Data swapping Variations on a theme by dalenius and reiss

Data Swapping:

Variations on a Theme by Dalenius and Reiss

Stephen E.Fienberg1, and Julie McIntyre2

1Department of Statistics

Center for Automated Learning and Discovery

Center for Computer Communications and Security

Carnegie Mellon University,Pittsburgh,PA15213-3890,USA

fienberg@https://www.doczj.com/doc/2518353892.html,

2Department of Statistics

Carnegie Mellon University,Pittsburgh PA15213-3890,USA

julie@https://www.doczj.com/doc/2518353892.html,

Abstract.Data swapping,a term introduced in1978by Dalenius and

Reiss for a new method of statistical disclosure protection in con?dential

data bases,has taken on new meanings and been linked to new statistical

methodologies over the intervening twenty-?ve years.This paper revis-

its the original(1982)published version of the the Dalenius-Reiss data

swapping paper and then traces the developments of statistical disclo-

sure limitation methods that can be thought of as rooted in the original

concept.The emphasis here,as in the original contribution,is on both

disclosure protection and the release of statistically usable data bases.

Keywords:Bounds table cell entries;Constrained perturbation;Con-

tingency tables;Marginal releases;Minimal su?cient statistics;Rank

swapping.

1Introduction

Data swapping was?rst proposed by Tore Dalenius and Steven Reiss(1978) as a method for preserving con?dentiality in data sets that contain categori-cal variables.The basic idea behind the method is to transform a database by exchanging values of sensitive variables among individual records.Records are exchanged in such a way to maintain lower-order frequency counts or marginals. Such a transformation both protects con?dentiality by introducing uncertainty about sensitive data values and maintains statistical inferences by preserving certain summary statistics of the data.In this paper,we examine the in?uence of data swapping on the growing?eld of statistical disclosure limitation.

Concerns over maintaining con?dentiality in public-use data sets have in-creased since the introduction of data swapping,as has access to large,comput-erized databases.When Dalenius and Reiss?rst proposed data swapping,it was in many ways a unique approach the problem of providing quality data to users Currently Visiting Researcher at CREST,INSEE,Paris,France.

J.Domingo-Ferrer and V.Torra(Eds.):PSD2004,LNCS3050,pp.14–29,2004.

c Springer-Verlag Berlin Heidelberg2004

Data Swapping:Variations on a Theme by Dalenius and Reiss15 while protecting the identities of subjects.At the time most of the approaches to disclosure protection had essentially no formal statistical content,e.g.,see the1978report of the Federal Committee on Statistical Methodology,FCSM (1978),for which Dalenius served as as a consultant.

Although the original procedure was little-used in practice,the basic idea and the formulation of the problem have had an undeniable in?uence on subse-quent methods.Dalenius and Reiss were the?rst to cast disclosure limitation ?rmly as a statistical problem.Following Dalenius(1977),Dalenius and Reiss de?ne disclosure limitation probabilistically.They argue that the release of data is justi?ed if one can show that the probability of any individual’s data being compromised is appropriately small.They also express a concern regarding the usefulness of data altered by disclosure limitation methods by focusing on the type and amount of distortion introduced in the data.By construction,data swapping preserves lower order marginal totals and thus has no impact on in-ferences that derive from these statistics.

The current literature on disclosure limitation is highly varied and combines the e?orts of computer scientists,o?cial statisticians,social scientists,and statis-ticians.The methodologies employed in practice are often ad hoc,and there are only a limited number of e?orts to develop systematic and defensible approaches for disclosure limitation(e.g.,see FCSM,1994;and Doyle et al.,2001).Among our objectives here are the identi?cation of connections and common elements among some of the prevailing methods and the provision of a critical discus-sion of their comparative e?ectiveness1.What we discovered in the process of preparing this review was that many of those who describe data swapping as a disclosure limitation method either misunderstood the Dalenius-Reiss arguments or attempt to generalize them in directions inconsistent with their original pre-sentation.

The paper is organized as follows.First,we examine the original proposal by Dalenius and Reiss for data swapping as a method for disclosure limitation, focusing on the formulation of the problem as a statistical one.Second,we ex-amine the numerous variations and re?nements of data swapping that have been suggested since its initial appearance.Third,we discuss a variety of model-based methods for statistical disclosure limitation and illustrate that these have basic connections to data swapping.

2Overview of Data Swapping

Dalenius and Reiss originally presented data swapping as a method for disclosure limitation for databases containing categorical variables,i.e.,for contingency tables.The method calls for swapping the values of sensitive variables among records in such a way that the t-order frequency counts,i.e.,entries in the the 1The impetus for this review was a presentation delivered at a memorial session for Tore Dalenius at the2003Joint Statistical Meetings in San Franciso,California.Tore Dalenius made notable contributions to statistics in the areas of survey sampling and con?dentiality.In addition to the papers we discuss here,we especially recommend Dalenius(1977,1988)to the interested reader.

16Stephen E.Fienberg and Julie McIntyre

t-way marginal table,are preserved.Such a transformed database is said to be t-order equivalent to the original database.

The justi?cation for data swapping rests on the existence of su?cient num-bers of t-order equivalent databases to introduce uncertainty about the true values of sensitive variables.Dalenius and Reiss assert that any value of a sensi-tive variable is protected from compromise if there is at least one other database or table,t-order equivalent to the original one,that assigns it a di?erent value.It follows that an entire database or contingency table is protected if the values of sensitive variables are protected for each individual.The following simple exam-ple demonstrates how data swaps can preserve second-order frequency counts. Example:Table1contains data for three variables for seven individuals.Sup-pose variable X is sensitive and we cannot release the original data.In particular, notice that record number5is unique and is certainly at risk for disclosure from release of the three-way tabulated data.However,is it safe to release the two-way marginal tables?

Table1b shows the table after a data-swapping transformation.Values of X were swapped between records1and5and between records4and7.When we display the data in tabular form as in Table2,we see that the two-way marginal tables have not changed from the original data.Summing over any dimension results in the same2-way totals for the swapped data as for the original data. Thus,there are at least two data bases that could have generated the same set of two-way tables.The data for any single individual cannot be determined with certainty from the release of this information alone.

Table1.Swapping X values for two pairs of records in a3-variable hypothetical example

(a)Original Data Record X Y Z 1010

2010

3001

4001

5111

6100

7100(b)Swapped Data Record X Y Z 1110

2010

3001

4101

5011

6100

7000

An important distinction arises concerning the form in which data are re-leased.Releasing the transformed data set as microdata clearly requires that enough data are swapped to introduce su?cient uncertainty about the true val-ues of individuals’data.In simple cases such as the example in Table1above, appropriate data swaps,if they exist,can be identi?ed by trial and error.However identifying such swaps in larger data sets is di?cult.An alternative is to release the data in tabulated form.All marginal tables up to order t are unchanged by the transformation.Thus,tabulated data can be released by showing the exis-tence of appropriate swaps without actually identifying them.Schl¨o rer(1981)

Data Swapping:Variations on a Theme by Dalenius and Reiss17 Table2.Tabular versions of original and swapped data from Table1

(a)Original Data

Z

Y X01 002 120

1

Y

X01

020

101

(a)Swapped Data

Z

Y

X01

011

111

1

Y

X01

011

110

discusses some the trade-o?s between the two approaches and we return to this issue later in the context of extensions to data swappping.

Dalenius and Reiss developed a formal theoretical framework for data swap-ping upon which to evaluate its use as a method for protecting con?dentiality. They focus primarily on the release of data in the form of2-way marginal to-tals.They present theorems and proofs that seek to determine conditions on the number of individuals,variables,and the minimum cell counts under which data swapping can be used to justify the release of data in this form.They argue that release is justi?ed by the existence of enough2-order equivalent databases or tables to ensure that every value of every sensitive variable is protected with high probability.

In the next section we discuss some of the main theoretical results presented in the paper.Many of the details and proofs in the original text are unclear,and we do not attempt to verify or replace them.Most important for our discussion is the statistical formulation of the problem.It is the probabilistic concept of disclosure and the maintenence of certain statistical summaries that has proved in?uential in the?eld.

2.1Theoretical Justi?cation for Data Swapping

Consider a database in the form of an N×V matrix,where N is the number of individuals and V is the number of variables.Suppose that each of the V variables is categorical with r≥2categories.Further de?ne parameters a i,i≥1,that describe lower bounds on the marginal counts.Speci?cally,a i=N/m i where m i is the minimum count in the i-way marginal table.

Dalenius and Reiss consider the release of tabulated data in the form of2-way marginal tables.In their?rst result,they consider swapping values of a single variable among a random selection of k individuals.They then claim that the probability that the swap will result in a2-equivalent database is

p≈

r(V?1)r (πk)(V?1)(r?1)

.

Observations:

1.The proof of this result assumes that only1variable is sensitive.

2.The proof also assumes that variables are independent.Their justi?cation

is:“each pair of categories will have a large overlap with respect to k.”

18Stephen E.Fienberg and Julie McIntyre

But the speci?c form of independence is left vague.The2-way margins for X are in fact the minimal su?cient statistics for the model of conditional independence of the other variables given X(for further details,see Bishop, Fienberg,and Holland,1975).

