翟树强Microsoft Word 文档 (4)
高中信息技术人教版必修1优秀教学案例：初识文字处理软件
3.教师对本节课的主要内容进行总结归纳，帮助学生建立知识体系。
（五）作业小结
1.教师布置具有针对性和实践性的作业，要求学生课后进行练习，巩固所学知识。
2.教师提醒学生在课后认真完成作业，培养他们良好的学习习惯。
3.教师鼓励学生在课后自主探索Word软件的更多功能和技巧，提高自己的信息技术水平。
作为一名特级教师，我深知教学内容与过程的详细规划对于教学的重要性。在实施教学内容与过程时，我注重将导入新课、讲授新知、学生小组讨论、总结归纳和作业小结有机结合，形成一个完整、高效的教学流程。在教学过程中，我密切关注学生的学习情况，根据他们的个体差异，适时调整教学内容与过程，确保每个学生都能在课堂上得到有效的学习和成长。同时，我还将注重激发学生的学习兴趣，创设轻松、愉快的课堂氛围，使学生在愉悦的情感状态下，积极参与到教学活动中，提高自己的信息技术水平。
五、案例亮点
1.贴近生活：本节课通过设计具有实际意义的任务，如制作个人简历、班级黑板报等，使学生能够直观地感受到Word软件在生活中的应用，从而提高他们的学习兴趣和积极性。
2.任务驱动：本节课以任务驱动的方式进行教学，让学生在解决实际问题的过程中，自然地学习和掌握Word软件的基本操作方法和技巧。这种教学方式不仅能够提高学生的学习效果，还能够培养他们独立解决问题的能力。
本节课的内容主要包括：Word的启动与退出、界面的熟悉、文档的创建、编辑、保存和打印等基本操作。针对这些内容，我将设计一系列具有针对性和实用性的教学活动，引导学生通过自主学习、合作探究的方式，掌握Word的基本操作，提高他们的信息素养。
在教学过程中，我将注重激发学生的学习兴趣，关注他们的个体差异，充分运用任务驱动、情境教学等方法，引导学生在实践中学会思考、解决问题。同时，通过设置不同难度的任务，使学生在完成任务的过程中，不断提高自己的信息技术水平，为今后的学习和工作奠定基础。
Multi-Robot Coordination in the Robot Soccer Environment
Multi-Robot Coordination in the Robot Soccer EnvironmentAshley Tews and Gordon WyethComputer Science and Electrical EngineeringUniversity of QueenslandAustralia{tews, wyeth}@.auAbstractMAPS (Multi-Agent Planning system) is a system developed for multi-agent coordination developed for use in the robot soccer domain. The system operates without explicit communication of strategy between agents, relying upon observation of team members to produce meaningful coordinated behaviour. Each robot is coordinated with the others by MAPS choosing goal actions that most benefit the team. The choice of goal action is based on the robot’s observations of the rest of the team and their behaviours. The various observations are modelled by superposition of potential fields, where the fields represent the influence of the team, other robots, and components of the environment. This paper outlines the system as it functioned in the UQ RoboRoos entry in the real robot small-size league of RoboCup ’98.Testing and competition results show that this coordination model keeps the robots in good field position in both attack and defence. The improved field position is shown to provide better opportunities for improving ball position and shooting at goal, as well as preventing collisions between robots.1IntroductionCoordinating robots in a dynamic environment is a difficult task. They must be able to carry out their contributions to the overall goal of the system efficiently and effectively while not impeding each other. The focus of multi-robot coordination should therefore be twofold: each robot should consider the objectives of the team while maintaining its own functional integrity. As such, the team plan should exist at a level where it provides strategies for each robot to contribute to the teams success. Each robot must consider the strategy it has been allocated and execute it as best it can without compromising its ability to maintain functional operation.MAPS (Multi-Agent Planning System) is a method developed for multi-robot control. MAPS performs high level multi-robot planning by generating an abstract representation of the robot’s environment at a particular point in time. This representation is built from the robot’s perception of the world. In particular, the representation accounts the position and behaviour of other robots in the environment, either on the same team or those in opposition. This abstraction is carried out using potential fields. By modelling features of the game from each robot’s perspective, strategic commands and coordinates are extracted and proposed to the robots. The robot’s low-level system considers MAPS requests and attempts to fulfil them as closely as possible, given further constraints of dynamic motion and obstacle avoidance. These constraints are particularly evident in the MAPS test environment: robot soccer.RoboCup as a Test EnvironmentRoboCup is an international event where teams of robots play soccer. The event consists of several categories with different requirements for each, in terms of embodiment, size and sensing ability. This paper deals with robots developed for the small size category. Small size teams consist of five robots that play with a golf ball on a walled field the same size as a table tennis table. External features of the teams generally consist of an overhead camera for visual feedback, a computer for processing the visual information and determining gameplay, and some form of communication system between the computer and the robots. There are no restrictions on the system design except for the size of the robots.While reliable electronic and mechanical hardware form part of the engineering solution, the challenge is centred on the design of suitable software. The software problem can be separated into two main areas of concern:1.the control loop for the robots, and2.the design for multi-robot cooperation.The control loop consists of components on-board and off-board the robot to provide an information loop to monitor and control the robots. Designing an efficient control loop is difficult since it contains time-intensive and quality trade-off aspects such as communication to the robots and image interpretation from the camera information. The time delays inherent in these systems make effective control of the robots difficult. This problem is exacerbated by the highly dynamic nature of robot soccer.This paper concentrates on the second problem,multi-robot cooperation. It describes the implementation of MAPS, and evaluates its performance in the robot soccer domain. The system was used on the UQ RoboRoos robot soccer team that came second in the small size league of