在线教学中教师与学生教学关联度的影响机制研究——以学前教育一门在线课程为例

随着云计算+工业4.0的整合,以及突如其来的新冠疫情影响,促使在线教学越来越成为主流的教学手段之一。大数据驱动下的在线教学蕴藏着具有广泛应用价值的数据,通过建构教学变量相关研究模型,探索教学行为态势的影响机制,具有较高的应用价值。本研究以疫情期间宁波幼儿师范高等专科学校学前教育专业一门在线课程作为研究对象,以数据挖掘和学习分析为工具,从数据分析层面展示在线教学行为关联序列,针对教学技术应用行为与学生理解程度的态势进行研究分析,挖掘并解析教学行为与学生学习效果的关联度。研究结果显示,数据分析可以揭示学前教育该门在线课程的教学现象与教学模式特性,为该门课程在线教学今后的优化提升提供了论据支撑,同时对学前教育专业其它技术实践类课程的在线教学也具有参考作用。     

社会资本、交易信任和信息不对称 ——个人对个人在线借贷市场的实验研究

个人对个人在线借贷是一种新兴的电子商务形式,自2005年出现起就得到了快速发展.以社会资本理论和社会认知理论为基础,研究个人对个人在线借贷市场中社会资本对交易信任以及出借意愿的影响.研究发现,借款者的社会资本会显著影响出借者的信任和出借意愿.信息不对称程度越高,社会资本对交易信任的影响就越大.     

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P2P网贷是我国互联网金融的重要组成部分,数年来发展迅猛,但是各平台公司良莠不齐,有的给投资人带来了损失,也造成了不良的社会影响。近年来,我国在政策和法律上不断加强对P2P网贷的监管。2016年8月底,银监会、工信部、公安部、国信办制定了《网络借贷信息中介机构业务活动管理暂行办法》规范网贷平台业务活动,促进网络借贷行业健康发展。P2P平台公司无论从自身存亡还是行业发展角度都要做好自律,提高风险意识,重视防范。笔者分析研究了大量P2P网贷行业违法案例,并结合其金融法治从业经验,首先介绍P2P网贷平台的经营模式,重点分析P2P网贷公司的风险和防范,最后指出机遇与挑战。     

内外部网络口碑与在线销售的动态交互过程——基于分层贝叶斯模型的解析

与以往单纯以内部网络口碑、相互独立的内部和外部网络口碑为研究对象,分析网络口碑与在线销售的关系不同。基于评论环境理论,采用分层贝叶斯模型,以当当网、京东、亚马逊和豆瓣网为数据搜集对象,在充分考虑网络口碑异质性及其来源多元化的条件下,结合外部网络口碑平台信息提供的特点,在进一步引入免费试样因素的基础上,对内外部网络口碑与在线销售的动态交互作用进行研究。结果发现：在异质性网络平台并存且效价差异较小的情境下,效价不再是影响在线销售的关键,而内部网络口碑数量、数量信息熵才是影响在线销售的关键,反过来在线销售也刺激了网络口碑数量的提升。虽然外部网络口碑的存在削弱了这一环形机制的积极影响,但免费试样可有效削弱该负向调节机制。     

在线送达法律问题探析——基于在线诉讼的视角

在线送达作为一种采用了现代信息技术的更加快捷、经济、高效的送达方式,应在现有技术水平和法律框架下确认其应有的法律地位,以支持和保障在线诉讼的确立和发展。     

“十三五”开局之年以信息化推动教育现代化新发展——2016年中国教育信息化十大热点新闻解读

目的/意义随着人工智能时代的到来,在高校教学中进行学生学习动机激励机制的探索,寻找激励学生学习动力的课堂教学方法,有利于适应新时代大学教育改革,打造在线开放“金课”,助力高教质量提升。设计/方法基于互联网移动教学平台在本科课堂教学中实施翻转课堂,从ARCS四个维度设计课堂教学模式,构建四维融合学习动机理论视阈下移动平台翻转课堂教学效果影响因素模型,并利用结构方程模型进行实证分析。结论/发现基于ARCS理论设计的翻转课堂教学模式,能够对高校学生学习动机产生积极影响,提升教学满意度并改善教学效果。移动互联网与教育的深度融合,为高校实施“在线金课”教改提供更多的解决思路和实践探索,推动教育技术进步,增强教育活力和创新力。     

基于在线评论数据的产品需求趋势挖掘

随着经济水平的提高和物质生活的丰富,消费者的需求变化也越来越快。能否迎合市场需求的变化是企业产品成功的关键。随着社交媒体的发展,消费者为了分享购物体验发表了许多在线评论信息,其中蕴含着消费者的需求变化。本文在产品特征提取和属性情感分析的基础上,构建了垃圾评论识别模型。然后,利用时间序列分析模型预测下阶段的产品属性关注度和情感计算。最后结合历史数据的变化趋势,分析产品属性的重要性和市场满意情况。利用汽车论坛上的汽车评论数据对本文提出的研究模型进行了验证。研究结果可以为企业制定营销策略以及产品改进与创新提供决策支持。     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

学习支持——确保网络教育质量的关键

本文从国内网络教育和学习者的实际出发,提出以学生学习支持的建设为突破口解决网络教育质量的问题,分析了建立学生学习支持的迫切性,并一般性的提出了建立学生学习支持的原则系统性原则、服务性原则、实际性原则、有效性原则.     

Improvements over the duplicate performance method for outlier detection in categorical multivariate surveys

Summary The detection of errors and outliers is an important step in data processing, especially those errors arising from data entry operations because they are of the entire responsability of the data processing staff. The duplicate performance method, is commonly used as an attempt to detect such type of errors. It implies typically typing twice the same data without any special precedence. If the errors are uniformly distributed among individuals, retyping a fraction of the total will also remove typically the same fraction of the errors. A new method is presented, which is able to improve that procedure by sorting the records putting first the most unlikely ones. The ability of the present methodology has been tested by a Monte Carlo simulation, using an existing database of categorical answers of housing characteristics in Uruguay. At first, it has been randomly contaiminated, and after that, the proposed procedure applied. The results show that if a partial retyping is done following the proposed order about 50 % of the errors can be removed while keeping the retyping effort between 4 and 14% of the dataset, while to attain a similar result with the standard methodology 50% (on, average) of the database should be processed. The new ordering is based upon the unrotated Principal Component Analysis (PCA) transformation of the previously coded data. No special shape of the multivariate distribution function is assumed or required.