  We develop a Multimodal Spatiotemporal Neural Fusion network for Multi-Task
Learning (MSNF-MTCL) to predict 5 important students' retention risks: future
dropout, next semester dropout, type of dropout, duration of dropout and cause
of dropout. First, we develop a general purpose multi-modal neural fusion
network model MSNF for learning students' academic information representation
by fusing spatial and temporal unstructured advising notes with spatiotemporal
structured data. MSNF combines a Bidirectional Encoder Representations from
Transformers (BERT)-based document embedding framework to represent each
advising note, Long-Short Term Memory (LSTM) network to model temporal advising
note embeddings, LSTM network to model students' temporal performance variables
and students' static demographics altogether. The final fused representation
from MSNF has been utilized on a Multi-Task Cascade Learning (MTCL) model
towards building MSNF-MTCL for predicting 5 student retention risks. We
evaluate MSNFMTCL on a large educational database consists of 36,445 college
students over 18 years period of time that provides promising performances
comparing with the nearest state-of-art models. Additionally, we test the
fairness of such model given the existence of biases.