RoboCup ’98 in Paris and third at the Pacific Rim Series of RoboCup in 1998.The multi-robot cooperation strategy is analysed in Section 2 and shows examples of system performance as well as the algorithms used. Section 3 describes the methods used to determine the integrity of the multi-agent approach in the RoboRoos system and presents the results. Section 4 presents a summary of the concepts in this paper.2Multi-robot CooperationAny multi-agent cooperation system requires complex coordination strategies to gain the full benefit of applying more than one agent to a problem. With mobile robotic environments, this has added difficulties due to the idiosyncrasies of both the hardware and software running the hardware. In the robot soccer environment, it includes communication delays, vision system integrity, inconsistent field conditions, and other robots. Since it must be accepted that many of these idiosyncrasies are beyond control, a robust multi-robot control system needs to be developed. 2.1Defining the ProblemThere are many problems to be addressed when designing a multi-robot strategy for teamwork. Having multiple moving objects in the environment adds dimensions of complexity to path planning. The robots can’t learn where the obstacles will be and must contend with them reactively while maintaining a longer-term strategic plan. The more moving objects in the environment, the more difficult it is to carry out team strategies. This is contradictory to how a multi-agent system should benefit from added agents.When designing a multi-agent system, it is difficult to assess which components should be modified for improvement [Mataric, 1997, Murciano et al., 1997]. Each agent has a responsibility to carry out its task to benefit the team. When the team doesn’t perform well, it can be difficult analysing where the problem is. The agents may have incorrect strategies or the overlying team strategy could be at fault.Mataric [1996, 1997], Claus and Boutilier [1997] have suggested communication as a possible solution in certain ideal environments. In environments such as robot soccer which has centralised vision however, communication is not necessarily beneficial. Each agent on the field can obtain enough information about the state of the environment not to require any extra information from other agents. Adding communication can also cause delays in agent reaction as they may wait on incoming information before deciding their next move [Sen et al., 1994].As a result of these problems, many multiagent designs are environment or problem specific and the technique adopted in one environment may not be transportable to another. However, using controlled environments enables a better focus on the design of the multiagent strategy rather than the effects of the environment. The robotic soccer domain is a good example of a constrained environment for this development. MAPS has been developed within the constraints of the robot soccer environment focusing on multiagent cooperation. The subsequent section describes the MAPS system.2.2MAPS OverviewMAPS consists of a high-level planning algorithm which analyses gameplay and sends commands and coordinates (directives) to the robots’ command interpreters. It is the robots’ responsibility to carry out this plan as best as they possibly can. This method of planning is similar to the plan-executor method described by Agre and Chapman [1990] where the planner contains the strategy for the team while the executor exists as the command interpretation mechanism onboard the robots.Agre and Chapman state that there are two types of planning: hard-wired planning and abstract. The hard-wired planning consists of the agents’ actions being predefined with little contingency inclusion. The planner gives the agents directives and if they can’t carry them out, the plan fails. Contrasting to this is the abstract planning method defined as ‘plan-as-communication’. With this method, the agents are given a high level version of the plan and execute it if they can or decide on a different approach if they deem it more applicable. Hence, the agents have more control in the planning process.MAPS provides a “plan-as-communication” to the robots. It does not rely upon the robots to complete the directives, and continues to observe them after it has been issued. As the robots attempt to carry out their directives, a new plan may emerge of which MAPS may take advantage.The three stages of the MAPS algorithm can be summarised as:1.Get new information: Update the world model basedon new sensor data or prediction of agent behaviour. 2.Choose an action: Based on the influence of thecomponents of the environment (typically agents and obstacles) choose an action type. The action type should designed to reduce interference between team members. Action choice is built from the perspective of the goal of the team.3.Find the location for the action: Based on the actiontype, the influences of the environment’s components can be constructed to show where a robot should perform the action. The choice of location is built from the perspective of the individual robot and its action.In these steps, there is no mention of an explicit team strategy or a coordinated plan. The choice of action and location for that action emerges from the influences of the various components of the environment. The construction of these influences and the manner in which they interact is the key to successful coordination. MAPS uses potential fields for this mechanism.Potential FieldsPotential fields can represent decisions in action space or