Analyzing learners’ learning behaviors helps teachers understand how learning behaviors of learners influence learning performance. To determine which learning behaviors influence learners’ science-based inquiry learning performance, this work develops an xAPI (Experience Application Programming Interface)-based learning record store module embedded in a Collaborative Web-based Inquiry Science Environment (CWISE) to record detailed data about students’ learning processes. This work discusses whether the significant correlation and cause-effect relationship among science inquiry competence, learning time, and learning performance exist, and examines whether remarkable shifts and differences in the learning behaviors of learners with different learning performances and inquiry competences exist by using sequential pattern mining and lag sequential analysis. The results demonstrate that inquire ability, total learning time in the designed inquiry learning course, and learning time in an inquiry buoyancy simulation experiment are positively correlated with learning performance and can predict learning performance, and the learning time in the inquiry buoyancy simulation experiment appears to be the most significant predictor. The results of lag sequential analyses indicate that learners with high learning performance and high inquiry competence re-adjust hypotheses after performing an inquiry buoyancy simulation experiment, while learners with low learning performance and low inquiry competence lack this critical inquiry learning behavior. This study presents a systematic analysis method to insight the effective learning behaviors in a web-based inquiry learning environment based on mining students’ learning processes, thus providing potential benefits in guiding learners to adjust their learning behaviors and strategies.
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共查询到19条相似文献，搜索用时 671 毫秒
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当前市场经济不断发展,全球竞争日益激烈,对客户满意度的研究已经成为了“以客户为导向”的经营模式的出发点.本文将对客户满意度、数据挖掘技术和基于客户满意度的数据挖掘模型分别进行介绍,并讨论如何利用数据挖掘技术提升客户满意度. 相似文献
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介绍了数据挖掘技术的相关知识和和客户获取过程中的重要影响因素,讨论了在客户获取过程中应用数据挖掘技术的基本环节,并充分肯定其应用前景。 相似文献
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数据挖掘技术在商业银行客户细分中的应用 总被引：1，自引：0，他引：1
傅国锦 《金华职业技术学院学报》2007,7(4):44-47,39
在数据仓库建设基本到位之后,银行应如何对庞大的客户信息进行深层次数据挖掘,建立客户与市场的细分体系,从而在经营与管理中发挥作用,是一个极为重要与紧迫的研究课题.在介绍客户细分理论与数据挖掘技术的基础上,对银行客户细分形式化描述过程模型做了讨论,并以K均值聚类算法对银行实证客户数据进行了挖掘,实验表明数据挖掘技术在银行客户细分方面的应用具有一定的有效性. 相似文献
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基于数据挖掘技术的客户关系管理系统,就是一个组织以数据挖掘技术为平台,将管理资源、业务流程与专业技术进行有效整合,构建的服务消费者的集合,它可以使得组织以更低成本、更高效率地满足客户的需求.本文以我国北方某城电信业为研究对象,着重研究了如何构建基于挖掘技术的客户关系管理系统问题.通过基于数据挖掘技术的客户关系管理系统的构建,电信企业可以根据客户的实际需求提供多样化、层次化、个性化的服务解决方案,以提高客户的满意度和忠诚度. 相似文献
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介绍了数据仓库、知识发现以及数据挖掘的概念,详细分析了聚类分析算法。以电信企业使用数据挖掘工具进行客户聚类分析为例,介绍了如何在税务客户管理中应用聚类技术对纳税人进行客户细分,从而实现对不同类别的纳税人提供有针对性的、个性化的服务。 相似文献
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数据挖掘在客户关系管理中的应用 总被引：1，自引：0，他引：1
黄文英 《黄冈师范学院学报》2006,26(Z1):76-78
该文讲述了客户关系管理(CRM)和数据挖掘之间的关系,以数据挖掘技术在CRM中的应用为研究目标,通过一个实例来验证了数据挖掘的有效性.实例研究以三农种业的客户为研究对象,通过对日常搜集到的客户数据的挖掘,解决该企业从市场细分,到目标市场选择,直至产品销售等一系列问题. 相似文献
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数据挖掘是一种专业性的数据处理技术,在大数据领域的应用优势明显。商业银行经常需要处理大量的客户信息,是典型的数据量巨大的商业领域,在商业银行中应用数据挖掘技术是未来发展的必然选择。在人们对商业银行服务要求越来越高的背景下,加强数据挖掘技术的研究及在商业银行中的应用具有重要意义。 相似文献
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首先介绍了CRM和数据挖掘的基本理论知识;然后通过数据挖掘在CRM中的主要应用和在CRM中启动数据挖掘的基本步骤两个方面对数据挖掘在CRM中的应用进行了详细的说明;最后对数据挖掘技术中的决策树分类算法应用于客户分类进行了研究,着重探讨了其中的SLIQ算法及其改进算法。 相似文献
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随着Web上的信息量剧增,大量有价值的信息隐藏于非结构化文档中,Web数据挖掘的作用愈显重要。本文从Web数据挖掘的定义与分类开始分析,介绍了Web数据挖掘的主要技术以及其在检验检疫管理中的应用实例和实现方法。 相似文献
