Metaphor Difficulty 1 In Press Metaphor and Symbol Send Correspondence to
In Press: Metaphor and Symbol
Metaphor comprehension:
What makes a metaphor difficult to understand?
Walter Kintsch & Anita R. Bowles
University of Colorado
Send Correspondence to:
Walter Kintsch
Department of Psychology
University of Colorado
Boulder, CO 80309-0345
wkintsch@https://www.sodocs.net/doc/e210638923.html,
303-492-8663
Abstract
Comprehension difficulty was rated for metaphors of the form Noun1-is-a-Noun2; in addition, participants completed frames of the form Noun1-is-________ with
their literal interpretation of the metaphor. Metaphor comprehension was simulated with a computational model based on Latent Semantic Analysis. The model matched participants’ interpretations for both easy and difficult metaphors. When interpreting easy metaphors, both the participants and the model generated highly consistent responses. When interpreting difficult metaphors, both the participants and the model generated disparate responses.
Key Words
metaphor
latent semantic analysis
predication
comprehension
metaphor comprehension
Metaphor comprehension:
What makes a metaphor difficult to understand?
There exists a considerable and convincing body of research in cognitive psychology and cognitive science that indicates that people understand metaphors in much the same way as they understand literal sentences (Cacciari &Glucksberg, 1994; Gibbs,1994, 2001; Glucksberg, 1998). Some metaphors are easier to understand than others, but the same can be said for literal sentences. On the whole, the view that understanding metaphors is a more complex process than understanding literal sentences is not supported by this body of research. In particular, it does not appear that metaphor comprehension first involves an attempt at literal comprehension, and when that fails, a metaphoric reinterpretation. Certainly, that is sometimes the case for complex, often literary metaphors, but most ordinary metaphors encountered in common speech and writing are simply understood without any need to figure them out. Some literal sentences, too, challenge comprehension and require a certain amount of problem solving for their comprehension. But most of the time the sentences that we hear and read are understood without deliberate reasoning, whether they are metaphorical or literal.
Of course, claiming that metaphorical sentences are understood in the same way as literal sentences does not tell us how either one is understood. Here, we describe a model of text comprehension (Kintsch, 1998, 2001) that attempts to specify the process of comprehension for both literal and metaphorical sentences, simulate the computations involved, and evaluate the model empirically.
A basic assumption of this model is that the meaning of a word, sentence, or text is given by the set of relationships between it and everything else that is known. This idea is operationalized in terms of a high-dimensional semantic space. Words, sentences, and texts are represented as vectors in this space; that is, meaning is a position in this huge semantic space, which is defined relative to all other positions that constitute this space. We thus represent meaning geometrically, i.e. mathematically, which means that we can calculate with meanings. For instance, we can readily calculate how close or far apart two vectors are in this semantic space – hence, the degree of semantic relationship between any words, sentences, or texts.
The technique that allows us to construct such a semantic space is Latent Semantic Analysis (LSA), as developed by Landauer and his coworkers (for introductions, see Landauer, 1998; Landauer & Dumais, 1997; Landauer, Foltz & Laham, 1998). A good way to form an intuition about LSA is to compare it with how people used to make maps (before satellite photographs): they collected a large number of observations about distances between various geographical landmarks and then put all these observations together in a two-dimensional map. Things will not fit perfectly because of measurement errors or missing information, but on the whole, it turns out that we can arrange all the geographical distances in a two-dimensional map, which is very useful because it allows us to calculate distances between points that were never measured directly. Note that if we want to make a map of the world, we will not be able to put all of our data into a two-dimensional map without severe distortions; we need three dimensions for this purpose. LSA constructs semantic spaces in an analogous way. The basic measurements are word co-occurrences. In the case of the semantic space used below, that means over 30,000 documents with over 90,000 different words for a total of about 11 million words. But what should be the dimensionality of the map that is to be constructed? If we employ too few dimensions (two or three, or even 100), the map will be too crude and cannot reflect the kind of semantic relations among words that people are sensitive to. Maps in too many dimensions are not very useful either, however. There is too much accidental, non-essential, even contradictory information in co-occurrence data, because which words are used with other words in any concrete, specific instance will depend on many factors, not just their meaning. We need to discard this excess and focus on the semantic essentials. It turns out, as an empirical fact, that semantic maps –spaces – of 300-400 dimensions yield results that are most closely aligned with human judgments.
LSA thus represents the meaning of a word as a vector in a 300-dimensional semantic space (that is, as a list of 300 numbers that are meaningful only in relation to the other vectors in that space). The meaning of a set of words can be represented as the centroid (vector sum) of the individual word vectors. Thus, sentence meanings are computed as the sum of the words, irrespective of their syntactic structure. Obviously, such a procedure neglects important, meaning-relevant information that is contained in
word order and syntax. In spite of this limitation, LSA has proven to be a powerful and useful tool for many purposes (see the references above). Nevertheless, the neglect of syntax is a serious limitation for LSA which is especially noticeable when we are dealing with short sentences.
The Predication Model of Kintsch (2001) was designed to overcome this limitation, at least for simple argument-predicate sentences. Specifically, the meaning of a predicate is modified to generate a contextually appropriate sense of the word. Consider
The stock market collapsed and The bridge collapsed.
The meaning of the predicate collapsed that is used here with two different arguments depends on its context: different aspects of collapse are foregrounded when the stock market collapses than when a bridge collapses. We say that collapse has more than one sense. (There are words, homonyms like bank, that have more than one meaning). The Predication Model generates context appropriate senses (or meanings) of a predicate by combining an LSA knowledge base with the construction-integration model of text understanding of Kintsch (1998). It modifies the LSA vector representing the predicate by combining it with features of its semantic neighborhood that are related to the argument of the predication. Specifically, it constructs the semantic neighborhood of the predicate (all the other vectors in the semantic space that are most closely related to the predicate) and then uses a constraint satisfaction process to integrate this neighborhood with the argument: stock market selects certain features from the neighborhood of collapse, while bridge selects different ones. The selected neighborhood vectors are then combined with the predicate vector to yield a context-sensitive sense of the predicate. A more detailed description of this model is given in Kintsch (2001) and the Appendix.
Generating context sensitive word senses does not always produce dramatic results. In the sentence
My lawyer is young
the meaning of young is not much modified by lawyer. This is different for metaphors. In
My lawyer is a shark
the meaning of the predicate is-a-shark is very different from shark in isolation – the fishy features of shark are de-emphasized (e.g., has-fins, swims), but they do not
disappear, while other features of shark (e.g., vicious, mean, aggressive) are weighted more strongly because they are somewhat lawyer-related, whereas has-fins is not.
