Discovery of College Students in Financial Hardship

College students with financial difficulties refer to those whose families can hardly afford their high tuition in universities, and should be supported by modern funding system. Indeed, students' economic plight negatively impact their mental health, academic performance, as well as their personal and social life. While funding students in financial hardship is widely accepted, there is limited understanding and research on effectively identification of the qualifying students. Traditional approaches relying on advisers' personal assessments are inefficient, and such subjective judgements may not reflect the truth. To this end, in this paper, we explore the data mining techniques for identifying students who are qualified for financial support. Specifically, we investigate students' complex behaviors on campus from multiple perspectives, and develop a learning framework, named Dis-HARD, by jointly incorporating the heterogeneous features to predict the portfolio of stipends a given student should be awarded. Our framework formalizes the above problem as a multi-label learning problem. Along this line, we first extract discriminative features from three perspectives: (i) smartcard usage behavior, (ii) internet usage behavior and (iii) trajectory on campus. Then, we develop a linear loss function with regularization to solve this multi-label classification problem. In addition, to effectively exploit the students' similarity and label dependency, we incorporate the graph Laplacian and composite l2,1-norm into the regularization of our model, and develop are-weighted algorithm to achieve effective optimization. Finally, experiments on real-world data demonstrate that our method consistently provides better performance compared to the existing state-of-the-art methods.