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Understanding which student support strategies mitigate dropout and improvestudent retention is an important part of modern higher educational research.One of the largest challenges institutions of higher learning currently face isthe scalability of student support. Part of this is due to the shortage ofstaff addressing the needs of students, and the subsequent referral pathwaysassociated to provide timeous student support strategies. This is furthercomplicated by the difficulty of these referrals, especially as students areoften faced with a combination of administrative, academic, social, andsocio-economic challenges. A possible solution to this problem can be acombination of student outcome predictions and applying algorithmic recommendersystems within the context of higher education. While much effort and detailhas gone into the expansion of explaining algorithmic decision making in thiscontext, there is still a need to develop data collection strategies Therefore,the purpose of this paper is to outline a data collection framework specific torecommender systems within this context in order to reduce collection biases,understand student characteristics, and find an ideal way to infer optimalinfluences on the student journey. If confirmation biases, challenges in datasparsity and the type of information to collect from students are notaddressed, it will have detrimental effects on attempts to assess and evaluatethe effects of these systems within higher education.

A Framework for Undergraduate Data Collection Strategies for Student Support Recommendation Systems in Higher Education