physical space. The concept behind their use involves creating a virtual map of the physical environment such that physical features are represented by regions ofattraction and repulsion in the virtual map. Different components of the environment or of the agent’s action space of the agent may be represented by components of the field that are superimposed to create a working field. The working field is an abstract and more suitable representation of the real world, allowing informed decisions to be made.Potential fields have been used in a variety of robot applications such as robot motor schemas in navigation [Arkin, 1990], obstacle avoidance [Khatib, 1986; Spence and Hutchinson, 1995] and as action maps for soccer robot movements [Reikki et al., 1998]. Navigation and obstacle avoidance environments are examples of physical space and imply that the agent will traverse the potential field to complete its goal. In action space domains such as soccer robot movements, the potential fields can be used to determine the next appropriate behaviour of the agent.2.3Constructional Elements for the PotentialFields Generated by MAPSThe three stages of the MAPS algorithm are used to determine the directives for each robot in the environment. The actions for the robots are determined as a function of the task. In the soccer robot example, this means either going for the ball or moving to a good location. The locations for the robots to carry out these actions are determined using potential fields. As a result, the action-location paradigm adopted is closely related to the plan or task of the system. The general algorithm can reflect the policies adopted for choosing agent actions and the potential fields generated from this perspective.The soccer robot example builds potential fields (with the implicit team plan encoded) out of the elements described below. Each element is designed to provide low values at attractive regions, and high values at unattractive regions. The elements are combined on a grid array that represents the physical environment such that coordinates are easily extracted. Some elements cover the whole area, while others influence only part.Base FieldFigure 1. Base field representationThe base field mask is a representation of the physical environment and is biased towards the goal of the system. When looking for low valued coordinates, this has the effect of encouraging the agents to move towards areas of interest.Figure 1 shows the base field used for the RoboRoos system. It is a representation of the physical soccer field used in the small sized league. The opposition goal is at the base of the ramp and this representation encodes the desire for the agents to carry out their directives towards it. Object RegionsThis is a field mask that represents an object’s presence in the working field. This mask is relatively small in area and is placed wherever the objects are on the field. The masks represent the objects’ locations, and regions around them considered to be their influence zone. This can be used for robot locations or obstacles.Robot’s PositionEach robot can have an area of responsibility. Once again, this can be an effect of the goal of the system. It may be desired to have the robots maintain responsibility in specific areas. In the soccer robot system, this is used to keep players in their specific field positions (eg, left wing). Distance From Current PositionThis function is added to prevent the planner from selecting coordinates on the boundaries of the potential field. It creates a field-wide virtual ‘dish’ encouraging the selected coordinates to be close to the current position of the object in question. In other words, the potential fields generated can produce desirable coordinates in positions conceivably too far from the robot to be appropriate since the nature of the environment is intended to be highly dynamic, and these locations will be overridden in subsequent planner evaluations.High Value ContinuationIn some cases, the planner evaluates the field in a line-of-sight manner similar to the clear path to object function described below. This is carried out by adding the highest valued coordinate to all other coordinate values from the object to the boundary of the field which has the effect of building a ‘shadow’ of high values where it is undesirable for the agent to go.Clear Path To ObjectIt is important for a robot navigating to positions in the environment to have a clear view of certain objects otherwise their location can’t serve any purpose. This function is represented in the potential field by making all occluded coordinates as high values. Figure 2 demonstrates an example of this. A desired object is located in the centre of the field as shown by the black circle. An obstacle is shown on the right by the white cross. The image shows the area behind the obstacle’s region occluding the object as a plateau of high values where these locations would obscurethe object from the robot.Figure 2. Clear Path To Object function representation2.4Real Time Implementation of MAPS in the Robotic Soccer EnvironmentThe potential fields generated in the RoboRoos system are used to determine the actions and destinations of the robots.The more specific version of the general algorithm for robotic soccer is shown below.Update robot and ball coordinates from vision system Build a field to determine robot actions [PF]for each robotif the robot is best to kick the ballBuild a “kick to” working field [PF]Send “kick to” command to robot Send “kick to“ coordinates to robotelseif we are in control of the