  Adaptive learning aims to stimulate and meet the needs of individual
learners, which requires sophisticated system-level coordination of diverse
tasks, including modeling learning resources, estimating student states, and
making personalized recommendations. Existing deep learning methods have
achieved great success over statistical models; however, they still lack
generalization for diverse tasks and suffer from insufficient capacity since
they are composed of highly-coupled task-specific architectures and rely on
small-scale, coarse-grained recommendation scenarios. To realize the idea of
general adaptive systems proposed in pedagogical theory, with the emerging
pre-training techniques in NLP, we try to conduct a practical exploration on
applying pre-training to adaptive learning, to propose a unified framework
based on data observation and learning style analysis, properly leveraging
heterogeneous learning elements. Through a series of downstream tasks of
Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and
Dropout Prediction, we find that course structures, text, and knowledge are
helpful for modeling and inherently coherent to student non-sequential learning
behaviors and that indirectly relevant information included in the pre-training
foundation can be shared across downstream tasks to facilitate effectiveness.
We finally build a simplified systematic application of adaptive learning and
reflect on the insights brought back to pedagogy. The source code and dataset
will be released.
