共查询到20条相似文献，搜索用时 890 毫秒
1.
解析法、列表法、图象法都是函数的表示方法,其中解析法是表示函数的最基本、最重要的方法,中学阶段研究的函数主要是用解析法表示的. 相似文献
2.
3.
商正琴 《数理天地(高中版)》2023,(9):24-25
函数是高中数学的重难点内容,解析式是其最基本的元素之一,函数解析式对研究函数的相关性质具有重要作用,因此求解函数解析式问题也属于一类基本数学问题.配凑法、换元法、赋值法等都是求解函数解析式的有效手段,本篇文章将详细介绍配凑法、代入法、赋值法和换元法在解题中的运用,以此帮助同学们更透彻地理解函数解析式含义,提高解答函数解析式的效率与准确度. 相似文献
4.
函数的常见表示方法有列表法、图象法、解析式法等．如何求一个函数的解析式,是我们在解题中常常碰到的问题．现在就求函数解析式的几种常见方法归纳如下： 相似文献
5.
函数的表示方法有列表法,图象法,解析法等．如何求一个函数的解析式,是大家在解题中常常碰到的问题．本文就求函数解析式的几种常用方法做一梳理。 相似文献
6.
7.
《楚雄师范学院学报》1996,(3)
本文用解析几何中的坐标法（简称解析法）研究了一些平面几何里点的轨迹。用解析法求轨迹问题的基本方法是使轨迹条件解析化。而且，由此再去探讨纯几何的解法则是很方便的。 相似文献
8.
张小萍 《张家口职业技术学院学报》2003,16(2):49-52
解析法适用于精度机构的运动分析。只要将解析结果编程上机，就可完成计算机辅助分析和设计。随着计算机的日益普及，解析法日遂受到人们的重视。 相似文献
9.
吴少华 《数学学习与研究(教研版)》2003,(9):12-13
在多种版本的高中课本中，点到直线距离公式的推导较繁，而且都是利用平面几何或三角知识与解析法结合的方法．如现行《高中数学第二册(上)》主要利用平面几何知识的面积法与解析法相结合．2001年前所用课本《平面解析几何全一册(必修)=》则从图形分两种情况研究并利用三角知识与解析法相结合，推导过程更为繁杂．下面给出纯解析几何的方法推导点到直线距离公式．除过程简捷外，而且体现了解析法的精髓及要点。能使学生体会解析法处理问题的实质性手段．设直线l的方程为： 相似文献
10.
11.
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
12.
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
13.
Qinyi Liu Mohammad Khalil 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1715-1747
The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.
Practitioner notes
What is already known about this topic- Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
- Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.
- Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
- Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
- Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.
- Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
- Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
- Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.
14.
肖俊洪 《中国远程教育(综合版)》2020,(4):1-11,76
学习分析自从2011年出现以来,不管是作为一个研究重点还是实践领域,它一直在发展,从某种程度上讲已经成熟了。学习分析不但在增进我们对学生坚持学习和顺利完成学业的了解以及提高我们教学策略的效果等方面有巨大潜能,它还能帮助学生在更加知情的情况下做出选择。然而,学习分析在多大程度上影响学生学习?它在什么条件下能够充分发挥其潜能?这些问题引起一些关注。我们在这篇概念性文章中提出从生态系统观的角度理解学习分析,或是把它视为某一个生态系统的一部分,或是把它当成一个生态系统,这个系统由各种人为和非人为因素(行动者)组成,包含一系列相互交叉、常常互相依存且又是彼此一部分的变量。鉴于学习分析有提高学习效果的潜能,我们基于学习的社会批判视角提出学习分析的生态系统观。我们从机构和机构以外社会层面的微观、中观和宏观因素出发对学习分析进行阐述。学习分析的生态系统观不认为学生对自己的学习可以免责,而是更加细致入微地了解促成(或妨碍)学习发生的因素(行动者)。 相似文献
15.
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
16.
William J. Ritchie Joseph Kerski Luis J. Novoa Mert Tokman 《Decision Sciences Journal of Innovative Education》2023,21(2):83-94
This teaching brief explores how and why location analytics should be taught in business schools using three objectives. First, an explanation is provided for the importance of including location analytics in the standard business school curriculum—especially in the field of supply chain management. Second, a lack of GIS-based location analytics methods in business school curricula is demonstrated. Third, a three-part location analytics exercise is introduced to contribute to the supply chain management curriculum. The proposed exercise utilizes the output of a GIS-based location analytics software (namely, Esri's ArcGIS Online) as the input for a location set covering problem that can be solved using an integer programming solver. The exercise can also be used as a stand-alone example of GIS in a supply chain management course. This teaching brief aims to (1) develop a new method to use in teaching location analytics in supply chain management and analytics courses and (2) bridge an important gap between supply chain management practice and curriculum. 相似文献
17.
Roland Tormey Cécile Hardebolle Francisco Pinto Patrick Jermann 《Assessment & Evaluation in Higher Education》2020,45(6):901-911
AbstractAlthough it is frequently claimed that learning analytics can improve self-evaluation and self-regulated learning by students, most learning analytics tools appear to have been developed as a response to existing data rather than with a clear pedagogical model. As a result there is little evidence of impact on learning. Even fewer learning analytics tools seem to be informed by an understanding of the social context and social practices within which they would be used. As a result, there is very little evidence that learning analytics tools are actually impacting on practice. This paper draws on research in self-regulated learning and in the social practices of learning and assessment to clarify a series of design issues which should be considered by those seeking to develop learning analytics tools which are intended to improve student self-evaluation and self-regulation. It presents a case study of how these design issues influenced the development of a particular tool: the Learning Companion. 相似文献
18.
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
19.
ABSTRACTThe implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare. 相似文献
20.
Doug Clow 《Teaching in Higher Education》2013,18(6):683-695
Learning analytics, the analysis and representation of data about learners in order to improve learning, is a new lens through which teachers can understand education. It is rooted in the dramatic increase in the quantity of data about learners and linked to management approaches that focus on quantitative metrics, which are sometimes antithetical to an educational sense of teaching. However, learning analytics offers new routes for teachers to understand their students and, hence, to make effective use of their limited resources. This paper explores these issues and describes a series of examples of learning analytics to illustrate the potential. It argues that teachers can and should engage with learning analytics as a way of influencing the metrics agenda towards richer conceptions of learning and to improve their teaching. 相似文献