ballBuild an attacking “go to” field [PF]elseBuild a defensive “go to” field [PF]Send “go to” command to robot Send “go to” coordinates to robot Algorithm 1. The main loop of the MAPS module for the RoboRoos robot soccer system.The algorithm determines which player is thekicker and gives them coordinates to kick the ball. The other players are given coordinates to move to. The kicking player keeps track of the ball and intelligently navigates to a position where it can kick the ball to the given coordinates.Construction of the Action Selection Working FieldThe action selection field is based on proximity to the ball.The field consists of a single construction element: the distance from current position element. This field is established at the ball. The robot with the lowest value is selected as the kicker, and all other robots are given commands to navigate to.Construction of the Kicking Coordinates Working Field To score more goals than the opposition, the active play should be encouraged towards their goal. A potential field is constructed to determine the best location for the kicker to kick the ball to. Below is the algorithm used.Set basefieldfor each opposition robotAdd opposition robot’s Object Regions Perform High Value Continuation for each grid coordinate on the field Add the Distance From Object (ball)Select lowest valued coordinate on fieldAlgorithm 2. The algorithm determining the kicking coordinatesThe potential field created from this algorithmrepresents bad locations for the ball (eg, near the opposition) as high values while the lower valued locations represent less dangerous areas. The base field is used as the foundation. Since it is impossible to kick the ball behind the robots on the field, the occluded coordinates are given higher values via the high value continuation element to prevent them from being selected.The potential field at this point is still stronglyinfluenced by the base field’s bias towards the opposition goal. This has the effect of making the goal and areas close to it appear as good locations, which is not always true since it could place the ball too far from any home team robots. A compensation function balances this by adding the distance from current position element to each location in the potential field. From the resulting potential field, the lowest valued coordinate is selected and used as the location to kick the ball to. An example is shown in Figure 3 with the opposition players in the configuration shown in the diagram on the right.In Figure 3, the opposition goal is on the right ofthe field. Darker areas represent high values as can be seenfrom the overlapping region between two of the playersFigure 3. Example of the kicking potential fieldcreating the largest peak in the figure. The white area shows the best area to kick the ball. From this potential field, the lowest value coordinate is at the bottom of the opposition goal. This is not obvious from the diagram since it is only slightly lower than other coordinate values in this region. Construction of the Attack/Defend Potential Field When attacking, it is important to position the robots in good locations for them to take a shot at goal or to receive the ball from a pass. The algorithm for determining the attack coordinates is shown below.Set basefieldfor each robotAdd Object Region for that robotAdd Robot’s Position maskApply Clear Path To Object (ball)Add the Distance From Current Position of the robotSelect lowest valued coordinate on fieldAlgorithm 3. The algorithm for determining each robot’s destination coordinates during attackEach robot’s presence is added to the base field to discourage locations close to other robots. To prevent clustering of the home team robots, each robot’s position is added to encourage them to remain in their own area of responsibility. Since it is beneficial to be able to see the ball to receive it, any locations not in sight of the ball are strongly discouraged. Finally, to prevent robots trying to go to extreme locations due to the base field’s influence, the distance from the robot to each grid position is added. From the resulting potential field, the lowest valued coordinate chosen as the destination for the focal robot.The algorithm to produce the potential field for extracting defend coordinates is similar to Algorithm 3 except that it searches for high values instead of low since these will represent locations of the opposition biased toward the home goal. In effect, the robots will be given coordinates to defend that are between opposition robots and the home goal.LogisticsSince the operating environment for MAPS is to be highly dynamic, the directives generated are constantly changing to account for the newest state of the environment to allow the robots to react quickly. In the RoboRoos system, decisions are made every frame processed by the vision system (25 fps) and uses less than 1% of the CPU bandwidth.3ResultsCase study evidence of performance has been gathered from observations of game play under contest conditions. While this evidence supports the MAPS system, further control studies have also been carried out to determine whether the implementation of the MAPS system for the RoboRoos soccer team has any significant affect on the team’s goal scoring performance.3.1RoboCup ’98The most obvious impact of the MAPS system in RoboCup was the field positions maintained by the robots. The field position component of the working field kept the robots operating in their prescribed positions on the field, without limiting the opportunity for a robot to take advantage