Kintsch (2000) has shown that this predication algorithm yields interpretations of simple Noun-is-a-Noun metaphors that are in agreement with our intuitions about the meaning of metaphors by comparing the vector generated by the model with appropriate landmarks. The measure used for these comparisons is the cosine of the angle between respective vectors, which can be interpreted in much the same way as correlation coefficients. Thus, the cosine between highly similar vectors is close to +1, while unrelated vectors have a cosine close to 0. For example, surgeon is related to scalpel (cos=.29) but not to axe (cos=.05), while butcher is related to axe (cos=.37) but not to scalpel (cos=.01). My surgeon is a butcher moves surgeon closer to axe (cos=.42) in the semantic space and farther away from scalpel (cos=.10). Conversely, My butcher is a surgeon relates butcher to scalpel (cos=.25) and diminishes but does not obviate the relationship to axe (cos=.26). Examples like these demonstrate that the LSA space, together with the predication algorithm, represent the meaning of metaphors in a human-like way.
In a recent review, Gibbs (2001) compared several models of figurative language understanding. It is instructive to situate the present approach among current conceptions of metaphor comprehension in psycholonguistics, several of which are closely related to it, while others provide illuminating contrasts. The two models closest to the present approach are the class-inclusion model of Glucksberg (1998) and the underspecification model of Frisson & Pickering (2001). Glucksberg’s view that Noun-is-a-Noun metaphors are class inclusion assertions where the appropriate class is newly generated by the metaphor, was the basis for developing the present model in Kintsch (2000). Indeed, LSA and the predication model are one way in which the notion of generating metaphorical superordinate categories can be operationalized. Frisson & Pickering’s notion that people initially access an underspecified meaning of words and then elaborate it in context also describes the predication algorithm on which the present model is based. Specifically, the underspecified representation of polysemous words in the present case is the LSA vector (which is not so much under specified as un specified, since it lumps together all meanings and senses of a word); the mechanism that generates a specific,
context appropriate interpretation is the constraint satisfaction process of the predication algorithm. A comparison with the constraint satisfaction model of Katz & Ferretti (2001), on the other hand, points out a limitation of the present model: the spreading activation process (see the example in the Appendix) considers only semantic constraints, while Katz & Ferretti want to consider a broader range of constraints (e.g., syntactic constraints).
Gentner & Bowdles (2001) highlights another limitation of the present approach. Some metaphors are understood like analogies, i.e. by structural alignment, which is a controlled, resource demanding process. The predication algorithm, in contrast, applies when sentences, (metaphorical or not), are understood automatically, without requiring this kind of problem solving.
The principal difference between the present model and other models -psycholinguistic, linguistic, or philosophical - is that it is a fully realized, computational theory. Below we explore whether this computational model arrives at interpretations that are like human interpretations. In Kintsch (2000), the LSA vectors generated by the model were compared with intuitively plausible landmarks. For instance, it was shown that My lawyer is a shark is closer to viciousness than lawyer by itself, which is what one would expect. Here, we employ a method that does not require the use of selected landmarks. Instead, we directly compare the vector constructed by the model with the set of interpretations of a metaphor generated by people. If the model successfully captures the meaning of the metaphor, the sentence vector should be more closely related to the set of interpretations generated by human comprehenders than to the individual words of the sentence.
We also propose to examine the computational processes that generate the vectors for different classes of metaphors for clues as to what differentiates the processing of easy and difficult metaphors. It is well known empirically (Katz et al., 1988) that there are large differences in the ease with which metaphors are understood. What is it that differs when the model processes easy and difficult metaphors? If we observe such a difference, this may be a clue about the sources of comprehension difficulty in human understanding.
Method
Participants
Twenty-four undergraduate students at the University of Colorado participated in the experiment. All were native speakers of English and received class credit for their participation.
Materials and procedure
Each participant was tested individually in a twenty-minute experimental session. After giving informed consent, each participant received an experimental packet consisting of a page of instructions and three pages of stimuli (10 stimulus sentences
per page). Each stimulus sentence was a metaphorical statement of the N
1-is-N
2
(for
example, My lawyer is a shark.)
Each participant saw the metaphors in the same fixed order. The stimulus order was pseudorandom with the constraints that no two metaphors with the same argument were adjacent and that no more than three easy or three difficult metaphors were presented in a row. The judgment of which metaphors would be easy and which would be difficult was based on data from a pilot experiment using these stimuli.
Beneath each stimulus sentence were two additional items. The first was a sentence completion frame consisting of the subject and verb "X is" of the original metaphor sentence followed by a blank line. Participants were instructed to complete the sentence with a literal version of the original metaphor. For example, if the participant saw the metaphor, My lawyer is a shark, followed by My lawyer is
_______________________" s/he might fill in very mean in order to reflect the literal meaning of the metaphor. After each sentence completion, a set of rating numbers was listed. The participants were asked to circle a number (1-5) to reflect the difficulty of comprehending the stimulus metaphor. A rating of "1" indicated that the metaphor was very easy to understand, and a rating of "5" indicated that the metaphor was very difficult to understand. Participants were instructed to work their way through the packets and to try to come up with an answer and rating for each stimulus metaphor.
Results
Average difficulty ratings were calculated for each stimulus metaphor. Difficulty ratings ranged from 1.29 (The mosquito is a vampire) to 4.21 (A factor is an administrator). Thirteen metaphors had a rating of 2 or lower and 13 metaphors had a rating of 3 or higher. For the simulations, these were designated as easy and difficult respectively. The remaining four metaphors with intermediate ratings were discarded.
Table 1 shows that the easy and difficult metaphors were clearly differentiated not just in their ratings but also in terms of the interpretive responses subjects generated. For easy metaphors, almost half (48%) of all responses were identical in meaning (e.g. blood sucker, sucks blood, blood sucking for the Mosquito is a vampire metaphor). Much less agreement (21%) existed among the subjects for difficult items, t(24) = 4.02, p<.01. While there were no failures to respond on the generation test for easy items, subjects could not generate a response on 7% of the trials for the difficult items. Furthermore, if one looks at the whole set of responses generated by the subjects, that set was more coherent for easy items than for hard items. The coherence measure used here is the average cosine of each subject‘s response to the whole set of responses for a particular metaphor, shown in the last column of Table 1. The difference between the coherence of easy items and difficult items was statistically significant, t(24)=4.38, p<.01.
Table 1
The results for difficult metaphors are noteworthy. Faced with items such as A factor is an administrator or Happiness is a ditch, people don’t just give up, but find some interpretation or another. And it is not just a random interpretation, either: on the average about 4 to 5 of our 23 subjects came up with the same response, which is less than the 11-subject agreement we found for easy metaphors, but far from random. Similarly, the responses subjects generated for the difficult metaphors were more diverse (the average cosine between a response and the total response set is .55) than for easy metaphors (average cosine is .64), but what is striking is that there still was a considerable level of agreement, even for what one might regard as pure nonsense.
Results of the Simulations
To determine how well the model was able to fit the data from the rating study, we used the predication model to compute the cosine between the vector representing the meaning of a metaphor and the vector representing the set of all responses generated by the subjects for this metaphor. Averaged over the 26 metaphors used for the simulation, this cosine has a value of .51. There is, however, no way of determining whether this value is high or low, for the absolute value of cosines in LSA depends on many factors; only relative values for cosines computed in the same way can be readily compared. Table 2 provides such a comparison. The cosine between the metaphor vector computed by the model and the set of responses generated by the subjects is higher than the cosine between either:
a)just the argument of the metaphor and the set of subject responses OR
b)just the predicate of the metaphor and the set of subject responses,
p < .001 by sign test. Importantly, the cosine generated by the predication model is also higher than the cosine between the centroids of the argument and predicate of a metaphor, p < .001 by sign test. Thus, while we cannot claim that the model predictions are good on an absolute scale, we know that they are better than what can be achieved by either the predicate or argument alone, or by the centroid of the two.