Dalenius and Reiss go on to present results that quantify the number of potential swaps that involve k individuals.Conditions on V,N,and a2follow that ensure the safety of data released as2-order statistics.However the role of k in the discussion of safety for tabulated data is unclear.First they let k=V to get a bound on the expected number of data swaps.The?rst main result is:

Theorem1.If V

4a1F1/(V?1)V(V r?r+1)/(V?1)

for some function F then the expected number of possible data-swaps of k=V individuals involving a?xed variable is≥F.

Unfortunately,no detail or explaination is given about the function F.Condi-tions on V,N,and a2that ensure the safety of data in2-way marginal tables are stated in the following theorem:

Theorem2.If V

N

{log(5NV p?)}2/(V?1)

≥a1V(V r?r+1)/(V?1)

where p?=log(1?p)/log(p),then,with probability p,every value in the database is2-safe.

Observations:

1.The proof depends on the previous result that puts a lower bound on the

expected number of data swaps involving k=V individuals.Thus the result is not about releasing all2-way marginal tables but only those involving a specic variable,e.g.,X.

2.The lower bound is a function F,but no discussion of F is provided.

In reading this part of the paper and examining the key results,we noted that Dalenius and Reiss do not actually swap data.They only ask about possible data swaps.Their sole purpose appears to have been to provide a framework for evaluating the likelihood of disclosure.

In part,the reason for focusing on the release of tabulated data is that identi-fying suitable data swaps in large databases is di?cult.Dalenius and Reiss do ad-dress the use of data swapping for release of microdata involving non-categorical data.Here,it is clear that a database must be transformed by swapping before it can safely be released;however,the problem of identifying enough swaps to protect every value in the data base turns out to be computationally impractical.

A compromise,wherein data swapping is performed so that t-order frequency counts are approximately preserved,is suggested as a more feasible approach. Reiss(1984)gives this problem extensive treatment and we discuss it in more detail in the next section.

Data Swapping:Variations on a Theme by Dalenius and Reiss19 We need to emphasize that we have been unable to verify the theoretical results presented in the paper,although they appear to be more specialized that the exposition suggests,e.g.,being based on a subset of2-way marginals and not on all2-way marginals.This should not be surprising to those faminiliar with the theory of log-linear models for contingency tables,since the cell probabilities for the no2nd-order interaction model involving the2-way margins does not have an explicit functional representation(e.g.,see Bishop,Fienberg,and Holland,1975). For similar reasons the extension of these results to orders greater than2is far from straightforward,and may involve only marginals that specify decomposable log-linear models(c.f.,Dobra and Fienberg,2000).

Nevertheless,we?nd much in the authors’formulation of the disclosure limi-tation problem that is important and interesting,and that has proved in?uential in later theoretical developments.We summarize these below.

1.The concept of disclosure is probabilistic and not absolute:

(a)Data release should be based on an assessment of the probability of the

occurrence of a disclosure,c.f.,Dalenius(1977).

(b)Implicit in this conception is the trade-o?between protection and util-

ity.Dalenius also discusses this in his1988Statistics Sweden monograph.

He notes that essentially there can be no release of information without some possibility of disclosure.It is in fact the responsibility of data man-agers to weigh the risks.Subjects/respondents providing data must also understand this concept of con?dentiality.

(c)Recent approaches rely on this trade-o?notion,e.g.,see Duncan,et al.

(2001)and the Risk-Utility frontiers in NISS web-data-swapping work (Gomatam,Karr,and Sanil,2004).

2.Data utility is de?ned statistically:

(a)The requirement to maintain a set of marginal totals places the emphasis

on statistical utility by preserving certain types of inferences.Although Dalenius and Reiss do not mention log-linear models,they are clearly focused on inferences that rely on t-way and lower order marginal totals.

They appear to have been the?rst to make this a clear priority.

(b)The preservation of certain summary statistics(at least approximately)

is a common feature among disclosure limitation techniques,although until recently there was little reference to the role these statistics have for inferences with regard to classes of statistical models.

We next discuss some of the immediate extensions by Delanius and Reiss to their original data swapping formulation and its principal initial application. Then we turn to what others have done with their ideas.

2.2Data Swapping for Microdata Releases

Two papers followed the original data swapping proposal and extended those methods.Reiss(1984)presented an approximate data swapping approach for the release of microdata from categorical databases that approximately preserves

20Stephen E.Fienberg and Julie McIntyre

t-order marginal totals.He computed relevant frequency tables from the original database,and then constructed a new database elementwise to be consistent with these tables.To do this he randomly selected the value of each element according to probability distribution derived from the original frequency tables and and then updated the table each time he generated a new element.

Reiss,Post,and Dalenius(1982)extended the original data swapping idea to the release of microdata?les containing continuous variables.For continu-ous data,they chose data swaps to maintain generalized moments of the data, e.g.,means,variances and covariances of the set of variables.As in the case of categorical data,?nding data swaps that provide adequate protection while pre-serving the exact statistics of the original database is impractical.They present an algorithm for approximately preserving generalized k th order moments for the case of k=2.

2.3Applying Data Swapping to Census Data Releases

The U.S.Census Bureau began using a variant of data swapping for data releases from the1990decennial census.Before implementation,the method was tested with extensive simulations,and the release of both tabulations and microdata was considered(for details,see Navarro,et al.(1988)and Gri?n et al.(1989)). The results were considered to be a success and essentially the same methodology was used for actual data releases.

Fienberg,et al.(1996)describe the speci?cs of this data swapping methodol-ogy and compare it against Dalenius and Reiss’proposal.In the Census Bureau’s version,records are swapped between census blocks for individuals or households that have been matched on a predetermined set of k variables.The(k+1)-way marginals involving the matching variables and census block totals are guaran-teed to remain the same;however,marginals for tables involving other variables are subject to change at any level of tabulation.But,as Willenborg and de Waal (2001)note,swapping a?ects the joint distribution of swapped variables,i.e, geography,and the variables not used for matching,possibly attenuating the association.One might aim to choose the matching variables to approximate conditional independence between the swapping variables and the others.

Because the swapping is done between blocks,this appears to be consistent with the goals of Dalenius and Reiss,at least as long as the released marginals are those tied to the swapping.Further,the method actually swaps a speci?ed (but unstated)number of records between census blocks,and this becomes a data base from which marginals are released.However the release of margins that have been altered by swapping suggests that the approach goes beyond the justi?cation in Dalenius and Reiss.

Interestingly,the Census Bureau description of their data swapping methods makes little or no reference to Dalenius and Reiss’s results,especially with regard to protection.As for ultility,the Bureau focuses on achieving the calculation of summary statistics in released margins other than those left unchanged by swapping(e.g.,correlation coe?cients)rather than on inferences with regard to the full cross-classi?cation.

Data Swapping:Variations on a Theme by Dalenius and Reiss21 Procedures for the U.S.2000decennial census were similar,although with modi?cations(Zayatz2002).In particular,unique records that were at more risk of disclosure were targeted to be involved in swaps.While the details of the approach remain unclear,the O?ce of National Statistics in the United Kingdom has also applied data swapping as part of its disclosure control procedures for the U.K.2001census releases(see ONS,2001).

3Variations on a Theme–Extensions and Alternatives

3.1Rank Swapping

Moore(1996)described and extended the rank-based proximity swapping algo-rithm suggested for ordinal data by Brian Greenberg in an1987unpublished manuscript.The algorithm?nds swaps for a continuous variable in such a way that swapped records are guaranteed to be within a speci?ed rank-distance of one another.It is reasonable to expect that multivariate statistics computed from data swapped with this algorithm will be less distorted than those computed af-ter an unconstrained swap.Moore attempts to provide rigorous justi?cation for this,as well as conditions on the rank-proximity between swapped records that will ensure that certain summary statistics are preserved within a speci?ed in-terval.The summary statistics considered are the means of subsets of a swapped variable and the correlation between two swapped variables.Moore makes a cru-cial assumption that values of a swapped variable are uniformly distributed on the interval between its bottom-coded and top-coded values,although few of those who have explored rank swapping have done so on data satisfying such an assumption.He also includes both simulations(e.g.,for skewed variables)and some theoretical results on the bias introduced by two independent swaps on the correlation coe?cient.

Domingo-Ferrer and Torra(2001a,2001b)use a simpli?ed version of rank swapping and in a series of simulations of microdata releases and claim that it provides superior performance among methods for masking continuous data. Trotinni(2003)critiques their performance measures and suggests great caution in interpreting their results.

Carlson and Salabasis(2002)also present a data-swapping technique based on ranks that is appropriate for continuous or ordinally scaled variables.Let X be such a variable and consider two databases containing independent samples of X and a second variable,Y Suppose that these databases,S1=[X1,Y1]and S2=[X2,Y2]are ranked with respect to X.Then for large sample sizes,the

corresponding ordered values of X1and X2should be approximately equal.The authors suggest swapping X1and X2to form the new databases,S?1=[X1,Y2] and S?2=[X2Y1].The same method can be used given only a single sample by randomly dividing the database into two equal parts,ranking and performing the swap,and then recombining.

Clearly this method,in either variation,maintains univariate moments of the data.Carlson and Salabasis’primary concern,however,is the e?ect of the data swap on the correlation between X and Y.They examine analytically

22Stephen E.Fienberg and Julie McIntyre

the case where X and Y are bivariate normal with correlation coe?cientρ, using theory of order statistics and?nd bounds onρ.The expected deterioration in the association between the swapped variables increases with the absolute magnitude ofρand decreases with sample size.They support these conclusions by simulations.

