Meng Huang, Shuai Liu, Yahao Zhang, Kewei Cui, Yana Wen
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引用次数: 3
摘要
人工智能技术与学校教育的融合已成为未来趋势,成为推动教育发展的重要动力。随着大数据时代的到来,虽然学生的学习状态数据之间的关系更接近于非线性关系,但结合人工智能技术的应用分析,可以发现学生的生活习惯与学习成绩密切相关。本文通过对防灾研究所信息学院近10个年级2000多名学生的生活习惯和学习状况的调查分析,采用分层聚类算法对收集到的近18万条记录进行分类,利用Echarts + iView + GIS的大数据可视化技术和JavaScript开发方法,基于地图动态展示学生的生活轨迹和学习信息,然后应用三维ArcGIS for JS API技术展示校园的网络基础设施。最后,结合人工智能Back Propagation神经网络算法,基于历史学习成果、人生轨迹、毕业生薪酬、学校基础设施等信息,建立培训模型。通过对训练结果的分析,发现学生的学习成绩与合理的实验室学习时间、合理的宿舍住宿时间、合理的体育锻炼时间和合理的社会娱乐时间有关。最后,系统可以根据建立的预测模型对学生的学习成绩进行智能预测,并给出合理的建议。该项目的实现可为高校教育工作者提供技术支持。
Research on the university intelligent learning analysis system based on AI
The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.
期刊介绍:
The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.