Table 2 also shows that there was no difference between how well the model fits the subjects’ responses for easy and hard metaphors. It is clearly not the case that the model fits the data only when subjects agree with each other, that is, for the easy metaphors. When people agree about the interpretation of a metaphor the model computes a vector that is closely related to that agreed upon meaning of the metaphor. However, for difficult metaphors, where there is much less agreement and subjects generate a more diverse set of responses, the vector computed by the model is just as close to the average of the subjects’ responses. For easy metaphors, the model focuses in on some specific meaning; for difficult metaphors, it specifies a diffuse but non-arbitrary meaning – just as real people do. To understand what is happening here one must remember that the LSA vector for a set of a responses is the centroid, i.e. average, of the individual response vectors. The model vector is equally close to that average of easy
and the average of difficult items, although the average for the easy items is computed from a narrow range of responses while the average of the difficult items is based on a diffuse set of responses.
Table 2
This interpretation is supported by an analysis of the relationship between the metaphor vectors computed by the model and the modal responses given by the participants. As Table 1 shows, almost half of all responses were common for easy metaphors; the average cosine between these modal responses and the metaphor vector is .32i. In contrast, many fewer common responses were given to difficult metaphors (21%), and their cosine with the metaphor vector is significantly lower, cosine = .22 (t(24) =
1.75, p < .05). The model calculates a vector that is equally close in semantic space to the set of responses participants produce for easy and difficult metaphors. However, that set is different for easy and difficult metaphors. For easy metaphors, there is agreement among participants and their choice is strongly related to the model vector. For difficult metaphors, there is much less agreement among participants, and their choice is less strongly related to the model vector. In the first case, the vector is fairly precise and generally focuses on a particular concept – the modal response of the participants; in the second case, this focus is lacking, but the model vector nevertheless captures the variety of responses produced by the participants.
Given these results, the question arises, “what makes a metaphor easy or difficulty according to the predication model?” One obvious candidate is the semantic distance between the argument and predicate of a metaphor. One might suppose that if the two terms are very far apart in the semantic space, it might be difficult to find something they have in common. This conjecture does not hold up, however: the average cosine between the argument and predicate for easy metaphors is .10, versus .07 for difficult metaphors –a difference that is unreliable statistically, t(24) = .96. Thus, metaphors are not difficult because their argument and predicate terms are unrelated overall.
Another possibility is that processing difficulty depends on how much information is available about either of the two terms of a metaphor. However, the data do not support this hypothesis either. The length of a vector is a measure of how much information LSA has about a word. The average vector length for the predicates of the
easy metaphors was .86, versus 1.27 for the hard metaphors, t(24) = 1.12, p >.05. Another way of measuring how much LSA knows about a word is to look at the number of other words that are close neighbors. However, there is no difference between the predicates of easy and hard metaphors in this respect either: easy predicates have on the average 17 neighbors with a cosine greater than .5 and 36 neighbors with a cosine greater than .4, and hard predicates have 18 neighbors with a cosine greater than .5 and 22 with a cosine greater than .4. Finally, there is no difference in the vector length of the arguments of easy (1.19) and hard metaphors (1.00), t(24) = .63, p > .05. Thus, it does not appear that processing difficulty is related to properties of either the argument or the predicate of a metaphor in isolation.
Table 3
A more promising hypothesis is that processing difficulty depends on whether at least a few items can be found that are strongly related to both the argument and the predicate of a metaphor. As described in the Appendix, the predication model for metaphors of the form N1-is-N2 works by selecting neighbors of N2 that are most closely related to N1 and uses these terms to modify the N2-vector. Are these terms more closely related to N1and N2 for easy metaphors than for difficult metaphors? It turns out that is not the case for N2 – the average cosine between N2 and the set of selected terms is not significantly different for easy and hard metaphors, t(24) = .62, as shown in Table 3. However, the differences between easy and difficult metaphors is statistically significant for N1, t(24) = 2.22, p <.05, and marginally significant for N1* N2, t(24) = 1.54, p = .07. Thus, the predication model suggests that metaphors are easy to process if the argument has a good match among the close neighbors of the predicate; if the match is less good, this is experienced as processing difficulty, perhaps because the search for a better match continues into regions where items are no longer sufficiently strongly related to N2. This must remain a tentative conclusion, however, for the relationship is not overly strong: the correlation between the rated difficulty of a metaphor and the cosine(selections : N1) is only r = -.46, which is significant statistically, but not very high.
Conclusions
Latent Semantic Analysis or LSA allows one to represent the meaning of words as vectors in a high-dimensional semantic space. The meaning of sentences can be computed from these vectors. There are two ways of computing a sentence vector, given the constituent word vectors. One is context free. It disregards the syntax and simply sums up the word vectors. Another possibility is to adjust the word vectors contextually according to their syntax. Specifically, in the predication model of Kintsch (2001),
sentence vectors of the form N
1-is-N
2
are computed by modifying the predicate vector N
2
according to the argument vector N
1
. Thus, a context appropriate sense of the predicate is generated. This model has been shown to provide a good account of several semantic phenomena that otherwise are outside the scope of LSA by itself, including metaphor comprehension (Kintsch, 2000). The present results provide further evidence that the predication model is capable of adequately representing the meaning of simple nominal metaphors, in the sense that the metaphor vectors it computes are closely related to the interpretations that people give to these metaphors.
The data reported here focus on differences in the way people interpret easy and difficult metaphors. We have shown that metaphors that are judged to be easy to comprehend are interpreted in similar ways by most people, whereas a greater range of interpretations exists for difficult to comprehend metaphors. However, people agreed among each other to some extent, even when the metaphors they were asked to interpret appeared to be pure nonsense. Faced with the seemingly impossible task of finding an interpretation for such metaphors, people do not give up (a failure to respond was observed in only in 7% of the cases); instead they come up with something, and there is a certain consistency among people in how they respond. There is not nearly as much consistency for difficult metaphors as for easy metaphors, but while interpretations are diffuse and vague for difficult metaphors, they are not random. This consistency in people’s responses may, however, not derive from having successfully interpreted a difficult metaphor, but may simply reflect word-based constraints.
Interestingly, the predication model behaved in much the same way: it came up with vague and less coherent interpretations for difficult metaphors, but it matched what
people said as well as for easily comprehended metaphors. For easy metaphors there is widespread agreement among people, and the model produces a vector close to that agreed upon interpretation. For difficult metaphors, responses are more varied, but the model produces a vector that is just as close to these varied responses as it is to the generally agreed upon interpretation of a good metaphor. For both people and model there is something in the semantic structure that guides their interpretation. The semantic structure provides a tight constraint for easy metaphors, and only a loose one for hard metaphors, but the comprehension process neither collapses nor becomes random.
