e-Learner认知效率建模及自适应调整方法研究

Aiming at the problem of lack of 'self-supervision service' in current e-Learning systems, firstly, based on thinking map, this project is to build an e-learner cognition performance model combined with the non-intelligence factors, such as e-learner's personality, interest and emotion. This model solves the problem of the quantifiable and computable representation of cognition components and their relationships among them. Secondly, to rocognize e-learner's cognition states from the heterogeneous multi-sources in multimodal interaction, an two-layer information fusion framework is proposed.At the same time, an utility based information fusion algorithm is introduced into the second layer fusion center, which considers the robustness, accuracy, performance and cost of each recognition method in various interaction mode, an utility based multi-objective function is constructed, and an optimization method is used to acquire the optimal parameters of fusion algorithm.Thirdly, learners behavior instruction following with the law of metacognition and learning content recommendation based on Wundt curve are to be exploited further so as to achieve the goal of adpative recognition regulation of e-learners. Finally, to verify and validate the proposed methods, these methods will be carried out in internet education School of our university. Obviously, this work will improve the learning efficiency and quality, and encourage to a learning ecological environment with characteristics of virtuous cycle and incentive interactions among e-learner's interest, cognition, behavior and emotion. Through this research, strive to publish 8-10 articles, four of which is in high level inernational journals.

针对当前e-Learning系统中"自我监督服务缺失"问题，本项目首先基于思维地图，构建融合个性、兴趣、情感等非智力因素的认知效率模型，解决认知要素及其之间关联关系的量化与可计算；其次，针对多模式交互中的"多源异质信息"，提出一种两级混合信息融合框架，分级地从低级向高级逐步识别认知状态的要素。同时，在二级融合中心采用基于效用函数的信息融合方法，即构造基于识别方法鲁棒性、性能与代价等因素的效用函数，形成多目标优化函数，利用最优化方法求取优化融合参数集合；再次，提出一种符合元认知规律的学习行为指导方法；拟研究基于Wundt函数的信息效用度量方法，提出融合个性、兴趣和情感的学习内容推荐方法，以实现认知自适应调整；然后，在本校的网络教育学院测试和验证所提方法。本研究将有助于形成学习者兴趣-认知-行为-情感之间互动激励与良性循环的学习生态环境。在认知效率建模、状态识别、自适应调整方面形成高质量成果。

针对当前e-learning 系统中普遍存在的"自我监督服务缺乏"问题，本项目提出多源信息融合框架的e-learner认知状态识别与学习内容推荐的解决思路。主要贡献点如下：.在e-learner认知状态识别方面，首先，基于思维地图，构建融合个性、兴趣、情感等非智力因素的认知效率模型，其中，认知状态包括认知策略、元认知策略、 情感、学习效果，这是认知状态识别目标；其次，提出了一个多源异质信息融合的e-learner学习状态识别框架。第三，构建了两类多模态150学习者数据集约20GB。实验证明所提方法的有效性和正确性，回归模型R2指标超0.9。.在非平衡交互文本情感识别方面，针对标签类分布严重非平衡且源数据集和目标数据集特征空间不同质问题，提出领域实例迁移的交互文本非情感识别方法。该研究方法有效缓解了交互文本的非平衡问题，使支持向量机、随机森林、朴素贝叶斯、随机委员会4个经典分类算法的加权平均的接收者运行特征曲线指标提升了11.3%。.在学习行为指导与学习内容推荐方面，①分析归纳出初次学习、平时复习、考前学习和考前复习4种学习场景，并提出了一个学习路径的多约束模型及推荐算法。②从资源蕴含的知识关联及用户的隐式反馈角度，提出一种基于图谱关联的跨课程视频子图推荐算法。该算法从准确率、覆盖率、召回率等指标上均优于传统的基于协同过滤的推荐算法，且在知识关联性指标上提升了15.2%。③针对图推荐中学习时序信息丢失的问题，提出了基于学习生成网络的群组学习资源推荐算法。实验结果表明：当取10个月的数据量时，在准确度、召回率、归一化折损累积增益和平均正确率均值指标上，分别比协同过滤算法高出7.5%，24.4%，13.9%，22.1%。.在示范与验证方面，研制出具有学习自调整功能的 e-learning 原型系统，并在西安交通大学网络教育学院应用。相关成果也被应用到学信网、教学质量实时监测大数据平台、国家志愿者服务关键技术与服务平台，覆盖百万以上用户。发表论文期刊15篇，其中SCI源刊的国际期刊8篇，申请发明专利8项，授权7项。获多项国家省部级一等奖和二等奖。