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Hadoop is a well‐known big data system and a subject covered in many big data courses. This article describes two role play games for teaching the two fundamental components in the Hadoop framework, MapReduce and Hadoop Distributed File System (HDFS). In the games, students form teams and play different roles as a part of a Hadoop cluster. The games are designed to let students collaborate with peers in the same way as MapReduce and HDFS components collaborate to perform computing jobs in a Hadoop cluster. Utilizing a computer communication channel, the games are designed to let students quickly and effectively understand typical MapReduce and HDFS operations. Survey results from students in two big data classes show that the games effectively improve learning outcome in understanding MapReduce and HDFS workflow and the Hadoop framework in general. Students appear more engaged in class activities and communicate with peers more often. 相似文献
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Betsy Page Sigman William Garr Robert Pongsajapan Marie Selvanadin Mindy McWilliams Kristin Bolling 《Decision Sciences Journal of Innovative Education》2016,14(4):362-381
The expression “big data” is ubiquitous in the business world today, but few undergraduate business students have the opportunity to gain practical experience with how new business analytics tools can be used in decision making. This article describes a set of hands‐on labs that prepare students to incorporate streaming data analysis into group research projects. Splunk is used to help students analyze and visualize streaming social media data. An evaluation of student projects and student survey results show that this practical approach of training students to manipulate and visualize big data was largely successful in achieving instructional goals. 相似文献
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William W. Swart Kenneth R. MacLeod 《Decision Sciences Journal of Innovative Education》2020,18(1):119-137
We show how the principles of flipped learning that have been successfully applied to analytics classes taught face‐to‐face (F2F) at the undergraduate and graduate levels were emulated in corresponding online classes. Student satisfaction in the online flipped analytics classes was compared to student satisfaction in the F2F flipped analytics classes. Data were collected between the Spring 2016 and Fall 2018 semesters and involved two instructors with a sample of 726 students. The results of an independent samples t‐test showed that there was no significant difference in satisfaction between the online and F2F offerings. A binary logistics regression analysis on the data revealed that whether the flipped course was taught F2F or online had no significant effect on students recommending the course to their peers. The results suggest that flipped learning is transferrable to online analytics courses and yields student satisfaction at par with equivalent F2F flipped courses. 相似文献
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Jonathan P. Pinder 《Decision Sciences Journal of Innovative Education》2014,12(3):199-217
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random. 相似文献
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Ivo D. Dinov Selvam Palanimalai Ashwini Khare Nicolas Christou 《Teaching Statistics》2018,40(2):64-73
Statistical inference involves drawing scientifically‐based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization‐based statistical inference infrastructure ( http://socr.umich.edu/HTML5/Resampling_Webapp ) that blends research‐driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user‐provided data. We designed, implemented and validated a new portable randomization‐based statistical inference infrastructure ( http://socr.umich.edu/HTML5/Resampling_Webapp ) that blends research‐driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user‐provided data. The core of this framework is a modern randomization webapp, which may be invoked on any device supporting a JavaScript‐enabled web browser. We demonstrate the use of these resources to analyse proportion, mean and other statistics using simulated (virtual experiments) and observed (e.g. Acute Myocardial Infarction, Job Rankings) data. Finally, we draw parallels between parametric inference methods and their distribution‐free alternatives. The Randomization and Resampling webapp can be used for data analytics, as well as for formal, in‐class and informal, out‐of‐the‐classroom learning and teaching of different scientific concepts. Such concepts include sampling, random variation, computational statistical inference and data‐driven analytics. The entire scientific community may utilize, test, expand, modify or embed these resources (data, source‐code, learning activity, webapp) without any restrictions. 相似文献
