In this paper, we present our plan for constructing a platform for collecting, mining, and utilizing behavior data for detecting students with depression risks. Unipolar depression makes a large contribution to the burden of disease, being at the first place in middle- and high-income countries. We survey descriptors of depressions and then design a data collection platform in a classroom based on the assumption that such descriptors are also effective to students with depression risks. Visual, acoustic, and e-learning data are chosen for collection and various issues including devices, preprocessing, and consent agreements are investigated. We also show two kinds of utilization scenarios of the collected data and introduce several techniques and methods we developed for feature extraction and early detection.