of a good opportunity that was outside its usual designated area. For example, the defender robot would usually hover back towards the goalkeeper in its prescribed activity area. On occasions where defender became the kicking robot, it would progress the ball rapidly back up the field. As it followed the ball, it would often be the best candidate for taking a shot on goal by virtue of the ball’s region of influence. However, if the shot was unsuccessful and play continued with the defender now out of the influence region of the ball, the defender would retire back into its defensive role.In general play, the field positions kept the robots from interfering with one another, which is the first requirement of the system. There was also clear evidence of a higher level of cooperation with robots passing the ball to one another. While the MAPS implementation does not explicitly contain a passing strategy, passing emerges from the intereaction between the robot using the “kick to”working field, and another robot using the “go to” working field. In both fields, there is an attraction towards free space near the goal mouth. If the kicking robot found the shot on goal to be blocked (by the influence of the high value continuation element), the working field would often contain a region of attraction in open space to either side of the goal mouth. Similarly, the robot using the “go to” field would also be attracted to this space. The effect would be a pass to the free space that a robot was moving towards.The defensive strategy was also highly effective, and often prevented the opposition from making effective attacks. The robots were able to quickly assume a position between the attackers and the home goal preventing progression of the ball. The play was similar to a traditional human soccer “marking up” procedure.The pitfalls of the system were mostly due to unexpected failures of other parts of the system. For instance, if a robot crashed, or was unable to move, the system does not reallocate its task to the remaining players. This was particularly noticeable with the kicker selection field, which would always choose the closest robot. To circumvent this type of problem requires identification of lack of performance of individual robots before re-allocation can be performed.3.2System Evaluation With and Without MAPS While testing during competition illustrates some features of the system, it does not clearly show whether MAPS has any impact on performance. In order to quantify the impact of MAPS on the performance of the robot team, a test was devised to illustrate the effect of removing all coordination. The testing procedures presented here show only the difference between using a multiagent coordination system and not using any coordination at all. It is not a comparison of methods, but only evidence that the system presented is better than having no coordination at all.Given that we have only a single team of robotswith which to test, all tests were conducted against stationary opposition in the configuration shown in Figure 4. The opposition robots are depicted as grey boxes.Figure 4. Opposition setup for testing.The first tests that were conducted used the multi-agent coordination method described in the preceding section. The second (control) tests were performed by simply telling each robot to kick the ball to the opposition goal. In this comparison, the first system consists of using team and agent plans that conform more towards the plan-by-communication method while the second system is purely a plan-by-command method. Tables 1 and 2summarise the results of five sessions of five minutes play under RoboCup rules.Time period Goals Scored Goals Against Locked Robots Free Balls 1803127031360404602256041Average6.63.21Table 1. Results of system playing with coordination switched on.Time period Goals Scored Goals Against Locked Robots Free Balls 1303225045340434609055152Average4.60.252.4Table 2. Results with coordination off.The results show an improvement using the multi-robot coordination system. In the games without coordination, there were many problems with robot-robot collisions, often resulting in the robots becoming temporarily deadlocked. The robots tended to cluster near the ball, so that if the ball rebounded to the other end of the field, it took a long time to recover, particularly with the robots impeding one another. Another side-effect of clustering was the number of free balls that resulted from the robots being too close to the ball and being unable to move closer to kick it. The coordinated team however,maintained a more even presence across the field ensuring that there was always one player to attend to the ball.Robot-robot collisions happened less in the coordinatedteam and the game play was more organised.4SummaryIf a group of agents share a common representation of the world, and a common way of choosing actions, it is possible to produce emergent cooperative strategies without explicit communication. MAPS implements this idea by modelling the world in a coarse grid structure and producing a working field that illustrates the influence of the components of the environment. MAPS forms cooperative strategies by observing team agents at the current point in time and choosing appropriate actions to increase the likelihood of cooperation in the near future. The strategies produced by MAPS are “plans-as-communication” where the execution module uses the plan as a resource for intelligent navigation, rather than as an explicit command.Use of this type of multi-robot control has proven effective in the robotic soccer environment with the RoboRoos achieving second place in RoboCup ’98 in Paris in 1998. The emergent properties and robustness of MAPS overcame many of the problems that exist in the vision and navigation systems of the RoboRoos to produce a cooperative team of robots that were capable in both attack and defence.References[Agre and Chapman, 1990] Agre, P., Chapman, D.: What are Plans For? Robotics and Autonomous Systems, 6,(1990) 17-34.