While this paper provides the?rst clear statistical description of data swap-ping in the general non-categorical situation,it has a number of shortcomings. In particular,Fienberg(2002)notes that:(1)the method is extremely waste-ful of the data,using1/2or1/3according to the variation chosen and thus is highly ine?ecient.Standard errors for swapped data are approximately40%to 90%higher than for the original unswapped data;(2)the simulations and theory apply only to bivariate correlation coe?cients and the impact of the swapping on regression coe?cients or partial correlation coe?cients is unclear.

3.2NISS Web-Based Data Swapping

Researchers at the National Institute of Statistical Science(NISS),working with a number of U.S.federal agencies,have developed a web-based tool to perform data swapping in databases of categorical variables.Given user-speci?ed param-eters such as the swap variables and the swap rate,i.e.,the proportion of records to be involved in swaps,this software produces a data set for release as micro-data.For each swapping variable,pairs of records are randomly selected and values for that variable exchanged if the records di?er on at least one of the unswapped attributes.This is performed iteratively until the designated num-ber of records have been swapped.The system is described in Gomatam,Karr, Chunhua,and Sanil(2003).Documentation and free downloadable versions of the software are available from the NISS web-page,https://www.doczj.com/doc/2518353892.html,.

Rather than aiming to preserve any speci?c set of statistics,the NISS pro-cedure focuses on the trade-o?between disclosure risk and data utility.Both risk and utility diminish as the number of swap variables and the swap rate increase.For example,a high swapping rate implies that data are well-protected from compromise,but also that their inferential properties are more likely to be distorted.Gomatam,Karr and Sanil(2004)formulate the problem of choosing optimal values for these parameters as a decision problem that can be viewed in terms of a risk-utility frontier.The risk-utility frontier identi?es the greatest amount of protection achievable for any set of swap variables and swap rate.

One can measure risk and utility in a variety of ways,e.g.,the proportion of unswapped records that fall into small-count cells(e.g.,with counts less than 3)in the tabulated,post-swapped data base.Gomatam and Karr(2003,2004) examine and compare several“distance measures”of the distortion in the joint distributions of categorical variables that occurs as a result of data swapping, including Hellinger distance,total variation distance,Cramer’s V,the contin-gency coe?cient C,and entropy.Gomatam,Karr,and Sanil(2004)consider a less general measures of utility—the distortion in inferences from a speci?c statistical analysis,such as a log-linear model analysis.

Data Swapping:Variations on a Theme by Dalenius and Reiss23 Given methods for measuring risk and utility,one can identify optimal re-leases are empirically by?rst generating a set of candidate releases by performing data swapping with a variety of swapping variables and rates and then measur-ing risk and utility on each of the candidate releases and provide a means of making comparisons.Those pairs that dominate in terms of having low risk and high utility comprise a risk-utility frontier that leads optimal swaps for allow-able levels of risk.Gomatam,Karr,and Sanil(2003,2004)provide a detailed discussion of choosing swap variables and swap rates for microdata releases of categorical variables.

3.3Data Swapping and Local Recoding

Takemura(2002)suggests a disclosure limitation procedure for microdata that combines data swapping and local recoding(similar to micro-aggregation).First, he identi?es groups of individuals in the database with similar records.Next,he proposes“obscuring”the values of sensitive variables either by swapping records among individuals within groups,or recoding the sensitive variables for the entire group.The method works for both continuous and categorical variables.

Takemura suggests using matching algorithms to identify and pair similar individuals for swapping,although other methods(clustering)could be used. The bulk of the paper discusses optimal methods for matching records,and in particular he focuses on the use of Edmond’s algorithm which represents individuals as nodes in a graph,linking the nodes with edges to which we attach weights,and then matches individuals by a weighting maximization algorithm. The swapping version of the method bears considerable resemblance to rank swapping,but the criterion for swapping varies across individuals.

3.4Data Shu?ing

Mulalidhar and Sarathy(2003a,2003b)report on their variation of data swap-ping which they label as data shu?ing,in which they propose to replace sensitive data by simulated data with similar distributional properties.In particular,sup-pose that X represents sensitive variables and S non-sensitive variables.Then they propose a two step approach:

–Generate new data Y to replace X by using the conditional distribution of X given S,f(X|S),so that f(X|S,Y)=f(X|S).Thus they claim that the released versions of the sensitive data,i.e.,Y,provide an intruder with no additional information about f(X|S).One of the problems is,of course,that

f is unknown and thus there is information in Y.

–Replace the rank order values of Y with those of X,as in rank swapping. They provide some simulation results that they argue show the superiority of their method over rank swapping in terms of data protection with little or no loss in the ability to do proper inferences in some simple bivariate and trivariate settings.

24Stephen E.Fienberg and Julie McIntyre

4Data Swapping and Model-Based Statistical Methods We can de?ne model-based methods in two ways:(a)methods that use a speci?c model to perturb or transform data to protect con?dentiality;or(b)methods that involve some perturbation or transformation to protect con?dentiality,but preserve minimal su?cient statistics for a speci?c model,thereby maintaining the data users’inferences under that model.The former is exempli?ed by post-randomization methodologies and the latter by work on the release of margins from contingency tables or perturbed tables from conditional distributions.We describe these brie?y in turn.

4.1Post Randomization Method–PRAM

The Post Randomization Method(PRAM)is a perturbation method for cate-gorical databases(Gouweleeuw,et al.,1998).Suppose that a sensitive variable has categories1,...,m.In PRAM,each value of the variable in the database is altered according to a prede?ned transition probability(Markov)matrix.That is,conditional on its observed value,each value of the variable is assigned one of 1,...,m.Thus,observations either remain the same or are changed to another possible value,all with known probability.This is essentially Warner’s(1965) method of randomized response but applied after the data are collected rather than before.Willenborg and de Waal(2001)note some earlier proposals of a similar nature and describe PRAM in a way that subsumes data swapping.

The degree of protection provided by PRAM depends on the probabilities in the transition matrix,as well as the frequencies of observations in the origi-nal database.PRAM has little e?ect on frequency tables.Given the transition matrix,it is straightforward to estimate the univarite frequencies of the original data,as well as the additional variance introduced by the method.The precise e?ect on more complicated analyses,such as regression models,can be di?cult to assess.See the related work in the computer science literature by Agrawal and Srikant(2000)and Ev?mievski,Gehrke,and Srikant(2003).

4.2Model-Based Approaches for the Release of Marginals

and Other Statistics

Fienberg,Steele,and Makov(1996,1998)suggest“bootstrap-like”sampling from the empirical distribution of the data,and then releasing the sampled data for analysis.Multiple replicates are required to assess the the added variability of es-timates when compared with the those that could be generated from the original data.In the case of categorical data,this procedure is closely related to the prob-lem of generating entries in a contingency table given a?xed set of marginals. Preserving marginal totals is equivalent to preserving su?cient statistics of cer-tain log-linear models.Diaconis and Sturmfels(1978)developed an algorithm for generating such tables using Gr¨o bner bases.Dobra(2003)shows that such bases correspond to simple data swaps of the sort used by Delanius and Reiss when the corresponding log-linear model is decomposable,e.g.,conditional independence

Data Swapping:Variations on a Theme by Dalenius and Reiss25 of a set of k?1variables given the remaining one in a k-way contingency table.

See Karr,Dobra,and Sanil(2003)for a web-based implementation.

The Dalenius and Reiss data swap preserves marginal totals of tables up

to order t,and so can be viewed as a model-based method with respect to a

log-linear model.In general,the set of tables that could be generated by data

swapping is a subset of those that could be generated by the Diaconis and Sturm-

fels algorithm because of non-simple basis elements required to generate the full

conditional distribution,e.g.,see Diaconis and Sturmfels(1998)and Fienberg,

Makov,Meyer,and Steele(2001).By comparison,the resampling method of

Domingo-Ferrer and Mateo-Sanz(1999)is not model-based and can only be

used to preserve a single margin.It has the further drawback of?xing sampling

zeros,thereby limiting its usefulness in large sparse contingency tables.

Burridge(2003)extended the approach in Fienberg,Makov and Steele,for

databases with continuous variables.Denote the database by(X,S),where X

represents sensitive variables that cannot be disclosed and S denote the remain-

ing variables.Let T be a minimal su?cient statistic for the distribution of X

given S.Values of the sensitive variables are replaced with a random sample,Y,

from the distribution of X given(T,S)and the database(Y,S)is released.The

idea is that the minimal su?cient statistic T will be preserved in the released

database.

Clearly this method makes strong assumptions about the distributional prop-

erties of the data.In the case of discrete variables where the distribution comes

from the exponential family,the results of Diaconis and Sturmfels apply again.

Burridge proposes the method speci?cally for the case where X|S is multivariate

normal with mean xβand covariance matrixΣ.He estimates su?cient statistics

by?tting a separate linear regression model to each column of X and construct-

ing the matrices?βand?Σ,and he describes methods for generating perturbed data Y that preserve the conditional mean and variance of X|S.He also dis-

cusses the level of protection realized by this procedure.how general the ap-

proach is and how it relates to the other kinds of data swapping objectives in

the non-categorical case remains to be seen.Note the similarity here to ideas

in Mulalidhar and Sarathy(2003a,2003b)but with a more formal statistical

justi?cation.

