Effective learning hyper-heuristics for the course timetabling problem.

•Course timetabling constraints are converted into generic structures.•Two types of learning for operator selection are contrasted: online and offline.•An empirical approach for selecting the best operators from a pool is proposed.•Online learning shows competitive results, even producing new best-known solutions.•The proposed method achieves a balance between generality and effectiveness.