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Adaptive learning aims to stimulate and meet the needs of individuallearners, which requires sophisticated system-level coordination of diversetasks, including modeling learning resources, estimating student states, andmaking personalized recommendations. Existing deep learning methods haveachieved great success over statistical models; however, they still lackgeneralization for diverse tasks and suffer from insufficient capacity sincethey are composed of highly-coupled task-specific architectures and rely onsmall-scale, coarse-grained recommendation scenarios. To realize the idea ofgeneral adaptive systems proposed in pedagogical theory, with the emergingpre-training techniques in NLP, we try to conduct a practical exploration onapplying pre-training to adaptive learning, to propose a unified frameworkbased on data observation and learning style analysis, properly leveragingheterogeneous learning elements. Through a series of downstream tasks ofLearning Recommendation, Learning Resource Evaluation, Knowledge Tracing, andDropout Prediction, we find that course structures, text, and knowledge arehelpful for modeling and inherently coherent to student non-sequential learningbehaviors and that indirectly relevant information included in the pre-trainingfoundation can be shared across downstream tasks to facilitate effectiveness.We finally build a simplified systematic application of adaptive learning andreflect on the insights brought back to pedagogy. The source code and datasetwill be released.

Towards a General Pre-training Framework for Adaptive Learning in MOOCs