5Discussion

In this paper we have revisited the original work of Dalenius and Reiss on

data swapping and surveyed the some of the literature and applications it has

spawned.In particular,we have noted the importance of linking the idea of data

swapping to the release of marginals in a contingency table that are useful for

statistical analysis.This leads rather naturally to a consideration of log-linear

models for which marginal totals are minimal su?cient statistics.Although Dale-

nius and Reiss made no references to log-linear models,they appear in retrospect

to provide the justi?cation for much of the original paper.A key role in the rele-

vant theory is played by the conditional distribution of a log-linear model given

its marginal minimal su?cient statistics.

26Stephen E.Fienberg and Julie McIntyre

There is an intimate relationship between the calculation of bounds for cell entries in contingency tables given a set of released marginals(Dobra and Fien-berg,2000,2001)and the generation of tables from the exact distribution of a log-linear model given its minimal su?cient statistics marginals.Work by Aoki and Takemura(2003)and unpublished results of de Loera and Ohn e?ectively demonstrate the possibility that the existence of non-simple basis elements can yield multi-modal exact distributions or bounds for cells where there are gaps in realizable values.These results suggest that data swapping as originally pro-posed by Dalenius and Reiss does not generalize in ways that they thought.But the new mathematical and statistical tools should allow us to reconsider their work and evolve a statistically-based methodology consistent with their goals. Acknowledgments

The preparation of this paper was supported in part by National Science Founda-tion Grant No.EIA-0131884to the National Institute of Statistical Sciences and by the Centre de Recherche en Economie et Statistique of the Institut National de la Statistique et des′Etudes′Economiques,Paris,France.

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幼儿园大班语言教学案例

幼儿园大班语言教学案例《好饿的毛毛虫》 永清县养马庄中心小学王艳苓 教学目标： 1、引导幼儿感悟故事创意，获得阅读快乐，产生持续阅读的愿望，培养自主阅读能力。 2、引导幼儿尝试用图表的方式表达、迁移自己对作品的理解和想象，建立初步的读和写的信心，培养幼儿的书面表达能力。 活动准备： 1、故事录音《好饿的毛毛虫》。 2、毛毛虫吃过的实物图片一套，色彩标志与数字卡片一套。 3、《幼儿习得手册》下学期2册2——3页》。 活动一：多重阅读，初步认识作品 活动过程： 一、引题 （出示小毛毛虫图片）小朋友看，这是谁？（毛毛虫） 这只毛毛虫又瘦又小，它是一直好饿好饿的毛毛虫。小毛毛虫饿了，它会自己找吃的吗？它会吃什么？吃多少呢？小朋友们想不想知道呀？ 二、猜想故事内容 幼儿翻看《幼儿习得手册》，提醒幼儿从前往后仔细观察画面，猜猜故事内容。S三、幼儿讨论故事内容 幼儿自由结组，和同伴一起看图，讨论故事内容。 三、集体阅读 老师引导幼儿仔细观察画面，图上画了什么？会是什么意思呢？是这样吗？ 四、听故事录音 小朋友听故事录音，提醒幼儿边听边指相应的画面。 五、找对应画面 幼儿听故事指相对应画面。 六、讲故事 和幼儿一起看书，完整的讲一遍故事。 活动二理解作品，感悟故事创意 一、幼儿讲故事 请两名幼儿边指点画面，边讲故事内容。 二、分组讨论 讨论：从这个故事中，你发现了什么，或者认识了什么，学到了什么。老师参与到幼儿的讨论中去，倾听、鼓励、启发、指导。提醒幼儿把大家的发现及时地用图片或符号的形式记录下来。 三、集体讨论 各组代表发言，其他幼儿补充。同时，老师将幼儿发现按不同的线索，同于表的形式加以概括：1、从时间上说，故事从一个星期开始，到下一个星期日，又过了些天。 老师将故事发生的时间按顺序从上到下排成一列。 2、小毛毛虫的成长过程：一个小小的“蛋”——又小又饿的毛毛虫_-------………又肥又大的毛毛虫——茧——一只漂亮的蝴蝶。 老师可让一名幼儿到前面来，把相关的小图片按毛毛虫从小到大的顺序，从上到下对应

幼儿园大班绘本教案：你是特别的 你是最好的

活动名称：诗歌：你是最好的活动目标： 1、欣赏诗歌，理解诗歌内容，学习用轻松和有力的语言有表情地朗读诗歌。 2、感知诗歌画面，结合自己的生活经验，尝试用“ⅹⅹⅹ没关系”的句型方便诗歌。 3、积极参与游戏，乐意在集体面前大胆地讲述自己的长处，增强自信心。活动准备： 1、幼儿用书人手一册，实物展示仪一台。 2、幼儿对自己有一定的认识，知道自己的长处。活动过程： 一、击鼓传花：我喜欢我自己，…… 1、教师：你喜欢你自己吗？你能用“我喜欢我自己，……”讲述喜欢自己的理由。 2、介绍游戏规则：大家击鼓传花，当鼓声停止时，红花就在谁的上，谁就在集体面前用“我喜欢我自己，……”的句型夸奖自己的长处，然后继续听鼓声传花。 3、游戏2~3遍。 二、欣赏诗歌，初步理解故事内容。 1、教师：我们每个小朋友都喜欢自己，因为我们每个小朋友都是最好的，虽然有一点小问题，但是没关系。下面。老师给小朋友念一首诗歌《你是最好的》 2、教师有感情地朗诵诗歌。 3、教师：你听见诗歌里说了什么？ 三、展示诗歌画面，学习朗诵诗歌，并通过提问，感知理解诗歌。 1、引导幼儿观察幼儿用书诗歌画面，并请幼儿有表情地朗诵诗歌。 2、带领幼儿看图学习朗诵诗歌。 3、提问：为什么掉了一颗牙没关系？为什么个子太小了也没关系？ 4、教师：在我们说没关系的时候，我们可以用怎样的声音朗诵？ 5、教师带领幼儿有表情的朗诵诗歌。 四、采用多种方式念儿歌。 1、幼儿念诗歌的前四句，教师念最后一句。 2、请8为幼儿上台来，依次轮流念一句“没关系”全体幼儿念每段的最后一句。 五、启发幼儿想象并仿编诗歌。 1、教师：你觉得还有什么事没关系？ 2、教师整理幼儿的讲述，并抓住诗歌里面的关键词，在黑板上画出简笔画。 3、引导幼儿看图标有表情的朗诵新的诗歌《你是最好的》。 活动反思：击鼓传花这个游戏很适合幼儿，活动中幼儿很积极、开心，特别是在说“我喜欢我自己，……”这句话，很多幼儿充满了自信。 “你是特别的，你是最好的”，这句话说起来很简单，但对于大班的孩子来说真的是那么自信,还是连他们自己都不知道呢？简单的几幅画面，孩子们解读出的却很多。每一幅让我们期待，而读完又若有所思。笑中思索，思索后讨论，不同于以前的画册。每个孩子都能用自己的方式认识自身、认识世界。缺少了自信和个性，都是很令人遗憾的事情，而读完这首诗歌，我觉得对于我来说也有一定的意义，真希望平时每个人（包括孩子、保育员、老师等）时刻都能充满自信。在提问“为什么掉了一颗牙没关系？为什么个子太小了也没关系？”等问题时，很多幼儿很不能理解，后来经过一番解释后幼儿方才有点懂。

幼儿园大班绘本教案

幼儿园大班绘本教案：你是特别的你是最好的 活动目标： 1、欣赏诗歌，理解诗歌内容，学习用轻松和有力的语言有表情地朗读诗歌。 2、感知诗歌画面，结合自己的生活经验，尝试用“ⅹⅹⅹ没关系”的句型方便诗歌。 3、积极参与游戏，乐意在集体面前大胆地讲述自己的长处，增强自信心。 活动准备： 1、幼儿用书人手一册，实物展示仪一台。 2、幼儿对自己有一定的认识，知道自己的长处。 活动过程： 一、击鼓传花：我喜欢我自己，…… 1、教师：你喜欢你自己吗？你能用“我喜欢我自己，……”讲述喜欢自己的理由。 2、介绍游戏规则：大家击鼓传花，当鼓声停止时，红花就在谁的上，谁就在集体面前用“我喜欢我自己，……”的句型夸奖自己的长处，然后继续听鼓声传花。 3、游戏2~3遍。 二、欣赏诗歌，初步理解故事内容。 1、教师：我们每个小朋友都喜欢自己，因为我们每个小朋友都是最好的，虽然有一点小问题，但是没关系。下面。老师给小朋友念一首诗歌《你是最好的》 2、教师有感情地朗诵诗歌。 3、教师：你听见诗歌里说了什么？ 三、展示诗歌画面，学习朗诵诗歌，并通过提问，感知理解诗歌。 1、引导幼儿观察幼儿用书诗歌画面，并请幼儿有表情地朗诵诗歌。 2、带领幼儿看图学习朗诵诗歌。 3、提问：为什么掉了一颗牙没关系？为什么个子太小了也没关系？ 4、教师：在我们说没关系的时候，我们可以用怎样的声音朗诵？ 5、教师带领幼儿有表情的朗诵诗歌。 四、采用多种方式念儿歌。 1、幼儿念诗歌的前四句，教师念最后一句。