If the model understands easy and difficult metaphor equally well (in the sense that it predicts human interpretations equally well), then what is different about the computational process for easy and difficult metaphors? It is not the case that the constituent words by themselves are more or less informative, nor is it the case that easy understanding requires a pre-existing global relation between the two terms of a metaphor. Rather, it appears that, although argument and predicate can be totally unrelated overall, a metaphor is comprehensible if some link is found between topic and vehicle, even though the two may be unrelated overall. Thus, lawyer and shark are orthogonal in the semantic space (cosine (shark:lawyer) = -. 01), but there are certain aspects – like vicious or mean – that link the two and make “My lawyer is a shark” an easily understandable metaphor.
Theories of metaphor comprehension have traditionally been informal. We hope that by offering a formal model that can yield quantitative experimental predictions and, at the same time, is conceptually related to the issues under discussion in the psycholinguistic literature, further progress can be made in our understanding of comprehension processes, metaphoric as well as literal. We also claim that the results presented here show that LSA provides a useful basis for a psychological theory of meaning.
References
Cacciari, C. & Glucksberg, S. (1994). Understanding figurative language. In M. A.
Gernsbacher (Ed.), Handbook of psycholinguistics (pp.447-478). San Diego:
Academic Press.
Frisson, S. & Pickering, M. J. (2001). Obtaining figurative interpretations of a word: Support for underspecification. Metaphor and Symbol, 16, 133-187.
Gentner, D. & Bowdle, B. F. (2001). Convention, form, and figurative language processing. Metaphor and Symbol, 16, 223-247.
Gibbs, R. W. Jr. (1994) Figurative throught and figurative language. In M. A.
Gernsbacher (Ed.), Handbook of psycholinguistics (pp.441-446). San Diego:
Academic Press.
Gibbs, R. W. Jr. (2001). Evaluating contemporary models of figurative language understanding. Metaphor and Symbol,16, 317-333.
Glucksberg, S. (1998). Understanding metaphors. Current Directions in Psychological Science, 7, 39-43.
Katz, A. N. & Ferretti, T. R. (2001). Moment –by-moment readings of proverbs in literal and nonliteral contexts. Metaphor and Symbol, 16, 193-221.
Katz, A. N., Paivio, A., Marschark, M., & Clark, J. M. (1988). Norms for 204 literary and 260 nonliterary metaphors on 10 psychological dimensions. Metaphor and
Symbolic Activity, 3, 191-214.
Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: Cambridge University Press.
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Kintsch, W. (2001). Predication. Cognitive Science, 25, 173-202.
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Table 1
Rated difficulty and properties of the responses generated for easy and difficult metaphors
Rated Difficulty of Comprehension Modal Response
Frequency
No-Response
Frequency
Coherence of
Responses
Easy 1.7548%0%.64 Difficult 3.6821%7%.55
Table 2
Cosines between the vectors for metaphors, their argument (N1) and predicates (N2), and all the responses generated by subjects
Cosine{metaphor : responses}Cosine{ N1 :
responses}
Cosine{ N2 :
responses}
Easy.50.34.34 Difficult.51.34.31
Table 3
Average cosines between the items selected by the model to modify
the predicate-vector and the predicate (N2) and argument (N1) of
the metaphor, as well as the product of the cosines