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教育信息化经历了学习管理系统（LMS）以及Web2.0应用的变革。新技术的深入应用带来了教育＂大数据＂的高速增长,挖掘这些教育数据潜在价值的迫切需求,使得学习分析应运而生。通过文献分析法,对国内外学习分析文献进行了分析和综述,首先对学习分析进行了概念界定和历史溯源,比较了与学习分析相关概念的区别和联系,之后针对学习分析作为教育信息化新热点,对其研究、发展、技术策略等方面进行了较系统地阐释,最后总结了学习分析目前面临的挑战和愿景,以期可以对学习分析进行全方位的阐述和梳理,并促进该领域的深入研究。 相似文献
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Marina E. Johnson Abdullah Albizri Rashmi Jain 《Decision Sciences Journal of Innovative Education》2020,18(1):90-118
Recent technological advancements in data storage and processing have changed how companies conduct their business. An increasing number of firms have started putting their efforts in extracting information from their databases to improve profitability and reduce costs using quantitative approaches. Thus, the job market has been experiencing a rapidly growing demand for business analytics (BA) practitioners, and universities across the globe are increasingly responding to this newly emerged field by offering both undergraduate and graduate level degrees as well as certificate programs. Thus, this research aims to provide a framework for academic institutions to develop a state‐of‐the‐art master's in business analytics (MSBA) curriculum by identifying concepts, skills, knowledge, and tools (CSKT) that industry seeks in BA practitioners. Our data‐driven methodology utilizes peer institution analysis, indeed.com web scraping, and focus group analysis with mid‐ and senior‐level analytics leaders from major companies. Our contribution to the literature and recommendations to institutions developing MSBA programs are offered at the end. 相似文献
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Foundations of dynamic learning analytics: Using university student data to increase retention 
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下载免费PDF全文 Sara de Freitas David Gibson Coert Du Plessis Pat Halloran Ed Williams Matt Ambrose Ian Dunwell Sylvester Arnab 《British journal of educational technology : journal of the Council for Educational Technology》2015,46(6):1175-1188
With digitisation and the rise of e‐learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper‐based course materials, registration, admissions and support services to the mobile, always‐on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self‐organising maps, to generate conversations, shared inquiry and solution‐seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates. 相似文献
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提出基于云计算平台（以Hadoop为例）应用布尔矩阵Apriori算法进行大数据关联规则挖掘的MR_B_Apriori算法。将Hadoop平台与布尔矩阵Apriori算法相结合,利用MapReduce框架分块处理布尔矩阵,计算出分块数据的频度,合并融合得到大数据集的频繁项集。分析表明MR_B_Apriori算法能够适用于大数据的频繁项集挖掘。 相似文献
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随着“大数据时代”的到来,一股席卷全球的智能化在线教育浪潮正在蔓延,高校传统的教学模式趋向颠覆,高校教师的职业发展也将受到重大挑战。众多大学生在在线学习时将会产生海量的数据,高校教师如何挖掘、分析这些数据,对改进自身教学实践、促进自身专业发展都具有丰富的价值。学习分析在大数据时代的高等教育中被广泛应用已成必然趋势,并具有非常广阔的应用前景,高校教师应该具备较强的数据分析能力。学习分析从以下四个方面促进大数据时代高校教师在线专业发展：可以提升作为在线学习者的高校教师的学习效率,激发其自主的专业发展意识;可以提高高校教师作为在线教学者的教学效率,发展其在线教学实践智慧;可以提高高校教师作为研究者的研究绩效,提升其对学生在线学习的服务能力;可以提高高校教师作为管理者的管理效率,提升其在线教学领导力。 相似文献
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精细化和理性化设计已成为城乡规划设计的趋势,大数据分析技术是城乡规划专业学生量化分析能力的重要构成和体现。城市大数据主要包括环境大数据和行为大数据。按照研究尺度,城市大数据的应用分为区域、总规、控规和街道设计4个层面。依据各类城市大数据特征和应用场景,城市大数据课程划分为理论教学、数据处理和综合实践训练3个模块,分别对应原理背景、软件技术、实践操作的能力提升。理论教学模块主要介绍最新前沿理念以及大数据原理、模式、类型等;数据处理模块主要培养学生的数据挖掘技术、数据处理和清洗校验技术、数据分析能力、数据可视化能力;综合实践训练模块指选取不同的研究课题和应用场景,进行大数据技术的应用实践。 相似文献
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Wilbur W. Stanton Angela D'Auria Stanton 《Decision Sciences Journal of Innovative Education》2020,18(1):138-165