[Arkin, 1990] Arkin, R.: Integrating Behavioral, Perceptual,and World Knowledge in Reactive Navigation. Robotics and Autonomous Systems, 6. (1990) 105-122.[Claus and Boutilier, 1997] Claus, C., Boutilier, C.: The Dynamics of Reinforcement Learning in Cooperative Multi-agent Systems. AAAI-97 Workshop on Multi-agent Learning, (1997).[Khatib, 1986] Khatib, O.: Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, Vol. 5, No 1, (1986) 90-98.[Mataric, 1997] Mataric, M.: Reinforcement Learning in the Multi-robot Domain, Autonomous Robots, 4(1), (1997)73-83.[Mataric, 1996] Mataric, M.: Using Communication to Reduce Locality in Distributed Multi-agent Learning.Brandeis University Computer Science Technical Report CS-96-190, Nov 1996.[Murciano and Millan, 1997] Murciano, A., Millan, J.:Learning Signalling Behaviors and Specialization in Cooperative Agents. Adaptive Behavior, Vol 5, No 1,(1997) 5-28.[Reikki et al ., 1998] Reikki, J., Pajala, J., Tikanmäki, A.,Röning, J.: Executing Primitive Tasks in Parallel. Proc. of the Second RoboCup Workshop, Paris 1998, 339-345.[Sen et al., 1994] Sen, S., Mahendra, S., Hale, J.: Learning to Coordinate Without Sharing Information. Proceedings of the Twelfth National Conference on Artificial Intelligence, (1994) 426-431.[Spence and Hutchinson, 1995] Spence, R., Hutchinson, S.:An Integrated Architecture for Robot Motion Planning and Control in the Presence of Obstacles With UnknownOpposition EndHome End。
Word2013解决方案等长篇文档排版教程
Word长篇文档排版1 引言在写作长篇文档的时候，经常需要根据特定的格式要求对文档进行排版，使文章更加的规范、整洁、美观。
Word是广为使用的文档排版软件，使用Word能够对文章进行专业排版，并且操作简单，易于使用。
在实际排版使用中，有一套较为实用的排版流程，现总结成文，同时以Word2013为基础，对Word长篇文档排版中经常遇到的问题加以说明。
Word长篇文档排版的一般步骤为：1、设置页面布局；2、设置文档中将要使用的样式；3、制作各章标题；4、设置页眉页脚；5、正文中图片、表格和公式的自动编号及正文引用；6、参考文献的标注及引用。
2 设置页面布局2.1 基本设置文档的页面页面布局是排版的第一步，利用它可以规范文档在使用哪种幅面的纸张，文档的书写范围，装订线等信息，设置文档的页面布局在Word2013的【页面布局】选项卡，如图2-1。
在图中1区可以分别设置页边距等信息，需要更详细的设置单击2区的右下箭头，打开【页面设置】详细对话框，如图2-2。
图2-1 设置文档的页面布局图2-2 页面设置常用选项【页边距】选项卡中，可以根据需要设置上下左右边距及装订线位置，页边距各参数的含义如图2-3，【纸张】选项卡中可以设置页面纸张类型，一般选“A4”即可，【版式】选项卡可以设置有关节的相关信息及页眉页脚的布局。
图2-3 页边距各参数含义2.2 让文字更清晰长篇文档一般很多文字，年纪大的人阅读起来比较吃力，为使用文字更清晰，一般采用增大字号的方法，但效果并不理想，其实在页面设置中调整字与字、行与行之间的间距，即使不增大字号，也能使内容看起来更清晰，具体步骤如图图2-4。
图2-4 设置文档网络让文字更清晰3 定义文档中将要使用的样式样式是文档中文字的呈现风格，通过定义常用样式，可以使相同类型的文字呈现风格高度统一，同时可以对文字快速套用样式，简化排版工作，而且，Word 中许多自动化功能（如目录）都需要使用样式功能。
2020新教材小学信息技术四上第6课《初识文字处理软件》
二、认识新朋友
T：接下来让我们比较下这两款软件的相同点和不同点（PPT出示，让学生比较，老师加以引导，适当激励）
（通过比较两款软件的相异点，锻炼S的观察力和判断力。）
T：刚刚我们已经略微比较了这两款软件的相异点，接着请同学们看屏幕，让我们一起来认识下我们的新伙伴--Word。(PPT出示Word完整界面，向学生详细讲解）
（强调下掌控光标的定位及修改的准确性，该学生如只用了一种删除的方法（delete/backspace)，请另外同学加以补充）
T：这两个方法都能起到删除的作用，老师先示范一下（这里提出“撤销”的功能）现在我再给同学们两分钟的时间，让大家感受下这两种方法有什么差异。
T：哪位同学已经发现了这两种方法的不同点？
T：有谁发现了这篇短文的瑕疵？
(S找出文章中的错别字。)
T：你们个个都是火眼金睛，观察的非常仔细。把这篇短文中的错别字都找出来了，那么接下来我们的任务就是要利用Word来修改错别字。先请同学们在本机上进行尝试，看看你能用几种方法进行修改，我给大家2分钟的时间。
（S本机上尝试）
T：请大家将手离开鼠标和键盘，老师刚才发现大多数同学的表现都很棒，现在我要请一位同学上台来演示，然后跟大家讲解下你是用什么方法来删除这个错别字。
S：写字板。
T：嗯，很好，大家的记忆力都很棒，那么接下来我要请同学们在自己的电脑上请出写字板，有困难的同学可以向同桌或者老师求助。
（通过回忆旧知，激发S学习兴趣）
T：非常棒，但是其实啊，“写字板”在处理文字的时候，有时候会“力不从心”，接下来让我们来认识下一位更加本领高强的好帮手--Word（PPT展示），那么谁愿意上台来尝试着将Word请出来为我们服务？（引入桌面Word图标）
Word大作业指导
Word大作业指导一、任务背景在现代办公环境中，微软的Word已成为最常用的文字处理软件之一。
为了提高大家对Word软件的熟练程度，我们决定组织一次Word大作业。
本次大作业要求参预者利用Word软件完成一篇文章的排版和编辑工作，以展示他们对Word软件的掌握程度。
二、任务要求1. 文章主题：自由选择，可以是任何感兴趣的话题，但需符合道德规范和公司政策。
2. 文章字数：至少1000字，不超过1500字。
3. 文章结构：包括标题、引言、正文和结论四个部份。
4. 标题：字体大小为16号，加粗，居中。
5. 引言：字体大小为12号，段落首行缩进，行间距为1.5倍。
6. 正文：字体大小为12号，段落首行缩进，行间距为1.5倍。
7. 结论：字体大小为12号，段落首行缩进，行间距为1.5倍。
8. 页面设置：页面尺寸为A4，页边距上下摆布均为2.54厘米。
9. 标题样式：使用Word内置的标题样式，确保标题层次清晰。
10. 段落格式：正文和结论段落采用左对齐，首行缩进2个字符。
11. 字体样式：正文和结论字体使用宋体，字号为12号。
12. 插入图片：至少插入一张图片，图片大小适中，与正文内容相关。
13. 插入表格：至少插入一个表格，表格包含至少三列三行，内容具有一定的数据关联性。
14. 插入页眉和页脚：页眉包含文章标题，页脚包含页码。
15. 文章排版：确保整篇文章的排版整齐、美观，段落间距一致。
三、任务步骤1. 打开Word软件，选择“新建文档”。
2. 设置页面尺寸和页边距：点击“布局”选项卡，选择“页面设置”，在“页面设置”对话框中设置页面尺寸为A4，页边距上下摆布均为2.54厘米，点击“确定”。
3. 编写文章标题：在文档的第一行输入文章标题，选中标题文本，设置字体大小为16号，加粗，居中对齐。
4. 编写引言：在标题下方空一行，输入引言内容，设置字体大小为12号，段落首行缩进，行间距为1.5倍。
5. 编写正文：在引言下方空一行，输入正文内容，设置字体大小为12号，段落首行缩进，行间距为1.5倍。
Lbobib高中信息技术教案---word操作（共5份8课时）w
Lbobib高中信息技术教案---word操作（共5份8课时）w
生命是永恒不断的创造，因为在它内部蕴含着过剩的精力，它不断流溢，越出时间和空间的界限，它不停地追求，以形形色色的自我表现的形式表现出来。
－－泰戈尔
课题: word复习---8道习题
课时:2课时
教学设想:通过课堂练习规范的习题，让学生复习了解word功能，操作更熟练。
教学目标: 认知目标：1、文章中文字的格式设置。
2、文章中段落的格式设置。
3、word中插入图片、表格等相关操作。
能力目标：通过课堂练习，使学生复习word基本操作并进行归纳总结。
教学重点：对练习题目的整体要求和细节要求的准确理解。
教学难点:学生掌握的基础操作有限，题目涉及到的方面比较广，要查漏补缺。
德育目标：学习和做题是相辅相成的，缺一不可。
教学准备:黑板和粉笔、极域电子教室。
教学过程：。
Word 2010的高级应用教案
2．操作演示：（在提前设计的案例中按照要求进行操作）
（二）多级列表设置法
1．方法讲解：
“开始”→“段落”→“多级列表”→“定义新的多级列表”。
1)选择标题级别：左上角的1～9级列表选择即可；
（通过学生作业与效果测评，自我评估教学效果和教学质量，查找存在问题，思考改进对策措施。）
一、目的
根据《安全生产法》规定，从业人员超过三百人的，应当设置安全生产管理机构或者配备专职安全生产管理人员；从业人员在三百人以下的，应当配备专职或者兼职的安全生产管理人员，或者委托具有国家规定的相关专业技术资格的工程技术人员提供安全生产管理服务。为加强公司的安全管理，实现“安全第一、预防为主、综合治理”的方针，特制定本制度。
3、车间负责人职责
（1）贯彻执行公司安全管理制度，全面领导本车间安全生产工作。
（2）落实车间安全生产目标，开展具体的实施工作。
（3）参与公司召开的各类安全会议，向本车间员工传达公司安全指示精神。组织召开车间安全会议。
（4）协助、配合公司相关车间进行安全工作检查，落实安全整改措施。
（5）组织车间开展、参与安全培训。
六、课题小结（回顾总结，提出重点，突破难点）
七、作业布置与效果测评
（一）作业布置与问题解答（习题册内容P28-30）
（通过作业批阅，发现学生存在问题，进行作业点评与问题解答。）
（二）效果测评的组织实施（教材P101页）
（通过“自评、互评与师评”，检查“知识目标”与“能力目标”的达标情况。）
八、教学诊断与改进
2.掌握大纲级别的设置方法，能够通过标题样式和多级列表法设置大纲级别；
计算机应用基础课件-第3章 Word 应用-实训素材-课后实验
典型实验实验1 基本Word文档制作一、实验目的1、文字的输入和字符、特殊符号的输入2、掌握文本的选定、插入、复制、移动、粘贴、删除操作，以及查找及替换等基本操作。