cos{selections: N2}cos{selections: N1}cos{sel: N1}*cos{sel:
N2}
Easy.40.30.12
Difficult.43.21.08
Acknowledgements:
This research was supported by a contract from the Army Research Institute to T. K. Landauer and W. Kintsch. We thank Tom Landauer, Kirsten Butcher, Eileen Kintsch, and the LSA Research Group for their help.
我的高中英语教学故事
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特点，把握学习规律。从教师的角度讲，新课改形势下英语教学的第一要务是必须转变观念，以实现教与学的全面改观。 一、帮助学生正确认识高中英语的学科特点 在高一开学伊始，可以在师生见面课上帮助学生分析初、高中英语的区别与联系，引导他们正确认识高中英语的学科特点。 还可以在第一次月考之后的试卷讲评课上，通过分析学生的错误试题，让他们明白：高中英语较之初中英语难学，是因为初中的教材内容丰富而有趣，篇幅短小而浅易，单词、词组、语法、句型等都较为简单。很多词组、句型即使从语法上说不出为什么，但也应该倒背如流，套用自如，在考试中得高分。而高中的英语课文篇幅长，课时少且课后的自习时间有限、词汇多，语法难且要求掌握的知识要点多，如果没有科学、合理的学科时间调配，就无法进行有效预习和复习。
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高中英语教学论文篇
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主学习水平）。相反，有些教师利用新教材组织了一些以学生为主体的课堂教学活动，但是，也只能说它是一种表象而已（突出地表现在公开课上），“花拳秀腿”，没有实际的意义。有的教师在处理该教材的过程中，要么出几个问题学生自学，要么让学生试讲，要么搞些同步导练就算“突出了以学生为主体”的教学思想。这两种做法都是片面的。高中英语课堂上教师应积极为学生创设情景，使学生能在较为真实的语境中获得真正的英语交际水平。不同的教学阶段，教师要持续地转变自己的主导角色，或者充当总指挥，总导演与裁判员的作用，构思，编排，组织和评价课堂练习活动，或者“传道，授业，解惑”，充当英语基础知识的有效讲解者，或者充当“划船水手”的作用，与学生平等地完成课堂教学活动等。 2、滥用教学媒体和教具 新教材突出了英语语言的交际功能，与社会生活实际紧密相关，时代感强。所以，若能合理使用教学媒体，就能改变传统的“教师一枝粉笔、一张嘴，从头至尾一言堂”的局面，若能将媒体使用恰当，就能极大地激发学生的学习兴趣，启迪学生的积极思维，准确模仿与交际，而且，能够很好地突破教学的重点和难点，显示其直观性与化繁难为简易的最优功能。不过，很多教师在课堂教学中，频繁地使用教学媒体，以机代人。该用的地方使用，不该使用的地方也使用；该用的教具用，不该用的教具也“闪亮登场”。结果，整节课鼓声、钟声、歌声、欢笑声与吵闹声不绝于耳，仿佛来到了金戈铁马，喊杀声一片的“古代战场”。所以，高中英语课堂教学中滥用教学媒体和教具的现象，是当今从教者应该注重的问题之一。
高中英语教学中评价的必要性
龙源期刊网 https://www.sodocs.net/doc/e210638923.html, 高中英语教学中评价的必要性 作者：贾军艳 来源：《读写算·教研版》2013年第08期 摘要：评价的最终目的在于增强学生的自信心，提高学生的学习兴趣，是学生形成积极 的情感态度，主动参与到课堂活动中来。对于错误的回答，评价时时应以启发为主。 关键词：评价；创设情境；学习；兴趣 中图分类号：G632 文献标识码：B 文章编号：1002-7661（2013）08-045-01 在课堂教学中，教师应偏重于使用形成性评价方式，评价的最终目的在于增强学生的自信心，提高学生的学习兴趣，是学生形成积极的情感态度，主动参与到课堂活动中来。对于错误的回答，评价时时应以启发为主；对于精彩的回答，不要吝啬赞美之辞；对所有学生要尽可能用赏识的目光看待学生的回答，需要注意的是，评价只是教学的一种手段，教师应当通过评价让学生更好的学好英语。 一、精心创设情境 高中英语教学中，不少教师费尽口舌向学生输灌课文知识，久而久之学生就厌倦了这种被动的学习方式，这与新课标中提出的“自主、合作、探究”的学习理念相违背。在现代英语教学中，有效地创设情境能够有效的促进教学。 情境创设是指在教学过程中为了达到既定的目标，创设与教学内容相适应的具体事件、场景或氛围，引起学生的情感体念，激发和吸引学生主动学习的兴趣，帮助学生迅速而正确的理解教学内容促进他们的心理机能全面和谐发展，以达到最佳的教学效果。情境创设应遵循以下几个原则。 1、可接受原则 创设情境应符合学生的身心发展特点，基于学生的实际水平，能够被学生所理解。例如笔者在进行老人教版Unit15 Great Inventions中的第一部分教学中的应用。这部分主要讲中国古 代的四大发明之一-----印刷术。我创设的情境是：课堂上和学生一起用纸、笔和一元硬币进行对硬币的复制，由此引出课文重点、难点。这样的课堂情境对学生非常熟悉，也非常有趣，极易调动学生的参与性。 2、真实性原则 在选择设置情境时，教师要注重联系学生的生活，在日常生活中发现和挖掘学习资源，善于利用学生的情感来设置相应的教学情境。教学情境越真实，学生越容易将自己的情感移入所
高中英语教学中的师生关系
高中英语教学中的师生关系 发表时间：2014-05-16T11:40:05.060Z 来源：《教育研究·教研版》2014年3月供稿作者：刘洪[导读] 英语教师要关注学生的发展需求了解、关注、满足不同学生的个性需要与共性需要，是教师要关注与研究的问题。刘洪 〔摘要〕21 世纪教育着眼于人的发展，关注人的情感、态度和人生观，新课程标准也非常强调英语教学中的情感因素，因为情感是人发展的持久动力。这就要求我们在英语教学中不但要重视学生的语言知识和语言技能的传授与训练，更要关注学生情感态度的发展。 〔关键词〕师生关系高中英语课堂教学 英语教学作为语言教学的一个过程，同时也是一个情感交流的过程。教学中如果正确把握情感教育，既可以帮助塑造学生良好的品行，也能激发学的学习兴趣，从而提高教学质量。中学生的学习活动带有很大程度的情绪化倾向，对学习有浓厚的兴趣、好奇心和强烈的求知欲望时，不仅能产生情感迁移，而且会使注意力特别集中和持久，学习和交往能力迅速提高。 1 英语教师要关注学生的发展需求了解、关注、满足不同学生的个性需要与共性需要，是教师要关注与研究的问题。教师要通过学生调研的方式切实了解学生的内在需求，据此制订合理的教学目标。在教学过程中，既要关注学生的情感需求，更要关注认知需求，只满足学生情感需求的课堂会偏离教学目标。另外，引导学生在学习方法、策略，良好的课堂行为习惯养成，最终发展学生的语言运用能力，才是真正满足学生的发展需求。学生的发展是英语课程的出发点和归宿。英语课程在目标设定、教学过程、课程评价和教学资源的开发等方面都突出以学生为主体的思想。关注、调查、分析并满足学生的发展需求是落实学生主体地位的保障。如何了解、关注、满足不同学生的不同需求以及共性需求，是教师要关注、研究的问题。学生需求的内涵很多，就英语学科教学来说，学生的需求是综合语言运用能力的发展需求，具体体现为语言技能、语言知识、情感态度、学习策略等方面的发展需求。从需求本身来看，还要区分学生的内在实际发展需求，即在一个特定阶段的学生在已有的经验、知识、思维、情感态度基础上的最近发展区。另外，还有课程总体目标、课程级别标准对学生提出的综合语言运用能力发展的外在需求。在现行的教学实践中，一些教师忽视学生的内在发展需求，忽视学生已有的知识、能力基础以及与话题有关的生活经验、思维方式、情感态度、认知水平，教师按照自己的经验和主观判断来确定学生的需求；还有一些教师把教材、教参确定的教学重点确定为学生的需求，结果是教学效率低下。