教育系近年来加大力度支持在读研究生和本科生参加高水平学术会议，极大的增强了学生的学术视野及学术水平。

近日，我系2014级教育信息技术专业研究生郑雅倩同学的论文被国际期刊Knowledge-Based Systems(SCI检索，一区，影响因子: 5.101)录用(2017年1月31日投稿，2017年10月18日接收)。Knowledge-Based Systems是人工智能领域重要刊物之一，这也是我系机器学习方面的研究成果首次在人工智能领域权威期刊上发表。

Yaqian Zheng, Chunrong Li, Shiyu Liu, Weigang Lu*. An improved genetic approach for composing optimal collaborative learning groups. Knowledge-Based Systems. 2018, 139: 214-225.

The current work focuses on a key issue in the collaborative learning context: the method of learner group formation. The main goal is to obtain optimal learning groups that meet various grouping requirements for different educational contexts. To achieve this goal, all requirements of the learner group formation problem are formulated into an integrated mathematical model and an improved genetic algorithm is proposed to solve the model. To analyse the performance of the proposed approach from a computational perspective, a series of computational experiments are conducted based on eight simulation datasets with different levels of complexity. The simulation results indicate that the proposed method is effective and stable for solving the learner group formation problem. An empirical study is also carried out to validate the proposed approach from a pedagogical view by comparing it with two traditional group formation strategies. The results show that groups formed through the proposed method produce better outcomes than others in terms of group grades, individual grades and student satisfaction.