3、掌握Word字符、字体、字号、段落、分栏等格式的设置。
4、熟悉Word页眉页脚、页面格式的设置。
5、掌握项目符号与编号的使用方法。
二、实验任务按下列样文创建Word文档。
以“实验一.docx”为文件名存盘。
三、实验要求1、文本录入和保存文档。
(l)新建一个空白文档。
(2)设置自动保存间隔时间为3min。
(3)在新建文档中录入以下文字：随着数据源越来越多，数据信息越来越碎片化，不仅给企业的数据管理带来了困局，同时也导致技术人员在大数据处理分析时必须要使用会更加精细化的数据处理工具和更加垂直创新的数据模块。
国内的大数据产业政策日渐完善，技术、应用和产业都取得了非常明显的进展。
以下对计算机技术应用发展分析。
趋势一：数据的资源化何谓资源化，是指大数据成为企业和社会关注的重要战略资源，并已成为大家争相抢夺的新焦点。
因而，企业必须要提前制定大数据营销战略计划，抢占市场先机。
趋势二：与云计算的深度结合大数据离不开云处理，云处理为大数据提供了弹性可拓展的基础设备，是产生大数据的平台之一。
自2013年开始，计算机技术已开始和云计算技术紧密结合，预计未来两者关系将更为密切。
除此之外，物联网、移动互联网等新兴计算形态，也将一齐助力大数据革命，让大数据营销发挥出更大的影响力。
趋势三：科学理论的突破随着大数据的快速发展，就像计算机和互联网一样，大数据很有可能是新一轮的技术革命。
随之兴起的数据挖掘、机器学习和人工智能等相关技术，可能会改变数据世界里的很多算法和基础理论，实现科学技术上的突破。
未来，数据科学将成为一门专门的学科，被越来越多的人所认知。
各大高校将设立专门的数据科学类专业，也会催生一批与之相关的新的就业岗位。
与此同时，基于数据这个基础平台，也将建立起跨领域的数据共享平台，之后，数据共享将扩展到企业层面，并且成为未来产业的核心一环。
长江大学办公自动化结课作业
办公自动化高级应用2013—2014学年 第二学期 《办公自动化高级应用》 课程考试试卷注意：1、本试卷共2页 2、考试形式：上级考试 3、考试时间：110分钟 考试要求：请根据此模板，排版出一模一样的WORD 文档。
（此字体为黑体四号，无左右缩进，无首行缩进，无段前段后间距，字符间距为标准字间距，两端对齐方式，行间距固定值16磅）一、 请根据此模板排版一份word 文档（40分）要求：纸张大小为A4（21×29.7cm ）；页边距采用上下边距为2.5cm ，左右边距为3.2cm ，装订线为左边0cm 处；版式：页眉1.5cm 、页脚1.75cm （2页word 文档页面设置要求均这样）。
第一页页眉为：办公自动化高级应用，第二页页眉为：宋文广；第一页页脚为：第1页；第二页页脚为：第ⅱ页。
其他内容均与此模板中内容一样。
（该段字体为宋体四号，无左右缩进，无首行缩进，无段前段后间距，字符间距为标准字间距，两端对齐方式，行间距为固定值20磅）二、Word 排版（10分）要求：内容用小四号，宋体字，文字的行间距采用多倍行距“1倍”，无左右缩进，无段前段后间距，首行缩进2个字符，字符间距采用标准字间距，分为2栏编排。
谓特色人才？该院院长一语中的：“就是要从我们的实际出发，培养一线工程师。
” 根据此理念，○该学院不断推进教学改革，坚持打造特色，目标就是要将学院建成特色人才的摇篮。
广gu ǎng 泛f àn 而ér 深sh ēn 入r ù的de研y án 究ji ū。
SmartArt 图何宋文广第ii 页三、请先用excel2007制作一张如同下面所示的表格，将其复制到此word 文档中（30分）加权平均分：=(C5*4.0+D5*4.5+E5*3.0+F5*2.0)/（4.0+4.5+3.0+2.0） 成绩评定：=LOOKUP(G5,{0,60,85},{"不合格","合格","优秀"}) 全班排名：=RANK(G5,$G$5:$G$14)四、简答题。
3月19日 Microsoft Word 文档 (2)
6、打马冠林，徐睦涵等学生回访电话。
7、高Байду номын сангаас李嘉维交费7695元，高二英语小班冉梦鑫交费400元。
明日工作目标
1、打外呼电话和回访电话。
2、安排初三学生曹粟怡化学试讲。
3、调整尤良课程并打印成表。
4、追缴高一赵利智尾款。
问题建议
领导点评
每日工作报告
部门
咨询部
姓名
郝沙沙
时间
3月16日
当天工作完成情况
呼出数
有效数
接待家长
接待电话
消课时
回访电话
业绩
21
2
12
25
8095元
1、高三尤良加化学课1次。
2、接待一名初二学生家长，想上数学和物理1对1，继续跟踪。
3、安排初三学生曹粟怡试听化学，失败，明天从新安排试讲。
4、跟田老师一起接待1名高一家长，试听高一数学班。
Word中的和索引技巧快速导航大纲
Word中的和索引技巧快速导航大纲Microsoft Word是一款功能强大的文字处理软件，广泛应用于办公、学术和个人领域。
在处理大量文档时，快速导航和整理内容是非常重要的。
本文将介绍一些在Word中使用和索引技巧的方法，帮助您更高效地导航和管理文档大纲。
一、使用标题样式快速生成大纲在Word中，使用标题样式是一种有效的方式来快速生成大纲。
标题样式提供了不同级别的标题格式，可以根据需要设置为一级、二级、三级标题等。
使用标题样式的好处是可以自动生成大纲，方便后续导航和整理。
步骤如下：1. 在Word文档中选择需要设置为标题的文本；2. 在Word工具栏的“开始”选项卡中，选择合适的标题样式；3. 选定文本后点击相应标题样式，Word将自动应用该样式，并生成相应级别的大纲。
二、创建书签以便快速跳转Word中的书签功能可以用来定义并标记特定内容，方便快速导航。
通过创建书签，您可以在文档中设置一个或多个点，以便随时跳转到指定位置。
步骤如下：1. 选中您要设置书签的位置；2. 在Word工具栏的“插入”选项卡中，点击“书签”按钮；3. 在弹出的对话框中，输入书签的名称，并点击“添加”按钮。
之后，您可以通过以下步骤快速跳转到书签所在的位置：1. 在Word文档中，点击工具栏的“插入”选项卡；2. 点击“书签”按钮，选择您设置的书签名称；3. 单击“转到”按钮，即可跳转到书签所在的位置。
三、使用目录快速导航内容目录是Word中用来展示文档结构和内容的重要元素。
通过正确使用目录功能，可以快速导航到文档中的不同章节和内容。
步骤如下：1. 打开您想要插入目录的Word文档；2. 将文本设置为标题样式，以显示文档结构；3. 在文档中需要插入目录的位置，点击Word工具栏的“引用”选项卡；4. 单击“目录”按钮，在弹出的菜单中选择合适的样式。
Word将根据文档的标题样式自动生成目录，并添加对应的页码。
通过点击目录中的章节标题，您可以快速跳转到相应内容所在的位置。
Word2010长文档使用技巧与实战方法(大纲、段落、插入文件等)
Word2010使用技巧与实战方法：快速编辑长篇文档时间：2013-02-06来自：会计网编辑：雪梅现在大家主要都是用MicrosoftWord来编辑长篇文档(不论各位用哪个版本，基本功能都是一致的，以下简称Word)。
如果不能充分Word的一些强大功能，大家在撰写和编辑较长篇幅的科技长篇文档的时候，可能经常要为不断地调整格式而烦恼。
在这里我把自己以前使用Word的经验和教训总结一下，以求抛砖引玉。
一篇长篇文档应该包括两个层次的含义：内容与表现，内容是指文章作者用来表达自己思想的文字、图片、表格、公式及整个文章的章节段落结构等，表现则是指长篇文档页面大小、边距、各种字体、字号等。
相同的内容可以有不同的表现，例如一篇文章在不同的出版社出版会有不同的表现;而不同的内容可以使用相同的表现，例如一个期刊上发表的所有文章的表现都是相同的。
这两者的关系不言自明。
笔者认为，长篇文档“表现”的编辑，是一个非常费时费力的工作。
如果在写长篇文档之前，做了各方面的准备，并按照一定的规律来编写和排列，会起到事半功倍的效果;否则，会给你带来无穷无尽的痛苦。
笔者根据自己写硕士长篇文档的体验，向各位提供如下建议，供大家参考。
用好样式编写长篇文档，一定要使用样式，除了Word原先所提供的标题、正文等样式外，还可以自定义样式。
如果你发现自己是用选中文字然后用格式栏来设定格式的，一定要注意，想想其他地方是否需要相同的格式，如果是的话，最好就定义一个样式。
对于相同排版表现的内容一定要坚持使用统一的样式，这样做能大大减少工作量和出错机会。
如果要对排版格式(文档表现)做调整，只需一次性修改相关样式即可。
使用样式的另一个好处是可以由Word自动生成各种目录和索引。
一般情况下，不论撰写学术长篇文档或者学位长篇文档，相应的杂志社或学位授予机构都会根据其具体要求，给长篇文档撰写者一个清楚的格式要求。
比如，要求宋体、小四，行间距17磅等等。
这样，长篇文档的撰写者就可以在撰写长篇文档前对样式进行一番设定，这样就会很方便的编写长篇文档了。
WORD11-17
WORD11-17第一篇：WORD11-17第十一套在考生文件夹下打开文档WORD.DOCX，按照要求完成下列操作并以该文件名（WORD.DOCX）保存文档。
【背景素材】为召开云计算技术交流大会，小王需制作一批邀请函，要邀请的人员名单见“Word人员名单.xlsx”，邀请函的样式参见“邀请函参考样式.docx”，大会定于2013年10月19日至20日在武汉举行。
请根据上述活动的描述，利用Microsoft Word 制作一批邀请函，要求如下：1.修改标题“邀请函”文字的字体、字号，并设置为加粗、字的颜色为红色、黄色阴影、居中。
2.设置正文各段落为1.25倍行距，段后间距为0.5倍行距。
设置正文首行缩进2字符。
3.落款和日期位置为右对齐右侧缩进3字符。
4.将文档中“×××大会”替换为“云计算技术交流大会”。
5.设置页面高度27厘米，页面宽度27厘米，页边距（上、下）为3厘米，页边距（左、右）为3厘米。
6.将电子表格“Word人员名单.xl sx”中的姓名信息自动填写到“邀请函”中“尊敬的”三字后面，并根据性别信息，在姓名后添加“先生”（性别为男）、“女士”（性别为女）。
7.设置页面边框为红“★”。
9.将设计的主文档以文件名“WORD.DOCX”保存，并生成最终文档以文件名“邀请函.DOCX”保存。
答案：1.【解题步骤】步骤：根据题目要求，选中“邀请函”文字，单击【开始】选项卡下【字体】组中的“字号”下拉按钮，在弹出的下拉列表中选择适合的字号，此处我们选择“三号”。
按照同样的方式在“字体”下拉列表中设置字体，此处我们选择“黑体”，单击“加粗”按钮设置字形为加粗，并在“字体颜色”下拉列表中选择“红色”，在“以不同颜色突出显示文本”下拉列表中选择“黄色”，最后单击【段落】组中的“居中”按钮即可完成设置。
2.【解题步骤】步骤1：选中正文，单击【开始】选项卡下【段落】组中的对话框启动器按钮，弹出“段落”对话框。
2015-2016学年上学期教学内容
一、Word电子文档（第1课时）1、打开Word方法2、认识Word窗口及工具栏功能3、页面设置（纸张大小）（第2课时）4、设置字体5、设置段落（第3课时）6、图片、艺术字（第4课时）7、表格（第5课时）8、插入页码、页眉、页脚9、查找、替换一、PowerPoint 演示文稿（第1课时）1、打开PowerPoint方法2、认识PowerPoint窗口及工具栏功能3、设置字体4、插入、删除幻灯片（第2课时）5、设置自定义动画效果6、设置幻灯片切换方式（第3课时）7、设置背景8、设置版式二、Excel 电子表格（第6课时）1、打开Excel方法2、认识Excel窗口及工具栏功能3、认识工作簿、工作表、单元格4、输入数据、设置字体（第7课时）5、行、列（增加、删除、高度、宽度）6、单元格区域合并及居中（对齐方式）7、设置边框（线型、颜色）（第9课时）8、公式【常用函数：求和（SUM）、求平均值（A VERAGE）、求最大值（MAX）、求最小值（MIN）、计数（COUNT）】。
9、设置数字格式（小数点位数、百分比位数）（第9课时）10、排序【升序（从小到大）、降序（从大到小）】。
11、筛选（第11课时）12、插入图表（第12课时）13、查找、替换（第4课时）9、插入声音、视频10、插入按钮11、设置超链接二、综合练习（第5课时至学年末）1、信息技术基础知识2、Word 电子文档3、Excel 电子表格4、PowerPoint 演示文稿5、综合练习2015-2016学年上学期教学内容（八年级信息技术）——杨文强2015-2016学年下学期教学内容（八年级信息技术）——杨文强。
利用Word VBA程序快速自动编排离子反应方程式
利用Word VBA程序快速自动编排离子反应方程式
刘贵伟;廉锁原;彭彩红
【期刊名称】《辽宁师范大学学报（自然科学版）》
【年(卷),期】2008(031)004
【摘要】离子反应方程式的编排比较烦琐,当对大量方程式进行编排时,需要耗费很多时间.针对这一问题,设计离子反应方程式自动编排的Microsoft Word VBA程序,该程序不仅可以便捷快速地编排离子反应方程式,而且适用于化学方程式、分子式和简单离子的编排,用户可以先输入无格式文本,然后选中文本,执行程序,就可以编排好,旨在减轻方程式编排的工作量.