还有一些教师似乎关注学生的发展需求，在教学设计中有学情分析部分，但是学情分析基本是依据教师的主观判断，说明学生已经学过哪些内容，或者是对学生整体特征进行描述。例如，一位教师呈现的学情分析的部分内容是：“1/3 学生属基础薄弱，对英语没有兴趣，学习态度消极，挤牙膏式学习，其中有3 个学生几乎完全放弃学习。”这种分析对于教师合理确定教学目标、教学过程没有帮助，似乎在给教师提供某种依据，可以把学生学习效果差的责任都归咎于学生的基础薄弱和态度消极。 2 培养学习兴趣，以情激趣 要想使学生对英语深感兴趣，教师必须钻研教材，发掘教材中的情感因素，通过英语教学上的艺术性、形象性、趣味性揭示英语教材本身的魅力，达到调动学生的积极性和主动性。并且在教学中认真研究学生的心理特征，针对学生的实际情况，采用现代化的教学手段和灵活多样的教学方法，尽量使用简笔画、幻灯、录像、课堂游戏、讲故事、演话剧、唱歌、课外活动等，不断改变自己的常规教学模式，让学生始终感觉到有一种新鲜感。并且要积极创造条件和环境，组织书写、口语、朗诵、阅读、征文和唱英文歌曲等比赛，使学生尽可能地多使用英语，在应用中尝到乐趣。这样既可以激发学生的学习兴趣，也有助于师生间感情的培养。 3 创设情境，以境生情 在英语教学中，创设生活化的情境，让学生在无拘无束、轻松愉快的生活场景中运用语言、交流思想、表达情感、锻炼才能。电教媒体的教学优势，能使创设的情境更为形象、生动、逼真，易于学生接受，易于唤起师生情感，调动了学生的学习积极性，得以提高教学效果，达到教学目的。教学中常用的电教媒体有：幻灯、投影、录音、录像、计算机、语言实验室等。这些电教媒体介入英语课堂教学中，为创设语言环境提供了有利条件，学生可以从被动的信息接受变为语言交际的积极参与者。然而要实现教学过程的最优化，达到教学目的，还要依据教学目标、教学内容、教学对象以及各种教学媒体的不同性能选择媒体，创设英语课堂情境。如选用录音媒体，营造学习氛围，学习活动可以是调查采访、角色表演、游戏竞赛、画外配音，学以致用，知识才能化为能力，才能为英语课堂引入一泓不绝的活水。生活化的场景不仅是语言的载体，还是情趣的载体、创造的载体，学生置身其中，感受到浓郁的生活气息，更容易进入角色，课堂气氛也异常活跃。 所学的语言知识在第一时间得到了运用；更重要的是，学生通过课堂充分展现了自我，并获得了丰富的内心体验。设计有效的课堂教学活动，不仅要充分体现“以学生为中心”的教学思想，最大限度的发挥学生的主体性，让学生乐于学英语，善于学英语，真正成为学习英语的小主人；而且还要使教学活动的设计要符合新课标理念下新的教学目标与要求，要能真正代表学生，激发学生的学习兴趣，并让学生在多元化评价中形成持久的学习动机，在完成任务的过程中体验学习成功的愉悦，以完善和发展学生的综合能力。生动有趣、重在实践的语法教学方法有助于营造出一个人性化的教学环境，不仅有利于调动学生的学习积极性，提高教学效果，还有利于学生身心、情感的健康发展。 作者单位：重庆市九龙坡区铁路中学27__
我的高中英语教学故事 (1)
英语课程故事 林口县职业技术教育中心徐世亮 在英语教学中培养学生的学习兴趣至关重要。我在平时的教学中将此作为工作的重点，并在教学中积累了许多教学故事。 在教学中培养学生的学习兴趣，增强教学效果，才能避免在以后的学习中产生两极分化。具体做法是把语言教学与日常生活联系起来，上课可以结合时事、热点，给学生讲有关政治、历史、社会等各方面的新闻、幽默等。还可以通过开辟第二课堂，如举办英语角、讲故事、各类竞赛等。老师课后应当多和学生交流接触，了解学生的需要，适当和学生一起参加活动、体育运动。课后还可以布置学生听英语广播、看英语新闻，然后再到学校进行交流。然而，在教学实践中，批改作业是一个薄弱环节、一个被忽视的环节。在此，我总结自己的教学体会，谈谈自己对批改作业的看法。 1 巩固教学效果，发展学生智能 教学的一个根本目的，是要求学生学会独立思考、独立自学，最终成为一个能独立工作的合格人才。课内教学的双边活动是至关重要的，但由于学生多，时间少，而学生主动活动机会则不多。所以，课外作业便是学生个体完成作业的独立活动的基本形式。学生在完成作业过程中，需要独立思考、独立钻研，这对学生有充分的实际锻炼机会。课外作业，有助于学生发展智能和创造性才能，有助于培养学生灵活地应用知识解决问题，有助于培养学生的能力，并能培养学生按时完成任务的习惯和责任心，以及克服困难的精神。因此，教师要在作业的布置和批改上下工夫，不仅要重视答案的公平性、客观性和唯一性，还要重视学生的选择性和创造性以及答案的开放性和多样性。 如，学了高二上册Unit9的Saving the earth, 我要求学生写一篇作文What do you think is the most serious problems in the world? 要求学生联系生活列举身边对环境造成污染的事例，并提出解决问题的方法，从而对学生进行环保教育，使他们认识到地球环境问题的严重性及如何由我做起，在日常生活中的点滴做起保护环境，节约能源。 2 及时对学生信息进行反馈，认真调整教学环节 每个学生对课堂上的知识掌握程度，都会在作业中直接反映出来。对于难度较大的题目，学生出现错误较多的作业，教师批语应多加鼓励，并且在讲解时要具体详细，对课堂教学也必须做出相应的调整。对于难度较小，作业一般不应该出现的问题，批语应该严肃恳切，并指导学生及时纠正。同时，教师可利用批语争对不同层次的学生征求对课堂教学，尤其是单元教学的意见，使自己对教学方法和教材处理更加科学合理，更加有利学生主动接受性、新知识，提出新问题。例如，教师可以在中下等水平的学生作业里用批语征求意见：Do you think this unit is difficult for you? What do you think is the most difficult point? 或者What’s your suggestion on how to deal with the next unit?坚持这样做一段时间后，学生会主动向教师反映上一个单元中还没有弄清的教学内容和在新课文预习的过程中遇到的问题。这样，就能做到因材施教、教学相长。 3 交流师生感情促进学生的求知欲 人的心理结构是有智力、意志、情感三个基本因素组成的。情感因素在培养学生学习兴趣，促进学生积极主动接受知识的过程中起到关键作用。教师应采用教学
Meta分析系列之一_Meta分析的类型_曾宪涛
·循证理论与实践· Meta分析系列之一：Meta分析的类型 曾宪涛，冷卫东，郭毅，刘菊英 ［中图分类号］R4［文献标识码］A［文章编号］1674-4055（2012）01-0003-03 证据是循证医学（Evidence-based Medcine，EBM）的核心，基于随机对照试验（RCT）的系统评价/Meta分析是当前公认的最高级别证据。近些年来，随着Meta分析的不断推广，国内Meta分析大量涌现，这为临床实践提供了很好的支持。但因制作者的水平各异，使得Meta分析的质量良莠不齐，也给临床实践中使用者的选择带来了疑惑；再者，Meta分析只有真正走向临床并正确的使用，才能体现其价值，而当前Meta分析的应用较少。鉴于此，我们拟从Meta分析的类型、相关软件、原始研究质量评价、报告规范、Meta分析的质量评价等方面进行一个系列的讲解，以期为使用者提供参考。 本文作为第一篇，我们将对Meta分的起源和发展进行简要回顾后，对当前存在的Meta分析类型进行一个简介。希望通过本系列，使相关的读者能够撰写出较高质量的Meta 分析并能将其在临床工作中进行运用。 1起源及发展 Meta分析的起源最早可以追溯到著名统计学家Pear-son，他于1904年在研究“血清接种预防肠热病的疗效”研究中用这一方法将接种肠热病疫苗与生存率之间的相关系数进行了合并［1］；1907年，Goldberger制作了特定的标准对所发表的有关伤寒杆菌尿毒症的文献进行选择及资料的提取分析，然后进行统计学分析，被认为达到了当今Meta分析的基本要求［2］；1920年，著名统计、遗传学家Fisher提出了“合并P值”的思想［3］，被认为是Meta分析的前身；1930年开始，Meta分析开始广泛应用了社会科学领域［2］。 