2、请8为幼儿上台来，依次轮流念一句“没关系”全体幼儿念每段的最后一句。 五、启发幼儿想象并仿编诗歌。 1、教师：你觉得还有什么事没关系？ 引导幼儿把它画下来，并仿编一句诗句。 2、教师整理幼儿的讲述，并抓住诗歌里面的关键词，在黑板上画出简笔画。 3、引导幼儿看图标有表情的朗诵新的诗歌《你是最好的》。 活动反思： 击鼓传花这个游戏很适合幼儿，活动中幼儿很积极、开心，特别是在说“我喜欢我自己，……”这句话，很多幼儿充满了自信。 “你是特别的，你是最好的”，这句话说起来很简单，但对于大班的孩子来说真的是那么自信,还是连他们自己都不知道呢？简单的几幅画面，孩子们解读出的却很多。每一幅让我们期待，而读完又若有所思。笑中思索，思索后讨论，不同于以前的画册。每个孩子都能用自己的方式认识自身、认识世界。缺少了自信和个性，都是很令人遗憾的事情，而读完这首诗歌，我觉得对于我来说也有一定的意义，真希望平时每个人（包括孩子、保育员、老师等）时刻都能充满自信。在提问“为什么掉了一颗牙没关系？为什么个子太小了也没关系？”等问题时，很多幼儿很不能理解，后来经过一番解释后幼儿方才有点懂。 大班语言：你是特别的，你是最好的 活动目标： 1、感知诗歌画面内容，尝试用语言和图画大胆的表达表现。 2、用自我欣赏的眼光，发现自己与众不同，分享自己的特别之处。 活动准备： 1、自制《我的书》诗歌ppt 3、视频笔和纸音乐 设计思路： 《纲要》中指出：要建构后继学习及终身发展的基础，培养好奇探究，勇敢自信的面向21世纪的儿童。结合大班幼儿的年龄特点，我选择了《你是特别的，你是最好的》这个绘本，意在透过言简意赅的文字与生动的图画，让孩子学会用自

幼儿园大班绘本故事教案

幼儿园大班绘本故事教 案 -CAL-FENGHAI-(2020YEAR-YICAI)_JINGBIAN

幼儿园大班绘本故事教案：老鼠娶新娘 活动目标： 1.理解故事内容，知道故事含义，明白任何事务、人物都不是完美的，是有缺点的。 2.喜欢自己的长处和别人的长处，承认自己的短处，学习取长补短。 3.体验婚嫁带来的喜悦气氛和抬轿子游戏带来的乐趣。 活动准备： 1.欢庆音乐一段。 2.《老鼠娶新娘》系列图画。 3.故事背景音乐一段。 4.汉字卡片：太阳------照；乌云------遮；风------吹；高墙------挡；老鼠--------打洞；猫-------抓； 取长补短 活动过程： 1、导入： (1)今天，我给大家带来了一段音乐，你来听听看在这段音乐里人们会在做些什么事？ 幼儿讨论（高兴的事、结婚）都是高兴的事情，今天老鼠村也发生了一件高兴的事情！ (2)_出示图片：花轿 提问：什么时候会坐轿子？今天老鼠美叮当也坐上欧陆花轿，当了新娘。 2、老鼠娶新娘 (1)美叮当要出嫁了，她要找一个世界上最强的新郎（出示循环图）她找到了太阳、云、风、高墙、老鼠小阿郎、猫。你们觉得他们中间谁是最强大的新郎呢为什么 (2)美叮当到底会嫁给谁呢？我们来听听故事。 讲故事（边讲边演示图片，故事背景音乐轻轻响起） 提问：你觉得在这个故事里谁是最强的新郎呢他有什么本领幼儿讲到谁就出示子卡。 小结：他们都有自己最强的地方，分别是……，但是没有人是最强的。

3.最强的你： 小朋友你们有最强的地方吗我们把最强的地方叫做长处，你知道自己的长处是什么吗每个人都有长处，有长处，可真好，因为长处会让我们很棒。 4.不强的你： 每个人都有自己最强的地方，但每个人也有不够强的地方，我们把不强的地方叫做短处，你知道你的短处是什么吗？请2—3个幼儿回答。你们能够知道自己的短处，真好，因为只有发现自己的不足，才能够进步！ 5.朋友圈： 我们都有长处和短处，今天老师带你们来玩一个朋友圈的游戏（用你的长处去帮助别人，你的短处请别人来帮助你，这就是取长补短）出示子卡。 小结：每个人都有自己的长处和短处，当我们取长补短，互相帮助时，就会变得很强大。 6.美叮当的新郎。 世界上没有最强的人，那美叮当到底该找谁当新郎呢（可提示：找不到最强的，但可以找最喜欢的，谁最喜欢她呢）美叮当嫁给了老鼠小阿郎，他们结婚了！看图片（结婚音乐起） 7.游戏：《抬花轿》 美叮当坐着花轿结婚了，我们也来玩抬花轿的游戏。 游戏开始：选一个女孩子来当新娘，新娘抛绣球选新郎！请2个男生来抬花轿，迎亲队伍出发了！ 推荐理由：我推荐此活动的理由是: 1、有效提问，让孩子正确的评价自己的能力和客观困难。 自信是确立自己能力，有把握去完成所承担的任务，敢于追求目标的情感体验。《老鼠娶新娘》，原本是一个带有浓浓气息的绘本故事，经过编者对教材的挖掘和设计，巧妙的寻找到了切入点，抓住绘本的中心思想及其精髓，通过几个有效提问，把“每个人都有自己的强项和弱项”的人性特点，通过这次教学活动让幼儿理解，让幼儿自豪的找出自己的强项。 2、积极合作，真诚欣赏他人的强项。 自信心强的孩子能在新的活动任务前不胆怯，能主动参加；讨论时能大胆发表意见，不轻易改变主意。活动中通过“抬花轿”这个游戏，让幼儿尝试与同伴积极合作，共同组队、讨论游戏的形式，提供了让幼儿理解人与人之间和谐共处的教育平台。

幼儿园大班绘本教案幼儿园绘本教案

幼儿园大班绘本教案幼儿园绘本教案 培养幼儿的观察能力和大胆的表现能力。在理解故事内容的基础上，大胆表现故事中的拟声词，感受故事的童真童趣。以下是精心的幼儿园绘本教案的相关资料，希望对你有帮助! 《好饿的小蛇》 活动名称：绘本《好饿的小蛇》 活动目标： 1.激发幼儿喜欢阅读的兴趣。 2.培养幼儿的观察能力和大胆的表现能力。 3.在理解故事内容的基础上，大胆表现故事中的拟声词，感受故事的童真童趣。 重难点分析： 重点：在理解故事内容的基础上，大胆表现故事对话。

难点：喜欢阅读，感受故事的童真童趣。 活动准备：《好饿的小蛇》绘本书、故事课件等 活动过程： 一、导入 教师出示绘本《好饿的小蛇》引导幼儿观察图书封面 提问：封面上有什么?小蛇饿了，它会找什么吃呢?会发生一件什么有意思的事情? @_@我是分割线@_@二、展开 1.出示小蛇吃东西的图片，引导幼儿进行猜测。 提问：请你猜一猜小蛇肚子里吃了什么?(引导幼儿发挥想象力大胆猜测) 2.教师根据课件生动的讲述故事

指导语：让我们一起完整的听一听故事，看看小蛇是究竟吃到了什么好东西。 提问：故事的名字叫什么?小蛇都找到了些什么好吃的东西? 总结：苹果是圆圆的、红色的;香蕉是长长的、黄色的;饭团是三角形的;葡萄是一串一串的、紫色的;菠萝是带刺的。 3.教师带领幼儿一起学小蛇吃东西的样子。 双手分开表示小蛇的嘴巴，生动的表情表现“啊呜”和“咕嘟”这两个拟声词。 4.教师第二遍完整的讲述故事 (1)教师和幼儿共同分享图画书《好饿的小蛇》 (2)教师讲故事，幼儿进行大胆表演。 三、结束

讨论：最后小蛇会怎样?会发生什么事情呢?引导幼儿进行观察后环衬和封底。 小结：小蛇吃饱了在呼呼呼的睡觉呢。 我的幸运一天 【设计意图】绘本讲述的是一个小猪误闯了狐狸家，小猪在危险时刻，沉着冷静，用自己的智慧逃离险境，使贪婪狐狸幸运的一天竟变成了小猪幸运的一天。大班幼儿具有初步的推理、表达能力，在活动中，运用启发式语言引导幼儿看看、想想、猜猜、说说，大胆推测故事情节，表达自己的想法，用动静结合来体验绘本学习的宽松氛围和乐趣，懂得在生活中提高安全意识，遇到危险和突发事件时，不要慌张害怕，要勇敢面对，用自己的智慧战胜敌人。 【活动目标】 1、通过猜猜、想想、说说等方式理解绘本内容，大胆表达自己的想法。 2、感受小猪如何使危险变为幸运的机智，知道在遇到危险时沉着、冷静，用智慧战胜敌人。