【总页数】4页(P439-442)
【作者】刘贵伟;廉锁原;彭彩红
【作者单位】大连工业大学,学报编辑部,辽宁,大连,116034;大连工业大学,化工与材料学院,辽宁,大连,116034;大连工业大学,学报编辑部,辽宁,大连,116034
【正文语种】中文
【中图分类】G232
【相关文献】
1.利用Microsoft Word及Word VBA快速实现试卷排版 [J], 初人山
2.基于AutoIt3和VBA的Word操作题自动批量批改程序的设计与实现 [J], 何剑
3.利用VBA对word操作自动评分提高课堂效率的研究 [J], 马喜红
4.利用Word VBA实现快速试卷编号 [J], 廉锁原;高世萍;李志刚
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因版权原因，仅展示原文概要，查看原文内容请购买。
巧用Microsoft Word实现科技期刊编排自动化
巧用Microsoft Word实现科技期刊编排自动化
温慧娟
【期刊名称】《编辑之友》
【年(卷),期】2004()S1
【总页数】2页(P69-70)
【关键词】期刊编排;Microsoft Word;科技期刊;子菜单;表格处理;编辑部;宏命令;宏语句;格式文本;文档;资料档;巧用
【作者】温慧娟
【作者单位】中国石油化工股份有限公司济南分公司<济炼科技>编辑部
【正文语种】中文
【中图分类】G230.7
【相关文献】
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2.利用Microsoft Word及Word VBA快速实现试卷排版 [J], 初人山
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5.Microsoft Word97在科技期刊排版中的应用 [J], 毛承洁
因版权原因，仅展示原文概要，查看原文内容请购买。
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教师节发言材料
拒绝平庸享受平凡
徂徕一中：翟树强
2009 年9 月
各位领导、老师：
大家好！
我是一中一名普通教师，任八年级四个班和七年级三个班的历史课，担任七年级六班的班主任。
近几年来在各级领导的关心和支持下，在同事们的帮助下，我在教育教学上取得了一点成绩，先后获得了区骨干教师、历史教学能手、教学成绩优秀教师、教学成绩优秀班主任、历史优质课一等奖等荣誉称号，尤其今年我的历史成绩居镇第一名区前列，被评为泰安市优秀教师。
我万分感激领导对我的褒奖和厚爱。
借此机会，对帮助和关心我的领导真诚地说一声谢谢！我知道这些荣誉不仅仅属于我个人，更应该属于全镇几百名默默耕耘的老师们。
我的工作极其平凡，微不足道，我只是做了我该做的事，站在这里发言我很荣幸，感谢领导给我这个机会，今天我向领导和老师们汇报的题目是《拒绝平庸，享受平凡》。
一、提高教学质量——是我人生不懈的追求
教学质量是第一生命线，是每位教师站稳讲台的关键，我始终坚持把提高教学质量放在第一位，我的理想是把平凡的教学变得不平凡。
第一爱岗敬业
徂徕是一方热土，这里有平易近人的领导，有团结向上的战斗集体，有和谐舒心的工作环境，有亲如一家的同事，如此优越的条件激发了我的无限热情，我全身心地扑在教学上，视教学为生命，以校为家。
每天我都早早起床，到宿舍喊喊懒虫查查卫生，到教室维持秩序
督促学习，晚上等到学生熟睡我才离校；每天我都要精心备课认真上课仔细批改作业，不敷衍塞责；每天我都情绪高昂积极向上，展现阳光心态。
第二提高自身素质
要给学生一碗水，自己得有一桶水，在如今的信息时代，更是如此，对老师的知识、能力、素养都提出了更高的要求。
所以我深入学习各种现代教育理论，牢固树立素质教育理念，及时更新教学观念，做到与时俱进；努力钻研业务，完善知识结构，提高教学能力；经常参加集体备课和听评课活动，向老教师和骨干教师学习，取其之长补己之短，提高教学水平。
第三潜心教改教研
优化教学方法，以学生为主体，教师为主导，贯彻落实区教研室“历史六步法课堂教学模式”和“小组合作学习”课题；优化课堂教学结构，活跃课堂气氛，培养学生学习兴趣；把现代教学手段引入课堂教学，提高课堂效率。
第四建立和谐融洽的师生关系
亲其师方可信其道，要提高教学质量就要拉近与学生的距离，做学生的朋友，建立平等融洽的师生关系。
和蔼可亲、平易近人就是我构建新型师生关系的法宝。
在教学工作中我注意营造宽松的课堂氛围，经常使用激励性的课堂语言，给学生提供尽量多的成功体验，既做学生的良师，更是学生的益友。
第五绝不让一个孩子掉队
作为教师，实实在在地讲，爱优生易爱差生难，这样就出现了优生受宠，差生被忽视、受歧视的现状，往往导致后进生因为长期得不到老师的肯定和表扬而自卑，产生厌学心理，甚至出现敌对情绪。
所以，在教学中我既爱“小天鹅”又爱”丑小鸭”，绝不让一个孩子掉队。
记得有一个星期一上午，我上课时，一名女生一直趴在桌子上，她的学习成绩本来很差，单元测验才D级，平时也不完成作业，看到她那不上进的样子我就上火，但是理智告诉我：简单粗暴是无能的表现，结果肯定事与愿违，于是我冷静下来。
下课后，我把她叫到办公室，动之以情晓之以理，经过一番谈心，她就哭着说出了原因，原来她是单亲家庭，返校时她的妈妈正打着吊瓶，她是挂念她妈妈。
我开导她说：“你牵挂父母证明你很孝顺，有爱心，懂得感恩，但孝敬父母的方式很多，你是妈妈的未来，她最大的愿望是想让你有出息！你只有刻苦学习，取得优异的成绩，她才高兴”。
听了这番话，她重重地点了点头。
从此她上课认真听讲，认真记笔记，课下及时完成作业，在暑假考试时她的历史成绩达到了A 级，其它各科也出现了明显的进步。
二、关爱每位学生——是我管理班级的出发点
高尔基说过：“谁不爱孩子，孩子就不爱他，只有爱孩子的人，他才可以教育孩子”。
关爱每位学生是班主任抓好班级的关键。
多年的班主任经历，使我深深地认识到：爱是教师最美丽的语言，爱是教育好孩子的根本。
如何把真诚的爱播洒到每位学生的心田，我是这样做的：
熟悉每一位学生，大到学生的学习、家庭、性格、爱好等，小到学生的字迹、口头禅。
身体力行、率先垂范。
只有用行为做出榜样，学生才会效仿，很多学生做完值日后把卫生工具随手一扔，横七竖八很不雅观，我发现后没有批评他们，而是自己把工具摆放整齐，一次、两次……渐渐地不用我动手，卫生工具就摆放的整整齐齐。
正可谓“话说百遍，不如手做一遍”。
严格要求学生。
俗话说得好：严师出高徒，“没有规矩，不成方圆”。
在班级管理中我的做法是严格要求、严中有爱。
例如：学生自己制定了《住校生就寝管理规定》、《上课十不准》、《失控时间自我要求》、《成功学生的品德》等规章制度，对照制度自我管理；对违规违纪的学生，无论是班干部，还是普通学生，无论是优秀生还是后进生我都一视同仁。
我对学生的严格要求是出于真诚的爱，严以爱为基础，爱以严为前提，严爱结合。
寒来暑往，四季交替，不知不觉已近不惑之年。
我不禁感叹，十八年，我已把青春留在了三尺讲台，但我无怨无悔。
依然平凡的我，毕竟没有碌碌无为。
今后我将更加勤奋，培养出更多人才，为建设美好徂徕、和谐徂徕献出微薄之力！
谢谢大家！。