategies Therefore,
the purpose of this paper is to outline a data collection framework specific to
recommender systems within this context in order to reduce collection biases,
understand student characteristics, and find an ideal way to infer optimal
influences on the student journey. If confirmation biases, challenges in data
sparsity and the type of information to collect from students are not
addressed, it will have detrimental effects on attempts to assess and evaluate
the effects of these systems within higher education.
