  Learning-outcome prediction (LOP) is a long-standing and critical problem in
educational routes. Many studies have contributed to developing effective
models while often suffering from data shortage and low generalization to
various institutions due to the privacy-protection issue. To this end, this
study proposes a distributed grade prediction model, dubbed FecMap, by
exploiting the federated learning (FL) framework that preserves the private
data of local clients and communicates with others through a global generalized
model. FecMap considers local subspace learning (LSL), which explicitly learns
the local features against the global features, and multi-layer privacy
protection (MPP), which hierarchically protects the private features, including
model-shareable features and not-allowably shared features, to achieve
client-specific classifiers of high performance on LOP per institution. FecMap
is then achieved in an iteration manner with all datasets distributed on
clients by training a local neural network composed of a global part, a local
part, and a classification head in clients and averaging the global parts from
clients on the server. To evaluate the FecMap model, we collected three
higher-educational datasets of student academic records from engineering
majors. Experiment results manifest that FecMap benefits from the proposed LSL
and MPP and achieves steady performance on the task of LOP, compared with the
state-of-the-art models. This study makes a fresh attempt at the use of
federated learning in the learning-analytical task, potentially paving the way
to facilitating personalized education with privacy protection.
