The CCAF Model is another model of instruction used by teachers in both online and traditional classrooms. Acronym stands for
C-ontext
C-hallenge
A-ctivities
F-eedback
This post will discuss each of these characteristics.
Context
Context is about establishing a setting in which the learning is relevant for the learners. This means developing real-world connections in the lesson so that students can see ways of application.
For example, if you are required to teaching a heavily theoretical course such as educational philosophy, establishing a context may mean showing how the various philosophy of education impact how teachers make decisions. You may also want to articulate how your own beliefs affect how you develop classes.
Challenge
Challenging students is the same as engaging them. Assignments need to be stimulating enough that students have to work somewhat to complete them. This step has a great deal to do with motivation and overlaps with the previous step of context.
The main difference here is that at the challenge stage the students should be actively engaged in doing something. With context, the teacher is laying the foundation for the learning.
Activities
Activities are an extension of challenge. Activities need to be risk-free in order to allow students to learn from mistakes without fear of this lowering their grade. This step of the CCAF Model is similar to the practice step of other models.
The activities can also include interaction with peers through group experiences. This allows for you another form of communication in relation to progress in achieving learning goals.
Feedback
While the students are engaged in challenging activities, this provides you as the teacher with opportunities to provide feedback on performance. Constant feedback helps students to know where they are at and how they are doing.
The feedback can take many shapes. It could be verbal encouragement, non-verbal approval, written, etc. The goal is to keep students in the loop in terms of their performance.
Conclusion
The CCAF model is a model that is focused on execution and is highly student-centered in terms of the activity level. After the context is set, the students are constantly engaged with doing various tasks and receiving feedback. This emphasis on action is what allows the students to be able to retain what they learn and call upon this knowledge in an authentic situation when they enter the workplace.
The ASSURE instructional design model is yet another approach to conceptualizing the teaching experience. This model has the following steps
A — Analyze learners
S — State standards & objectives
S — Select strategies, technology, media & materials
U — Utilize technology, media & materials
R — Require learner participation
E — Evaluate & revise
In this post, we will look at each aspect of the ASSURE model.
Analyze Learners
Analyzing learners is in many ways another term for conducting a needs analysis. A teacher needs to know the skills and abilities of the students they are work with in order to determine where the students need to go. Any form of pre-assessment or communication with the students can provide information for analyzing the students.
Standards and Objectives
Once you know where the students are, you need to see how you can incorporate government standards and objectives if necessary. In education, there is a balancing act between the needs of the students and government requirements. This step makes you aware of this balancing act.
Select Strategies
With the ideas of the content settled, it is now time to determine the activities that will be used to facilitate learning. How this is done depends on the students’ needs and the governmental requires as well as the preferences of the teacher.
Utilize Technology
Incorporating the use of technology is one of the distinct traits of the ASSURE model. How this is done is again up to the teacher. The point is that if someone is an adopter of the ASSURE model it implies some use of technology.
Require Student Participation
Students need to be active learners in the classroom. This natural means having activities that provide opportunities for engagement. This can happen through using technology or by other means.
Evaluate & Revise
Evaluating happens with the assessment that comes after the learning experience. It allows the teacher to see if the students have demonstrated mastery of the content. The options for doing this depend on how the class was developed.
Conclusion
The ASSURE model provides an alternative approach to the setting of the learning environment of a classroom. Keeping in mind these components can guide teachers in preparing lessons that are beneficial to the students.
Decision trees are used for classifying examples into distinct classes or categories. Such as pass/fail, win/lose, buy/sell/trade, etc. However, as we all know, categories are just one form of outcome in machine learning. Sometimes we want to make numeric predictions.
The use of trees in making predictions numeric involves the use of regression trees or model trees. In this post, we will look at each of these forms of numeric prediction with the use of trees.
Regression Trees and Modal Trees
Regression trees have been around since the 1980’s. They work by predicting the average value of specific examples that reach a given leaf in the tree. Despite their name, there is no regression involved with regression trees. Regression trees are straightforward to interpret but at the expense of accuracy.
Modal trees are similar to regression trees but employ multiple regression with the examples at each leaf in a tree. This leads to many different regression models being used to split the data throughout a tree. This makes model trees hard to interpret and understand in comparison to regression trees. However, they are normally much more accurate than regression trees.
