Multitasking optimization for personalized exercise group recommendation in E-learning environments

Abstract

Personalized exercise group recommendation (PEGR) is to select a set of exercises from a large exercise bank for students, which plays an important role in E-learning. Due to the complexity of real application scenarios, PEGR is usually modeled as a large-scale constrained multi-objective optimization problem and solved by multi-objective evolutionary algorithms (MOEAs). However, the “curse of dimensionality” and the complex constraints handling are the two challenges encountered when designing MOEAs to solve the PEGR problem. To this end, we propose a novel evolutionary tri-tasking algorithm named ETT-PEGR to tackle the challenges of solving the PEGR, in which two auxiliary tasks are constructed to help solve the original task through knowledge transfer. Specifically, the first concept-recommended auxiliary task is designed to recommend knowledge concepts instead of exercises to students, which can help accelerate the convergence speed of the original task since the number of concepts is much smaller than that of exercises. The second constraint-ignored auxiliary task is designed to help the solutions of the original task to cross the infeasible region. In addition, a novel knowledge transfer mechanism based on different encoding strategies is proposed for the original task and the two auxiliary tasks, which can effectively realize the knowledge transfer between them. Experimental results on four popular datasets show that ETT-PEGR outperforms the state-of-the-art algorithms for PEGR.