  A learning management system streamlines the management of the teaching
process in a centralized place, recording, tracking, and reporting the delivery
of educational courses and student performance. Educational knowledge discovery
from such an e-learning system plays a crucial role in rule regulation, policy
establishment, and system development. However, existing LMSs do not have
embedded mining modules to directly extract knowledge. As educational modes
become more complex, educational data mining efficiency from those
heterogeneous student learning behaviours is gradually degraded. Therefore, an
LMS incorporated with an advanced educational mining module is proposed in this
study, as a means to mine efficiently from student performance records to
provide valuable insights for educators in helping plan effective learning
pedagogies, improve curriculum design, and guarantee quality of teaching.
Through two illustrative case studies, experimental results demonstrate
increased mining efficiency of the proposed mining module without information
loss compared to classic educational mining algorithms. The mined knowledge
reveals a set of attributes that significantly impact student academic
performance, and further classification evaluation validates the identified
attributes. The design and application of such an effective LMS can enable
educators to learn from past student performance experiences, empowering them
to guide and intervene with students in time, and eventually improve their
academic success.