直到1955年，Beecher［4］发表了医学领域第一篇真正意义上的Meta分析，用以评价安慰剂的疗效，并给予了Meta分析初步的定义；之后Meta分析在社会学领域再次得到发展，这一术语由英国教育心理学家Glass［5］于1976年命名并将其定义为：“The statistical analysis of large collection of analysis results from individual studies for the purpose of integrating the findings”。此后，关于Meta分析的定义一直存在争议，不少方法学家亦都对其下过定义，但都倾向于“Meta分析是对以往的研究结果进行系统定量综合的统计学方法”这一含义［3］。 如今，Meta分析在医学领域应用最为广泛，应用最多的是对干预性的随机对照试验的评价，在观察性研究和交叉对照研究中也得到推荐［6］。自Archie Cochrane奠定“循证医 基金项目：湖北医药学院2010年教学研究项目（2010017）；湖北医药学院2011年度优秀中青年科技创新团队项目（2011CZX01）；湖北医药学院附属太和医院2011年度博士科研启动基金（2011QD01；2011QD05）。 作者单位：442000十堰，湖北医药学院附属太和医院口腔科（曾宪涛，冷卫东）；武汉大学公共卫生学院流行病学教研室（郭毅）；湖北医药学院附属太和医院麻醉科（刘菊英）。 通讯作者：刘菊英，E-mail：liu6119@163．com doi：10．3969/j．issn．1674-4055．2012．01．002 学”思想以来，Meta分析已成为临床证据的重要来源之一［7］。 2单组率的Meta分析 单组率的Meta分析，是一种只提供了一组人群的总人数和事件发生人数，不像其它类的Meta分析有两组人群，多为患病率、检出率、知晓率、病死率、感染率等的调查，基于的原始研究为横断面研究（cross-sectional study）。 目前，对各独立样本中效应量为率的同类研究资料进行Meta分析并没有比较成熟的方法，较常用的有以下几种：①加权计算［8］：即根据每个独立研究的样本量大小，给予不同的权重，对各独立样本的效应量率进行合并；②直接等权相加［9］：即把各独立的结果事件直接等权相加，然后直接计算合并率，再用近似正态法计算其可信区间（95%可信区间=p ?1．96Sp）；③调整后再等权相加［10］：即对各个独立研究资料的率进行调整后再行等权相加，计算出合并率的大小。 对单组率的Meta分析而言，最难的就是控制异质性，进行亚组分析和Meta回归分析是其重要的处理方法［8-9］。 3单纯P值的Meta分析 1932年，著名统计学家Fisher提出了“合并P值”的思想，被认为是Meta分析的前身［3］。但在后期的应用中许多学者发现单纯合并P值存在以下不足［11-12］：不同研究未能根据研究特点进行加权；无法获知事件的发生信息，故无法得出有任何临床意义的信息；③无法分析两个结论相反的研究；④无法进一步评价研究之间的差异。 因此，单纯行P值的Meta分析是不推荐的。但当纳入研究仅给出了P值，且按照Cochrane系统评价员手册［13］给出计算方法也不能计算出需要的数据，且临床实践需要合并，那么，在这种情况下可以考虑单纯对P值进行合并。 4Meta回归分析 在Meta分析时，需分析各研究间的异质性，并对异质性的来源进行探讨，Meta回归（meta-regression）分析可评价研究间异质性的大小及来源。一般认为，Meta回归分析是亚组分析的一种扩大，主要通过对多因素的效应量进行联合分析实现，仅当Meta分析纳入的研究数量在10个以上时才行此分析［13-15，28］。 在Meta回归里，将效应估计量（如RR、OR、MD或logRR 等）作为结果变量，将可影响效应量大小的研究特征因素（“协变量”或“潜在效应量改变因子”）作为解释变量，则回归系数描述了结果变量怎样随着解释变量的单位增加而改变；其统计学差异性通过对结果变量和解释变量之间有无线性关系来确定，通过回归系数的P值来判断这种差异有无统计学意义［13-15，28］。 5累积Meta分析 累积Meta分析（cumulative meta-analysis）最早应用于1981年，是指将研究资料作为一个连续的统一体，按研究开展的时间顺序及时将新出现的研究纳入原有Meta分析的一种方法［16-19，28］。因此，Meta分析每次研究加入后均重复一次Meta分析，可以反映研究结果的动态变化趋势及各研究对结
高中英语教学经验浅谈
高中英语教学经验浅谈 舒城中学房成雅 本人在过去若干年的高中英语教学中,我认为作为一个称职的英语教师除了具有扎实的专业基础和饱满的工作热情外，还要以学生为主体，善于发现各种问题，培养发掘学生的语言能力，这样才能知己知彼，百战不殆。现在我将对这些年高中英语教学的一些见解阐述如下: 一、以贯彻交际性原则为实战思想,强化以学生为主体的主体性意识 学生不仅是教育对象,更是重要的教育资源。依靠学生的内部力量、发挥其学习天性是教学的重要教学理念及行之有效的教学手段。所以在日常教学中,教师应该重视且发挥好学生的主体作用。然而实际情况是,小学、初中英语教学中的英语学习主体性挖掘和运用要容易得多。但是随着学生年龄的增长,自我意识的强化,英语语言积累储量的增加,高中生在英语教学中的主体意识反而明显下降。如何做到这种主体性意识的增强呢，可以从以下两点入手： 1、适当让学生表演对话或课文内容。针对近期所学内容,布置学生课后进行讨论排练,然后再搬上课堂表演,比如说“My teacher”那课,我就组织学生进行演讲比赛,要求每班有若干同学参加,课后进行排练,学生们积极性都很高,最后比赛时邀请外班学生评选,活动非常成功,在激发学生学习英语的同时也娱乐了学生的课外生活; 2、多鼓励,少批评。为了使学生更爱开口,在练习中,要重视运用鼓励性语言,不断强化学生参加交际的信心,要鼓励学生多练,多开口,不怕犯错,要在错误中不断提高。教师要尊重学生的情感,营造一种愉悦、宽松、平等、合作的课堂氛围,让学生有积极的情感体验。教师是学生学习情绪的主导者,营造良好的课堂氛围,必须要求教师在课堂上要情绪饱满,对上课充满信心,带着轻松自信的情绪走进课堂,以良好的情绪影响和感染学生。 英语学科很强的实践性则更能说明学生主体性的重要所在。要具有一定的英语听、说、读、写能力,学生除了要认真学习英语的有关知识外,更重要的是要亲自实践。就好像不下水永远也学不会游泳一样,英语学习者也必须投身到使用英语的实践当中。北京外国语学院刘润清教授说:“English can’t be taught.It must be learned. Jimmy said,‘I forget what I was taught,I only remember what I’ve learnt.’”这说明在英语教学活动中,学生才是主体,衡量教学效果的最终尺度是学生对英语知识掌握的程度,而不是老师水平的高低和付出劳动的多少。 二、以探究性学习能力为教学准绳，逐步培养学生的自学能力 在英语教学中只有通过辅导学生掌握一套科学的学习方法,并培养学生的自学能力,才能使学生的学习积极性和主动性得以发挥，并在具体学习中取得事半功倍的效果。培养学生的自学能力具体包括:培养学生的预习能力、各种方法掌握英语基本知识的能力、学生自己整理所学知识的能力。具体实施起来可以从以下两点入手：
高中英语教学中的亮点
高中英语教学中的亮点 ——浅谈情境教学 湖北襄阳一中杨向东 关键词：高中英语情景教学尝试
内容简介: 情境教学是英语教学的重要组成部分,英语教学的目的就是培养学生的英语语言素养，提高学生的英语实践能力。具体地讲，就是让学生在掌握英语语言基础知识的同时，通过听、说、读、写的训练使知识向能力转化。因此，英语教学的全过程就是教师创设情境，在情境中传授知识，组织学生进行语言操作，使学生通过大量练习培养语言习惯，建立语言系统的过程。 作者简介:杨向东,湖北省襄樊市襄阳一中高级教师,任教高中英语二十八年,担任过十年英语教研组长;现任学校政教处主任,高三年级主任;一直在教学第一线,在历届高考中取得优异成绩,获得湖北省优秀辅导教师, 襄樊市骨干教师,襄阳区优秀教师等荣誉称号.