Fact‐based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from seeking applicants with a specific degree to now recruiting analytics talent based on required skill sets. For more than a decade, business schools have been developing new programs in analytics in response to industry's needs. However, in developing meaningful career‐ready professionals, business programs must understand the skills required across different analytics job functions. In this article, the authors present a comprehensive assessment of the skills sought by employers when considering a candidate for an entry‐level analytics position. The authors describe the demand for various types of analytics professionals, identify the job titles and functions with the most significant demand, and then draw a comparison of the job requirements of hard skills, soft skills, software skills, and credentials between three of the most sought‐after analytics areas: data science, data analytics, and business analytics. The authors conclude by providing faculty and administrators with recommendations on how to adapt their courses and programs to provide students with the fundamental preparation necessary for careers in data science, data analytics, and business analytics. 相似文献
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《Performance Improvement》2017,56(7):6-16
Big data analytics technology is changing the human capital development landscape. Global benchmarking trends reveal a gap between what executives expect from our profession and what we are currently capable of delivering. Emerging big data performance analytics technology offers our profession the capability to answer this growing executive expectation to diagnose the current and projected strengths and vulnerabilities of their workforce. It provides real‐time evidence that our performance improvement solutions optimize human and organizational performance. 相似文献
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建立大数据专业课程群,循序渐进地引导学生从基础到综合、从理论到实践,从专业基础课进入到大数据专业核心课程的学习,逐步开设分布式系统、云计算、数据获取、数据处理、大数据挖掘与分析、大数据预测与人工智能、数据可视化等课程,将大数据技能学习路线清晰化、科学化;精心设计课程理论与实践环节,理论教学将案例贯穿到教学内容之中,让学生能够更加直观地理解知识,实践环节采用模块式实验设计,让教师能够根据课程内容对实验内容进行组合,能够有针对性地让学生掌握相关技术。经过实践,大数据课程群建设对学生竞赛、就业及深造起到了较好的支撑作用。 相似文献
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新冠疫情背景下在线教育成为全球共同选择。随着在线教育资源的极大丰富以及教师和学生时空上的隔离,如何实时了解学生学习状态以便为教师提供有效教学反馈,如何为学生提供合适在线教育资源以满足差异化需求,成为在线教育亟待解决的问题与挑战。围绕网络课程智能适配方案,构建学生标签大数据平台,重点分析用户数据分类、获取方式及完整学生画像体系设计方法;结合多属性决策模型,探讨网络课程评级与分类方法;基于协同过滤匹配度算法,探讨实现学生和课程的精确推荐方法。网络课推荐方法不仅有助于提高网络课程利用率,也有助于根据学习者个人特征开展更加灵活有效的网络课程教学。 相似文献
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Davinia Hernández-Leo Roberto Martinez-Maldonado Abelardo Pardo Juan A. Muñoz-Cristóbal María J. Rodríguez-Triana 《British journal of educational technology : journal of the Council for Educational Technology》2019,50(1):139-152
The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students' interactions can be used to increase the understanding of learning experiences. Despite its clear synergy, there is only limited and fragmented work exploring the active role that data analytics can play in supporting design for learning. This paper builds on previous research to propose a framework (analytics layers for learning design) that articulates three layers of data analytics—learning analytics, design analytics and community analytics—to support informed decision-making in learning design. Additionally, a set of tools and experiences are described to illustrate how the different data analytics perspectives proposed by the framework can support learning design processes. 相似文献
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"数据仓库与数据挖掘"是在大数据时代背景下产生的一门新兴的课程,不同的学校针对该课程有不同的教学方法,本文介绍了在教学过程中针对该课程的特点所使用的融合了CDIO工程理念的项目驱动教学方法。本方法以工程教学的思想对教学目标、教学内容、教学方法进行了重新设置,总结了以项目驱动数据挖掘课程教学实践的一般流程,并给出具体的实验教学方案。 相似文献
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Stephen J. Aguilar 《TechTrends》2018,62(1):37-45
We are still designing educational experiences for the average student, and have room to improve. Learning analytics provides a way forward. This commentary describes how learning analytics-based applications are well positioned to meaningfully personalize the learning experience in diverse ways. In so doing, learning analytics has the potential to contribute to more equitable and socially just educational outcomes for students who might otherwise be seen through the lens of the average student. Utilizing big data, good design, and the input of the stakeholders, learning analytics techniques aim to develop applications for the sole purpose of reducing the classroom size to 1. Over time, these digital innovations will enable us to do away with a model of education that teaches toward the non-existent average student, replacing it with one that is more socially just—one that addresses the individual needs of every student. 相似文献