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利用数据挖掘中的分类、关联规则和聚类等技术,完成了对系统审计数据的分析、分类规则的学习及入侵模型的建立,实现了对入侵攻击及系统异常行为的检测。通过对测试结果的分析,验证了将数据挖掘理论融入安全审计分析是可行和有效的。 相似文献
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Diversification in shopping, a long-pursued subject in consumer behavior analysis, is approached from a broad perspective of the diversity in daily travel patterns, which may or may not involve shopping trips, as well as the diversity in shopping locations and frequency. The focus of this analysis is on the heterogeneity across individuals in the ways in which they each diversify their respective shopping behavior. This study explores differences across individuals in the variations of their shopping travel patterns across days. Treating the day-of-the-week evolution of shopping travel patterns as a stochastic process, characteristics of diversification are quantified for respective individuals. Finally, heterogeneity across individuals is identified using an array of statistical methods. The analysis, based on results of a six-week travel diary survey in Germany with geo-coded activity locations, reveals the effects of individual, household, and urban attributes on diversification in shopping behavior, including that full-time workers with medium incomes (4000–4999 Deutsche Mark per month) tend to have more variations in their shopping engagement. 相似文献
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夏燕 《上海教育评估研究》2021,10(1):74-79
学科评价是高等教育评价的重要内容,是高校学科建设的重要组成部分。在智能化时代,利用人工智能、大数据技术对学科数据进行深度挖掘和科学分析,可以将学科评价从基于小样本或不完整信息的评价转化为基于整体信息的多元化科学化评价。本文通过应用聚类、神经网络分析、关联规则分析等数据挖掘方法对学科数据进行建模分析的思考,对智能化时代高校学科评价进行探索。 相似文献
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讨论了商业智能的基本概念和结构模型,阐述了数据挖掘在商业智能中的作用,并介绍了数据挖掘对象的分类方法和常用的数据挖掘技术。 相似文献
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廖律超 《福建工程学院学报》2012,(3):266-270
分析了公众出行信息特征及影响出行线路选择的因素,设计了多源交通数据融合挖掘的系统框架,并研究了其中的多源数据相关度计算、层次化子空间聚类及联合聚类挖掘等关键技术。理论分析及实验结果表明,系统对于多源交通数据的融合分析及高维数据的降维聚类具有良好的处理能力及可扩展性。 相似文献
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Renuka Mahajan J. S. Sodhi Vishal Mahajan 《Education and Information Technologies》2016,21(1):123-148
An important research area in education and technology is how the learners use e-learning. By exploring the various factors and relationships between them, we can get an insight into the learners’ behaviors for delivering tailored e-content required by them. Although many tools exist to record detailed navigational activities, they don’t explore the learners’ usage patterns for an adaptive e-learning site. The previous web log data analyses, done so far, have been very limited in their scope as they lack detailed empirical results on the learning technology usage. This paper discusses the detailed results of a case study of web data mining in a specific e-learning application. The main objective of this study is to conduct research on usability and effectiveness of the e-content by analyzing the web log. For this, a suitable data set was retrieved from raw web log records, to which various web mining & statistical techniques could be applied. We have evaluated different features of e-content that can lead to better learning outcomes for the learners, by understanding their navigational behaviors, their interaction with system and their area of interest. We found, for example, what sequence of topics were the most liked and the least liked by the learners; we also found that these patterns, lead us to hypothesize, the correlation and regression analysis between the average time, test score and total attempts. 相似文献
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