摘要：
早期预警是在线学习中的重要主题,通过早期预警识别有不及格风险的学生可帮助教师及时开展个性化教学干预。使用深度学习模型对学生微观行为模式进行分析以提高早期预警的效果,并提出结合LSTM-autoencoder特征处理和注意力权重计算的不及格风险学生早期预警模型(LSTM-autoencoder and attention based early warning model,LAA)。该方法通过LSTM-autoencoder对学生行为时间序列数据进行特征处理,采用注意力机制计算关键预测因子。实验结果表明,LAA比基线模型取得更高的召回率,对低交互型和非持续型学生具有更好的识别效果,且能将教学干预时间提前;此外,该方法可识别影响成绩的关键周次和行为,可用于辅助教师开展在线教学指导。
作者机构：
华中师范大学国家数字化学习工程技术研究中心 武汉430079;华中师范大学物理科学与技术学院 武汉430079;华中师范大学教育大数据应用技术国家工程实验室 武汉430079;[Qiuting C.; Yunze D.] College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China;[Yanwen W.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China, College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China
期刊：
Journal of Systems Science and Systems Engineering,2021年30(4):417-432 ISSN：1004-3756
通讯作者：
Lei Niu
作者机构：
[Huang, Litian; Yu, Xinguo; Niu, Lei] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan 430000, Peoples R China.;[Zhao, Jinhua] Wuhan Univ, Sch Econ & Management, Wuhan 430000, Peoples R China.;[Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430000, Peoples R China.
通讯机构：
[Lei Niu] C;Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China
摘要：
The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.
作者机构：
[李旻; 何婷婷] National Engineering Research Center for E-Learning (Central China Normal University), Wuhan;430079, China;Computer and Information Engineering College, Henan University, Kaifeng;475001, China;[李旻] 430079, China<&wdkj&>Computer and Information Engineering College, Henan University, Kaifeng
通讯机构：
[Li, M.] N;National Engineering Research Center for E-Learning (Central China Normal University)China
通讯机构：
National Engineering Laboratory for Educational Big Data, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
作者机构：
[吕磊; 贾钊逸; 栾银森] School of Information Science and Engineering, Henan University of Technology, Zhengzhou;450001, China;[吴珂] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan;430079, China;[吕磊; 贾钊逸; 栾银森] 450001, China