幼儿园大班绘本教案带反思——我爸爸

幼儿园大班绘本教案带反思：我爸爸 设计意图： 爸爸是幼儿非常熟悉和亲近的人。绘本《我爸爸》以孩子的口吻描写了一位高大、温柔的父亲形象，他样样事情都能干，温暖得像太阳。绘本中的爸爸开始被比喻成各种动物形象，最后作者突然笔锋一转，抒发了对爸爸的深深爱意。这是个幽默十足又感人至深的故事，让人久久不能忘怀。在本活动设计中，我首先让幼儿结合自己的生活经验谈论自己的爸爸，诸如他们的职业和爱好；再通过分段欣赏，引导幼儿理解绘本中布朗爸爸的高大形象，并尝试用“像……一样”的语句来赞美自己的爸爸；最后通过观看自己和爸爸的照片，让幼儿了解爸爸为自己做了很多事情，从而萌发对自己爸爸深深的爱。 目标： 1 理解故事内容，感受布朗爸爸真的很棒。 2 尝试用“像……一样”的句式夸夸自己的爸爸。 3 感受布朗父子间浓浓的情意，萌发爱自己爸爸的情感。 准备： 1 经验准备： 了解爸爸的职业、爱好。

2 材料准备： (1)幼儿收集爸爸带自己出去玩拍下的照片，教师将这些照片做成相册“大手牵小手，心会跟爱一起走”。 (2)完整的绘本《我爸爸》，以及从中节选9页编成的分段式绘本。 3 邀请幼儿的爸爸来参与活动。 过程： 一、谈谈自己爸爸的职业和爱好 师：每个人都有爸爸。谁愿意来介绍一下自己的爸爸是做什么工作的，有什么爱好? (幼儿自由介绍，教师加以提炼：威武勇敢的警察爸爸，享受美味的美食家爸爸，厨艺高超的厨师爸爸，喜欢爬山的爱运动爸爸，善于传授知识并且爱学习的教授爸爸，等等。) 师：你们的爸爸从事着不同的职业，都在努力工作，他们爱学习，爱运动，每个爸爸都与众不同。你们的爸爸真了不起! 二、分段欣赏绘本《我爸爸》 (一)欣赏绘本第一部分，认识布朗的爸爸。 师：今天，我请来了一位外国小朋友的爸爸，他是布朗的爸爸。我们一起来认识一下。 师(出示布朗爸爸的图画)：你们看到的布朗爸爸是什

幼儿园大班语言教案：小猫的故事

教学资料参考范本 幼儿园大班语言教案：小猫的故事 撰写人：__________________ 部门：__________________ 时间：__________________

活动目的 1.启发幼儿在观察小猫图片的基础上，大胆地进行想象，创编小 猫的故事，并运用粘、剪、画的技能绘制连环画。 2.幼儿根据自己所编的《小猫的故事》，设计制作成四幅连环画。画面要求色彩丰富、鲜艳，内容简单，有意义，并能讲出自己所画的 故事内容。 3.培养幼儿想象力、创造力，发展幼儿的口语表达能力。 活动准备 1.连环画纸若干 2.八个不同动态，不同表情的线描小猫图样。 3.水彩笔若干，各色电光纸若干，胶水、剪刀、订书机等。 4.木偶小猫一只，连环画范样两幅。 活动过程 一、用木偶小猫表演形式引出课题。 1.木偶小猫：小朋友们好！我是小猫咪咪。今天，我给小朋友带 来的礼物是两张画，一张画的是我的故事，一张画是用彩色电光纸剪 贴成的小猫妙妙的故事（教师从小猫手中接过两幅连环画，并向幼儿 展示）。我的许多好朋友小花猫、小白猫、小黑猫都想请小朋友把它 们的故事编到连环画里。 教师：咪咪，我们小朋友都非常愿意给你帮忙。等小朋友们画好后，我就给你送去，好不好？ 木偶小猫："好！谢谢小朋友们，再见！" 二、教师出示、讲解范画。

1.教师出示范画（一），是用水彩笔绘制成的四幅连环画，请幼儿观看。教师讲解连环画内容：(1)小猫咪咪是一只非常玩皮的猫。一天早上，它从窗户里往屋外跳。一不留神，将猫爸爸放在窗台上的一盆花碰倒在地。(2)猫爸爸看见了，非常生气，气得胡子都撅了起来。(3)调皮的咪咪跳到家门口的树上，冲着猫爸爸做鬼脸。(4)咪咪认识到了自己的错误，将碰翻在地的花盆重新放回到了窗台上。 2. 教师出示范画（二），是用彩色电光纸剪贴而成的四幅连环画，请幼儿观察看，讲出连环画的故事内容：(1)一天中午，小猫妙妙正坐在树下玩耍。(2)忽然她看到前面小桥对岸，山羊老公公拄着拐仗正要过桥去。 (3)妙妙赶忙跑过桥去，扶着山羊公公过了桥。(4)山羊公公过了桥，高兴地对小猫妙妙说："你真是一个爱帮助别人的好孩子啊！" 三、教师给幼儿出示八只不同动态、不同表情的小猫范样，如图2，启发幼儿大胆想象，开启幼儿的思路，编出小猫的故事。 1.幼儿编故事时要有时间、地点、人物及发生的事情的内容。 2.教师请幼儿将自己编的故事，用四幅画面表现出来。 3.教师请几名幼儿将自己编的故事讲给大家听。故事讲完后，教师进行讲评，肯定最合理的故事情节，要求幼儿编的故事内容要有意义。 四、幼儿进行创作连环画练习，教师巡回指导。 1.教师将活动分成两组：一组用彩色水笔来绘制连环画；一组用彩色电光纸粘贴连环画。

幼儿园大班语言教案春天里的故事

幼儿园大班语言教案春天里的故事设计意图： 我们班的幼儿在回答问题时，大部分会较完整地表达， 但讲述的语言不够优美，情节性不强。所以我通过利用多媒体课 件特有的情感功能，让幼儿在看一看，选一选，讲一讲的过程中 进行编述活动，引导幼儿自由想象，加入适当的表情、语言来连 贯表达，使幼儿的想象力、创造力、表达力得到充分的发展。 活动目标： 1、引导幼儿运用连贯、完整、优美的语言表述出课件中 实物的内容、情节。 2、引导幼儿合理地选择背景、主角，鼓励幼儿大胆地运 用不同的音量、声调，有表情地进行讲述，体验讲述活动的快乐。 活动准备：自制课件 活动过程： 一、感知理解讲述对象。 春姑娘悄悄地送来了许多美丽的图片，我们一起来欣赏、观察一下。 1、逐一显示图1至图5，引导幼儿运用已有词汇进行描述。 参考语：这是柳树，这是一棵怎样的柳树呢? 2、逐一显示图6至图8，引导幼儿运用优美词汇组合成 一句或一段完整的话。

参考语：谁在放风筝?心情怎样?用一句完整的话来说一说。 二、引导幼儿选图，运用已有经验讲述。 全屏显示8张图片，再现讲述对象。 1、想不想用这些图片来编述成一个完整的故事? 2、我们先一起来讨论一下，该怎样编故事?要注意哪些 问题? A、完整：比如，什么时候?在什么地方?有谁?干什么?发 生了什么事?心情如何?结果怎样?要让人听明白是怎么一回事。 B、运用优美的词语讲述，要给人一种美的享受。 C、运用不同的音量、声调，有表情地讲述。 D、加上一个恰当的故事名字，围绕故事名字讲述。 采用自由式或结伴式先进行编述，再在集体面前个人讲述。 采用幼儿点评与对比点评进行评价，提高孩子讲述能力。 回家把自己选择的图片画在纸上，组合成一幅美丽的画，然后请爸爸妈妈帮助你，把你编的完整、优美的故事写在图画的 下面，带来让大家一起来分享。

幼儿园大班绘本教案：小黑鱼

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幼儿园大班绘本教案：快乐

幼儿园大班绘本教案：快乐 Picture book teaching plan of kindergarten large class: happine ss 编订：JinTai College

幼儿园大班绘本教案：快乐 前言：本文档根据题材书写内容要求展开，具有实践指导意义，适用于组织或个人。便于学习和使用，本文档下载后内容可按需编辑修改及打印。 教学目标 1.在阅读画面语言的基础上欣赏故事，感受故事表达的 温情。 2.理解故事角色对快乐的认识，能寻找并发现自己生活 中的快乐。 3.学习用“××的快乐是……”的句式完整地说出自己 或他人的快乐。 教学准备 1.将绘本《快乐是什么》制作成PPT （插入与画面相匹 配的音乐数段），微笑表情图，大字卡：“××的快乐就是……”、“快乐是什么？”。 2.事先向幼儿发放“快乐调查表”。 教学过程 1.谈话导入

师：（出示快乐的表情）看，这是什么表情？人的心情怎样就会微笑？ 师：当我们心情高兴、快乐时，就自然会在脸上流露出微笑。老师今天很开心，很快乐，因为我能和小朋友在一起。老师知道你们一定也有很多快乐的事情，你愿意大声说出来和大家一起分享吗？谁来说说，你什么时候很快乐？ 幼：我在做游戏的时候很快乐。 幼：画画时我很快乐。 师：小朋友都有自己的快乐，快乐真是一种美好的感觉。可是有一只小老鼠，（点击封面）它经常问妈妈：快乐是什么？（出示字卡）想知道鼠妈妈是怎么回答的吗？我们一起来看书吧。 自评：以笑的表情图导出“快乐”的主题，让幼儿结合自身经验说说自己的快乐，从而自然引出了《快乐是什么》这本图画书，又为阅读的开展积累了前期经验。 2.阅读感知 （1）师：（点击第一页）猜猜这是什么季节？你从哪里看出来的？（引导幼儿观察樱花盛开的景象。）

