基于二分图的个性化学习任务分配

Personalized Learning Task Assignment Based on Bipartite Graph

学习是一种复杂的事件。个体的学习效果受多方面因素的影响,且不同个体有不同的学习习惯,学生通常难以根据自身学习特点合理规划学习时间表。虽然目前有关任务管理方面的研究提出了一些具有通用性的理论管理策略,但其忽略了个体间的差异性;另外,现有研究不能提供一种计算方法来形成具体的任务管理方案。针对上述问题,文中通过数据分析找出学习效率与时间因素的关联性,从而理解学生的学习特征,量化出个性化的学习效率;使用二分图的方法构建学习任务分配场景,根据不同的学习目标设计自适应效用函数,并基于此提出了一种基于迁移学习的动态分配算法TLTA,用于为学生制定合理的任务分配方案。在真实的学生数据集上进行了大量实验,验证了所提方案的有效性及适用性。

“Learning”is a complex event.Individual’s learning effect is affected by many factors.Moreover,different individuals have different learning habits.Therefore,it is challenging for students to plan their learning schedule reasonably according to their own characteristics.Although some general theoretical strategies for task management have been proposed,the differences among individuals are usually neglected.Furthermore,existing research cannot provide a calculation method to form a specific task mana-gement schedule.To this end,this paper tries to explore students’learning characteristics by deeply studying the relation between learning efficiency and time factor through data analysis.Based on this,it quantifies personalized learning efficiency.Furthermore,it exploits the bipartite graph method to construct the learning task assignment scenario,and designs adaptive utility function according to different learning goals.Then,a dynamic allocation algorithm TLTA based on transfer learning is proposed to formulate a reasonable schedule for students.Finally,a large number of experiments are carried out on real learning datasets,and the results validate the effectiveness and applicability of the proposed work.