高中英语教学中的亮点 ——浅谈情境教学 英语教学的目的就是培养学生的英语语言素养，提高学生的英语实践能力。具体地讲，就是让学生在掌握英语语言基础知识的同时，通过听、说、读、写的训练使知识向能力转化。因此，英语教学的全过程就是教师创设情境，在情境中传授知识，组织学生进行语言操作，使学生通过大量练习培养语言习惯，建立语言系统的过程。 一、为什么要进行情境教学 首先，情境化（contextulization）是英语教学的基本前提。只有实现情境化，才能实现英语教学。语境化的基本教学单位是在一定语境下的话语（discouse），脱离了语境，话语就不具备交际功能。比如：Thank you在一般情况下表示感谢，但是在某种情境中，它可以用来表示愤怒或拒绝帮助。Excuse me辞典上的解释是“对不起”，如果仅仅把此解释当作知识传授给学生，当他们处于交际环境时，仍然不知道到底哪种情况下的“对不起”是Excuse me. 其实，它主要用于求助生人时为了引起对方注意所用的提示语。 Excuse me, would you please tell me how I can go to the railway station? 它还可以表示要提前离开时告别前的提示语。 Excuse me, I am afraid I must be going. 表示打断别人的谈话 Excuse me for interrupting you, 表示举止失礼时表示道歉。如咳嗽，打喷嚏，写错字或演示失误要修改时说的提示语，此时不用别人的回答。 由此不难看出，只有在语境中语言的真实性才得以保证，语言的交际功能才能得以
浅谈高中英语教学中的新理念
浅谈高中英语教学中的新理念 发表时间：2011-10-28T11:32:51.637Z 来源：《现代教育科研论坛》2011年第10期供稿作者：杨晓[导读] 新课程标准倡导采用活动途径，让学生通过“自主、合作、探究”的方式参与学习。杨晓（永年县第二中学河北永年 057150） 新课程标准倡导采用活动途径，让学生通过“自主、合作、探究”的方式参与学习。这就需要教师转变教学观念，实现课堂教学与学生心灵的沟通，增进了师生之间、生生之间的互动、沟通和信任，让学生积极主动地探索知识，在互动交流里点燃知识的火花。 1.教学理念的确立 近年来，英语的教学模式发生了很大的改变，传统的呈现式教学正在逐步地向任务型教学模式转化。而我们现在所采用的新目标英语教材则正是为任务型教学而设计的一套新的教材。课堂是实施新课程改革的主战场，是全面推进素质教育的前沿阵地。面对新的教育形式，认真反思教学方面的不足之处。只有教学模式的改革，革新传统的教学模式，才能与现行教材相适应，才能与当代的教学模式相符。实践证明，只有把学习的主动权交给学生，充分发挥学生的主动权，给他们展示的机会，加强平等、合作、交流意识的培养，学生就会创造出奇迹。 2.“自主体验，展示互动”教学模式的确立和运用 课堂教学改革的核心是把课堂还给学生。我在此理念的支持下，结合本校的实际，通过理论学习、研究探讨、总结改进，确立了“三-五-四”任务型教学模式。基本思路:以学定教、合作探究，展示提升，四清巩固。“三”是指三大流程:预习指导-课堂教学-四清巩固。“五”是指课堂教学的五大环节:巧设引题——指导自学——合作探究——成果交流——巩固提升。“四”是四清巩固:即堂清——日清——周清——月清。 2.1预习指导；预习有利于培养学生的自学能力，提高课堂的效率。要求同学科教师要认真备课，共同设计《学案》。首先，要备教材。弄清本单元的教学目标，反复钻研教材。其次，要备学生。分析学生达到这个目标的难点是什么。并根据学生的学习基础，学习习惯、学习方法以及兴趣爱好，明确教学目标。同时把教学目标及时展示给学生，让学生心里有数，知道该做什么，如何去做。学生根据《学案》自学课本。独立完成，遇到问题，可以在小组内共同讨论解决。解决不了的，可以提交给教师。 2.2课堂教学；运用“自主体验，五环导学”的教学方法。由同学科教师共同研究设计教学《通案》。教学时间分配原则是“30+15”，即要求教师讲解小于15分钟，学生活动大于30分钟。①巧设引题。要求教师导入要具有趣味性和启发性，形成悬念。②自学指导。它是预习的进一步深化，教师要指导学生自学。③共同探究。此环节以小组的形式进行。对〈学案〉的学习在组内交流，合作学习。教师要认真地巡视，掌握学生的学习、探究情况。对出现的问题要及时解决。④成果交流。本环节是小组和小组之间的探究成果交流。互相学习和借鉴加深对知识的理解。要求学生展示他们的成果，实现学习的交流和共享。⑤巩固和提升。教师设计练习对内容进行及时巩固。引导学生以灵活多样的方式进行全面的回顾、整理和反思。一堂课的最终目标是让学生通过自主学习与合作探究掌握学习内容，完成学习目标。 2.3四清巩固；堂清和日清由任课教师负责；周清由学科组负责；月清就是月考，由学校统一组织。三大流程贯穿在一起，从课前、课上、到课后，构成了完整的自主学习模式。 3.体会 3.1教师观念的转变是第一位的。教学设计上要体现学生的主体地位。教学强调做中学(learningbydoing)。有的学生，学习英语时间不长，受到母语影响，容易在练习的过程中发生混淆。所以，在教学时，教师要注意中西方文化的差异。坚持以“学生为主体，教师为辅导，训练为主线”的三位一体的教学模式。 3.2课堂教学应努力引导学生从被动接受知识，转为主动探索知识，变“学会”为“会学”。要做到这点必须在培养学生的学习兴趣和动脑、动口、动手上下功夫，让他们主动参与教学活动，以调动他们的学习积极性和主观能动性，形成以学生思维为核心，能力为目标，育人为目的的素质教育教学模式。 3.3采用各种方法激发学习兴趣。如编口诀。教师可编些口诀来帮助学生记忆，降低学习难度，使学英语的热情升温。教师只要常收纳，多积累，勤总结，口诀就会涌涌不断，教学也定会趣味横生。 3.4在课堂上，任务布置下去后，开展小组竞争的形式，以提高学生的参与性和合作性。获胜的小组成员可以和作业，测试当中的优秀学生一起参加每月英语之星考评，并及时奖励，以提高学生学习的积极性。 3.5在知识传授环节，能采取多样的形式，根据学生的特点，兴趣进行活动，如两人活动，小组活动，模拟对话，扮演角色，讲故事，述大意，变对话为叙述，变叙述为对话，即兴口头作文，看图说话，组句成文等。 3.6教学中可经常穿插情感教育。如在教生日时，可加入给父母制作生日卡环节，在教一日活动时，可以叫学生自己体会忙碌但快乐的一天中，自己可以从事哪些活动等。设计活动要有明确的教学目的和可操作性。 实践证明，该教学模式充分考虑了中学生的生动活泼和强烈的好奇心、表现欲，极大地调动学生的学习积极性，激发学生的学习兴趣，培养学生的个性，提高学生各方面的素质，是一种行之有效的教学方法。坚持“自主、合作、探究”的教学理念，提高教学效率，既注重“双基”的落实，又着眼于学生的发展和能力的提高，才是素质教育的根本所在。