基于非干预式感知的个性化学业求助资源推荐研究进展

汤路民; 余若男; 董启文; 洪道诚; 傅云斌

doi:10.3969/j.issn.1000-5641.2018.05.002

基于非干预式感知的个性化学业求助资源推荐研究进展

doi: 10.3969/j.issn.1000-5641.2018.05.002

华东师范大学数据科学与工程学院, 上海 200062

基金项目:

教育部人文社科青年基金 15YJC630032

国家自然科学基金 61332013

国家自然科学基金 61672161

详细信息

作者简介:
汤路民, 男, 硕士研究生, 研究方向为机器学习.E-mail:51174500126@stu.ecnu.edu.cn

通讯作者:
洪道诚, 男, 高级工程师, 硕士生导师, 研究方向为数据管理、教育信息化.E-mail:hongdc@dase.ecnu.edu.cn

中图分类号: G202
计量
- 文章访问数: 174
- HTML全文浏览量: 92
- PDF下载量: 224
- 被引次数: 0
出版历程
- 收稿日期: 2018-07-09
- 刊出日期: 2018-09-25

A review of non-intrusive sensing based personalized resource recommendations for help-seekers in education

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

摘要

摘要: 现代信息技术提供的强大移动终端、数据存储和计算平台，极大地促进了信息技术和教育学科的深度融合，有利地推动了"教育信息化2.0行动计划"的实施，也为研究学业求助提供了坚实的技术保障.借助多种新型的感知机理和实现技术，建立日常教学实践活动中非干预式的学业求助行为感知和分类，使实现自适应个性化的学业求助资源推荐成为可能.本文针对非干预式感知的个性化学业求助资源推荐研究状况，展开具体分析，并针对未来可能研究进行了展望：学业求助非干预式感知、学业求助多源异构数据分析、以及学业求助资源个性化推荐方法.以上研究内容充分利用和发挥了现代信息技术的优势，探索其在学业求助应用场景下切实可行的途径和方法.有利于实现对学习者学业求助需求的精准定位并提供自适应个性化的资源推荐，贯彻了我国教育信息化2.0建设中的精准教育理念，具有理论和实际的双重意义.
- 学业求助 /
- 非干预式感知 /
- 教育信息化2.0 /
- 个性化教育 /
- 精准教育
Abstract: Mobile devices, data storage, and computing platforms of modern information technology have accelerated the integration of the information technology and education disciplines, promoted the "Education Informatization 2.0" Plan, and provided a solid technical foundation for academic help-seeking. With the help of new sensing mechanisms and techniques, non-intrusive sensing of help-seeking and personalized recommendation methods can now be used for teaching practices in academia. This study reviews the research progress of non-intrusive sensing based personalized resource recommendations, offers detailed analysis, and lists possible directions for research; potential future research topics include non-intrusive sensing for help-seeking, continuous association analysis and integration for multidimensional data, and personalized resource recommendations for help-seekers. This study also makes contributions to precision education and personalized education for the China Education Informatization 2.0 Plan by providing solutions for non-intrusive sensing based personalized resource recommendations for help-seekers.
- help-seeking /
- non-interventional perception /
- education informatization 2.0 /
- personalized education /
- precision education

HTML全文

图 1 基于内容的推荐算法

Fig. 1 Content-based recommendation algorithm

下载: 全尺寸图片幻灯片

图 2 基于用户的协同推荐算法

Fig. 2 User-based collaborative recommendation algorithm

下载: 全尺寸图片幻灯片

图 3 基于深度学习的推荐系统架构

Fig. 3 Framework for a deep learning-based recommendation system

下载: 全尺寸图片幻灯片

图 4 个性化学业求助资源推荐原型系统

Fig. 4 Prototype for personalized academic resource recommendation system

下载: 全尺寸图片幻灯片

表 1 非干预式感知技术优点及缺陷

Tab. 1 Advantages and disadvantages of non-interventional perception technology

非干预式感知技术	优点	缺陷
hline辐射扫描技术	成本低, 环保, 耗能少	对人体有害
压电技术	灵敏度高, 可靠	易受干扰, 成本高
多媒体感知技术	集成度高, 交互方便, 易扩展	成本高, 实时传输要求高, 数据冗余
光纤感知技术	灵敏度高, 适应恶劣条件, 耗能少	成本高, 过于敏感
无线电信号技术	成本低, 普及度高, 穿透性强,	信号稳定性稍差
	感知范围大, 适应恶劣条件

下载: 导出CSV

表 2 主流推荐算法的优势和局限

Tab. 2 Advantages and limitations of mainstream recommendation methods

方法	优势	局限
	没有冷启动问题	项目特征提取困难
基于内容的推荐方法	不需要惯用数据	无法发掘用户潜在兴趣
	可解释性较高	新用户无法推荐
协同过滤推荐方法	不需要用户和项目	稀疏性问题
协同过滤推荐方法	大部分应用场景推荐质量高	冷启动问题
	缓解了冷启动问题	需要大量的工作才能得到正确的平衡
混合方法	缓解了稀疏性问题
	可发掘用户潜在兴趣

下载: 导出CSV

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