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幼儿园大班语言教案我是大班的小朋友 活动目标: 1、对诗歌内容感兴趣，激发做大班小朋友的自豪感. 2、初步学习有表情地朗诵诗歌. 活动准备; 课件-照片：拥抱的孩子，朋友之间的礼让，上课的时候。 活动过程： 一、导入 1.照片-拥抱，谈话引出课题 (1)你们看，这两个小朋友在干什么?(拥抱) 他们为什么要拥抱呢? (引导幼儿说一说，暑假小朋友很长时间没见面，见了面 很亲热) (2)暑假你们在家都做了哪些什么有意义的事呢? 我们都来说一说好吗? (鼓励幼儿大胆讲述) 二、展开 1.开学了，小朋友都长大了，成了一名大班的小朋友了。

有一首诗歌，名字是《我是大班的小朋友》，你们想听吗? 2.帮助幼儿欣赏并熟悉诗歌内容 (1)教师有感情的朗诵诗歌 这首诗歌叫什么名字? (我是大班的小朋友) 听了这首诗歌，你们心里有什么样的感觉? (让幼儿充分发表自己的见解，说一说自己的感受) (2)第二遍欣赏诗歌 你们再听一听， 诗歌里的小朋友帮助别人做了那些事? 你们最喜欢哪一句? 3.借助课件-照片，引导幼儿理解诗歌内容。 (1)成了大班的小朋友，应该怎样做呢? 诗歌里是怎样说的? 上课时是怎样做的? 帮助小弟弟小妹妹做了哪些事? (2)引导幼儿在教师的提示下练习念诗歌 4.教师和幼儿一起朗诵诗歌，激发幼儿产生做大班小朋友的自豪感。 三、结束

1.你们喜欢诗歌里的大班的小朋友吗?为什么? 2.你们现在也是大班的小朋友了，你们想怎样做大班的小朋友呢? 诗歌 我是大班的小朋友 开学了，我高高兴兴的来到幼儿园， 从现在起，我就是大班的小朋友了。 小弟弟，你听过老师讲课吗? 来!看我上课多认真，举手发言动脑筋。 小妹妹，你会穿衣服吗? 来!我帮你把衣服穿整齐; 小朋友，你会做玩具吗? 来!我折一个小纸球送给你。 老师老师您别夸奖我， 因为，我是大班的小朋友了。

(完整版)幼儿园大班绘本阅读教学设计

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看你认识它们吗？（出示剪刀）这是什么？（幼回答剪刀）剪刀是用来干什么的？（剪东西的）它又是什么？（出示蓝色布料）布料是用来干什么的？那你说一说布料可以做什么？（衣服、裤子、手绢等）哦，原来布料这么有用啊！ 2、今天老师还给你们带来了一本特别好看的图画书，这把剪刀和布料就藏在这本图画书里面，让我们来看看吧！ （二）引出故事，出示图画书 1、封面上都有谁？（一个白胡子老爷爷和一个小男孩） 2、那你猜一猜老爷爷和这个小男孩是什么关系呢？ 3、上面还有一行字，就是故事的名字，叫爷爷一定有办法，爷爷会有什么办法呢？他是怎么做的？我们一起来看一下。 （三）介绍扉页 我们再来看扉页，你有看到了什么？（许多小星星）它的背景是什么颜色的？（蓝色）对，这就是一块带小星星的蓝色布料，故事就从这块蓝色的布料开始了。 （四）播放课件，幼儿观看课件，回答问题 1、这个故事叫什么名字？ 2、约瑟小时候的小毯子是谁为他缝制的？ 3、约瑟渐渐地长大了，奇妙的蓝毯子变得怎样了？妈妈说了什

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8：艾玛在干什么？它会变成什么样子？ 幼儿一页一页专注的翻看，手指图边看边说。特别是学一学艾玛用鼻子卷树干摇果子，幼儿模仿角色动作，有的皱着眉头用劲的样子，有的摇头晃脑的样子……个个都很投入到角色中。 3、重点观察图9、11、12、14，观察艾玛和群像的神态细节，感受艾玛的调皮、幽默和快乐。 图9：艾玛涂上果汁变成灰色回到象群中，找一找哪个是艾玛？为什么？ 幼儿立即手指中间的一只大象叫起来：在那儿。 师：在哪儿？从哪里看出来的？ 幼1：其他大象都闭着眼睛，艾玛睁着眼睛四处看。 幼2：他翘着鼻子、抬着腿走路的样子，一下子看出来了……师：艾玛的动作、眼神看出他心里正高兴呢，有点得意、有点调皮的样子，大家学一学。 图11：艾玛回来很长时间了，其他大象什么表情？大象们认出艾玛了吗？艾玛在哪里？它又是什么表情？ 幼：大象还是闭着眼睛一动不动。幼：它们没认出来，艾玛高兴的笑了。 幼：至于艾玛是翘着鼻子，眼睛斜看大象。 师阅读原文文字，问：艾玛眼睛瞪大，卷起长鼻子笑起来，大家学一学图12：突然大象们怎么了？BOOO！什么意思？ 幼：耳朵竖起来，吓了一跳。 幼一边说一边做着眼神：大象被吓了一跳，瞪大眼睛，眼珠子都朝上翻。 大家听了哈哈笑起来，很自然的也学起大象吓了一跳，吃惊的样子，唯妙唯肖。 图14：下雨了，艾玛的花格子图案又出现了，大象们看到了是什么反应呢？每只大象笑的样子不一样，大家学一学。 幼儿：有的坐着，鼻子翘上天大笑；有的捂着嘴笑；有的背靠背笑；有的张大嘴笑……大家学动作把活动推向了高潮，孩子们尽情地学起大象们的动作、表情。

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2、小猪想了几个办法？第1个是什么？他是怎么说的？狐狸有没有听小猪的话呢？为什么会听小猪的话，按小猪说的去做呢？后来，狐狸便忙了起来，他干了什么事呢？（可引导幼儿看图） 3、当狐狸把小猪洗干净后，有没有吃到小猪呢？为什么？原来小猪又想了第2个办法，狐狸有没有听小猪的话？狐狸是怎么想的？狐狸又忙了起来，他干了什么事呢？（可引导幼儿看图） 4、当小猪吃完了丰盛的午餐后，这次狐狸有没有吃到小猪呢？为什么？原来，小猪又想了第3个办法，狐狸有没有听小猪的话呢？狐狸是怎么想的？狐狸是怎么给小猪按摩的呢？（引导幼儿看图）瞧，这时狐狸的头上已经冒出什么了？可是，小猪有没有让他停下呢？小猪说什么？最后，狐狸累的都怎样啦？（引导幼儿看图） 5、最后，狐狸有没有吃到小猪呢？小猪后来怎么样啦？（引导幼儿看图）他一边往家跑，一边笑眯眯地说了什么呢？为什么小猪说“这是我最幸运的一天呢”？ 6、你喜欢小猪吗？为什么？ ㈣尝试以故事中小猪的身份，大胆想出各种帮助自己逃脱危险的办法。 教：小猪在遇到危险时，一点也不惊慌，冷静下来，想出了那么多可以说服狐狸的办法，真是一个既勇敢又聪明的小猪！我要向他学习，你们呢？如果你是这只小猪，当你被狐狸抓住了，你会不会害怕和放弃呢？你会怎么办？你会想什么办法帮助自己逃脱危险呢？ ㈤回到生活中，迁移生活经验，使幼儿知道不管遇到什么危险或困难，都不要害怕和慌张，应立刻动脑筋，想办法，才能化险为安，解决困难。 幼儿园故事：七十二变 一天，孙悟空被儿子孙小圣缠上了："爸爸，听人说您会七十二变？" 悟空说："这不假，你爸爸样样都会变。" "教教我吧！" 悟空磨不过儿子，就教了他一招——变树。

幼儿园大班绘本的阅读教案

幼儿园大班绘本的阅读教案 作品分析： 《床底下》这本书自始至终都在以一种轻松、风趣、调侃的笔调描绘那些床底下的怪物，直到最后才将“镜头”从床底下拉到床上，推出那个大大的、具有震撼力的“你”——原来，正是这个作为人的“你”（即孩子），吓得床底下的妖物们“跌跌撞撞、失魂落魄”地逃窜！。“现在，床底下什么怪物也没有了。”全书到这儿戛然而止。如此轻描淡写的一笔，却既让人释怀，又令人回味。它其实是在含蓄地告诉孩子：你所惧怕的东西，恰恰是惧怕你的东西。战胜恐惧，说到底就是战胜自己——因为，那些床底下的妖物在现实生活中并不存在，它们都是你自己一手制造出来的。 每到午睡时候，在上床前，孩子们总爱趴到床下看看，在他们看来，床底下是一个非常神秘的地方，由此，我们利用孩子的这一兴趣点设计了《床底下》这一教学活动，目的在于满足孩子的好奇心，在游戏中培养幼儿的阅读兴趣，发展孩子的语言表达能力。 活动目标： 1、阅读故事，仔细阅读画面，帮助幼儿理解故事的内容。 2、通过故事让幼儿学会遇到困难要迎面而上，想办法克服困难。 活动准备： 绘本《床底下》PPT. 活动过程：

一、导入活动1.教师：小朋友，又到我们讲故事的时候了，开心吗？ 2.今天老师给你们带来了一个非常有趣的故事，我们一起来看一看好吗？ 二、阅读故事1.出示书的封面。 （1）教师：小朋友你们看，书的封面上有谁？他在干什么呢？（小男孩爬在床上朝床底下看）（2）教师：这本书的名字就叫做《床底下》。 （3）教师：床底下会有什么呢？谁来猜一猜？你们猜的对吗？我们继续往下看。 2.阅读第一页。 （1）教师：他的床底下究竟有什么东西呢？你看到了吗？ 教师讲述画面：床底下有一只臭烘烘的鞋子，一块蓝绿相间的拼图玩具，一个苹果核。。。。。。可是床底下还有一些别的东西呢！ （2）幼儿观察画面：（教师做嘘的动作）小男孩在干什么？(睡觉)他是独自一人睡觉，还是有人陪睡？（独自一人睡觉）他在睡觉之前做了一件什么事？你看出来了吗？（玩机器人玩具、看了一本书）（3）教师：那你猜猜他睡着了，容易干什么呢？（做噩梦）（4）教师：真的吗?他会梦见谁呢？我们一起来看。 3.阅读第二页。

幼儿园大班语言教案《我想…》含反思

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4.在感知故事内容的基础上，理解角色特点。 5.愿意分角色表演简单的故事情节。 教学重点、难点 重点：.理解儿歌的内容，感受儿歌的语言美，敢于在集体面前大胆地表达自己的想法 难点：学习用儿歌中的句式创编儿歌 活动准备 动物图片若干自制纸箱1个 活动过程 一：开始环节 观察动物图片，引出活动主题 1.请幼儿从纸箱中依次摸出一张动物图片，互相进行观察。 2.图片上的动物叫什么?它什么地方最特别?这些特别的地方能干什么? 3.假如你像动物一样有这些特别的地方，你想做什么?

4.有一首儿歌，写的就是刚才我们讲的事情，名字叫《我想…………》 二：基本环节 (一)引导幼儿欣赏儿歌，理解儿歌内容 1.教师有感情地朗诵儿歌，引导幼儿发现儿歌的排列句式 2.问：儿歌的题目是什么?有哪些词重复出现? (二)鼓励幼儿创编儿歌 1.如果你是小鸟(小兔小鱼)，你想做什么? 2.鼓励幼儿展开想象，为每段儿歌续编一句话，如：我想有对翅膀，我用翅膀飞翔。 (三)导幼儿根据儿歌句式创编新的儿歌 1.还喜欢什么小动物?你想变成什么?想做什么? 2.教师示范创编，如：我看见小鸭，我想有双鸭蹼，我用鸭蹼游到海里玩。 3.以4人一组合作创编一段儿歌。(讨论) 三：结束环节

幼儿园大班绘本教案：纸真好玩

幼儿园大班绘本教案：纸真好玩 一、设计意图《纸真好玩》是一本与小朋友平时所看的完全不一样的书，它不是给小朋友讲一个故事，而是一本告诉小朋友怎么和纸玩游戏的书。在这本书中，作者从易到难呈现了22中制作纸质玩具所需的材料、制作步骤和玩法，根据这些提示，小朋友动动自己的小手，通过撕、折、剪、贴、画等多种方式就可以做出这些好看又好玩的玩具。在这些活动中，幼儿需要调动自己的感官，充分运用手指的小肌肉，因此能有效地锻炼手眼协调能力，提高感觉统合的水平，促进大脑前庭的发育。 《纲要》中指出，要培养幼儿对生活中常见的简单标记和文字符号的兴趣，这本书不只是一本手工操作书，当幼儿阅读这些图示步骤时，无形中就学会了操作说明，通过阅读，幼儿能获得操作的乐趣，也发展了阅读的兴趣，将进一步促进幼儿阅读能力的发展，值得我们和孩子一起阅读。 二、活动设计（一）活动目标1、学习阅读科学类的工具书。 2、阅读中能留意各种材料的汉字和贴、粘、撕、折等动作的汉字。 3、体验根据文字与图谱步骤提示制作纸玩具的乐趣。 （二）活动准备1、教学大书《纸真好玩》，纸质玩具若干。 2、材料：卡纸、色纸、图画纸、皱纹纸等各种操作用纸。 3、彩色笔、白胶、胶水、剪刀、回形针、水彩笔。 4、可以张贴挂图的活动操作板。 （三）活动过程1、出示纸质玩具，引发幼儿对纸质玩具的兴趣与好奇☆你们知道这些玩具都是用什么材料做的吗？ ☆阅读绘本的封面。看看封面的照片和书名，猜猜书里会是什么？ 2、阅读绘本，了解制作玩具的方法步骤。 ☆选择一张制作纸质玩具的图片说说图上有些什么内容（了解图上有4个部分的内容）☆看看玩具是如何制作的我们要做的这个玩具，名称是什么？ 制作这个玩具需要什么工具？ 我们怎么制作这个玩具？

幼儿园大班语言教案(6篇)

（一）语言：故事——月亮姑娘做衣裳 活动目的： 1. 使幼儿知道月亮时圆时缺，引起幼儿观察月亮的兴趣。 2. 学习词：量、衣裳、可惜。 活动准备： 月亮不同时期的图片实物投影仪 活动过程： 1. 提问引起幼儿兴趣：在晴朗的晚上，天上回看见什么呢？（月亮、星星）月亮是什么形状的？ 2. 教：月亮姑娘她很爱美，一直都想穿上美丽的衣裳，可是怎么 也没办法，小朋友想不想知道是怎么回事呢？ 3. 教师讲述故事《月亮姑娘做衣裳》，边讲边出示图片。 4. 在讲完以后，请小朋友想一想，为什么月亮姑娘的衣服一直都 穿不上呢？（因为月亮每天都在变化，所以穿不上） 5. 复述故事，听完以后讨论：小朋友有什么方法可以让月亮姑娘 穿上美丽的衣裳呢？请小朋友开动脑筋想办法。 6. 活动延伸：可以给小朋友讲一讲月亮为什么会有阴晴圆缺，太 阳地球和月亮的关系又是什么样子的。（月亮其实是地球的一颗卫星，它围绕地球旋转，白天看不见它，因为太阳太亮了，晚上看不到太阳的时候才会看见它，它反射的是太阳光，有的时候地球的影子遮住了太阳光，所以我们看到的月亮时圆时缺。） （二）大班语言：月亮姑娘做衣裳 教学目标: 1. 理解故事内容,进一步了解月亮的变化过程,知道月亮每天都在变化. 2. 学习故事中优美的语言,并根据故事展开丰富的想象. 准备: 月亮变化图4张,衣裳图3张 纸,剪刀,胶水,蜡笔,记号笔等. 过程: 1. 谈话导入,引出月亮做衣裳的课题. (1) 以猜谜语的形式导入活动.

师:有时圆圆像个盘子,有时弯弯像只船,要问这个是什么?晚上抬头向天看.(月亮) (2)师:冬天到了,天气冷了,人们都穿上厚厚的衣服.月亮姑娘呀她也觉得很冷,想去做一件衣裳,那你们想想月亮姑娘该做什么样的衣裳呀? 幼儿讨论 2. 结合图片,分段欣赏故事. (1) 教师讲述第一段 提问:a﹑哎呀,为什么裁缝师傅给她做的衣裳会穿不上呢?(因为她长胖了一点,好象弯弯的镰刀) b﹑那该怎么办呀? 幼儿回答。 （2）教师讲述第二段。 提问：a、这回裁缝师傅给她重新做的衣裳她能穿上吗？（不能） b、唉！到底是怎么回事呀？（月亮姑娘又长胖了，弯弯的像小船） （3）教师讲述第三段。 提问：a、这回月亮姑娘能穿上新衣裳吗？（不能）为什么？（因为她又像一只圆盘子了） b、裁缝师傅会不会再给她做衣裳了？（不会，因为她的身材量不准） c、为什么她的身材会量不准？（因为她每天都在变化） 师：今天我们学的这个故事的名字就叫“月亮姑娘做衣裳”。那我们接下来再完整欣赏一遍故事。 3. 结合图片，完整欣赏故事。 提问：月亮姑娘是怎么变的？（引导幼儿学习月亮变化词句，如细细的、弯弯的像眉毛，好象弯弯的镰刀，弯弯的像小船，圆圆的像盘子。 4. 给月亮姑娘做衣裳。 （1）师：原来月亮姑娘每天都在变化着，平时呢我们小朋友也可以观察一下月亮的变化。现在月亮姑娘还没有穿到合身的衣裳，晚上出来她会冷的呀！如果请你来当一回裁缝师傅，你会给月亮姑娘做一件什么样的衣裳？幼儿回答 （2）幼儿制作衣裳，教师指导。 （3）请幼儿介绍自己制作的衣裳。 （三）语言：月亮姑娘做衣裳（大班） 目标： 1、理解故事内容，学习描述月亮变化的语句。 2、初步了解故事中比喻手法的运用。 准备： 自制夜晚天空的背景图，月亮变化图。 过程： 1、以猜谜语的形式导入活动。 “有时圆圆像个盘子，有时弯弯像只船，要问这个是什么？晚上抬头向天看。”