Both types of trees have the goal of making groups that are as homogeneous as possible. For decision trees, entropy is used to measure the homogeneity of groups. For numeric decision trees, the standard deviation reduction (SDR) is used. The detail of SDR are somewhat complex and technical and will be avoided for that reason.
Strengths of Numeric Prediction Trees
Numeric prediction trees do not have the assumptions of linear regression. As such, they can be used to model non-normal and or non-linear data. In addition, if a dataset has a large number of feature variables, a numeric prediction tree can easily select the most appropriate ones automatically. Lastly, numeric prediction trees also do not need the model to be specific in advance of the analysis.
Weaknesses of Numeric Prediction Trees
This form of analysis requires a large amount of data in the training set in order to develop a testable model. It is also hard to tell which variables are most important in shaping the outcome. Lastly, sometimes numeric prediction trees are hard to interpret. This naturally limits there usefulness among people who lack statistical training.
Conclusion
Numeric prediction trees combine the strength of decision trees with the ability to digest a large amount of numerical variables. This form of machine learning is useful when trying to rate or measure something that is very difficult to rate or measure. However, when possible, it is usually wise to allow to try to use simpler methods if permissible.
There are several ways in which curriculum design can happen. For example, forward design aka the Tyler method involves the selection of content, followed by determining teaching approaches, and finally determining the quality of the teaching and content based on some form of assessment.
Backward design starts with content but then the focus moves to developing assessment that is consistent with the content, the last step for backward design is deciding how to teach the content in a way that allows students to develop the skills need to demonstrate understanding through successfully completing the assessment
Central design is yet another distinct approach. In this post, we will look at the characteristics of central design and how it differs from both forward and backward design.
Definition
Central design begins with deciding on the teaching approach first followed by the content and assessment. In this approach, it is the method of teaching that is most important. There is an assumption among teachers who use this approach that the method of teaching along with the supporting activities will lead to successful learning outcomes or demonstrations of mastery.
Central design is highly fixated on learning processes. For example, there is an emphasis on discussion, decision-making, critical thinking, etc. All of these examples are somewhat fuzzy in being able to assess them. We can tell when they happen but it’s not easy to place a score on them because these are subjective skills.
For many, this design is seen as learner-centered due to its emphasis on active learning. Discussion requires active learning as do critical thinking and the other examples in the previous paragraph. These experiences contribute to the individual development of the students.
Some Concerns
Despite the advantages of central design, there are some concerns. In order to place an emphasis on teaching methods, it implies that the teacher is a mastery of one or more methodologies. This makes central design difficult for beginning teachers to execute.
In addition, for even experienced teachers, lack of objectives can make it very easy to wonder of course when teaching. For example, whenever teachers select activities that look fun or entertaining they are practicing central design because of the emphasis on teaching activities. However, with a lack of clear objectives, students are having a good time with being able to articulate what they are learning.
The issue with objectives can also spill over into affecting the assessment. Without clear goals, it is difficult to determine what the students learned or if they achieved any of the goals and objectives of a lesson. With so much testing taking place these days it is difficult to justify such a design.
Using Central Design
Central design is highly useful in the social sciences and humanities. Such classes as critical thinking, public speaking, art appreciation are some examples of courses that can employ central design due to the subjective nature of completing course requirements. For the hard sciences, it might be better to stick to forward or backward design due to the need to absolute know specific forms of information.
In general, if the primary goal is developing subjective skills central design is an excellent choice. However, if what you are trying to teach can clearly be measured and evaluated forward or backward design is much more appropriate choice
In this post, we are going to analyze some data in order to make some classification rules. Specifically, we are going to look at the “Males” dataset from the “Ecdat” package. Our goal is to make some rules that explain when a male is married or not. Below is the code to load the needed packages as well as the dataset “Males”
library(Ecdat)
library(RWeka)data("Males")
1. Explore the Data
The first step as always is to explore the data. We need to determine what kind of variables we are working with by using the “str” function
The first two variables “nr” and “year” are not too useful for us. “nr” is an identification number and “year” is the year the data was collected. We shouldn’t expect much change in marriage rates over a few years and the identification number has no meaning in our analysis. So we will ignore these two variables.
Nex, we visualize the data with some tables and histograms. Integer variables will be visualized with histograms and factor variables with tables. The code and results are below.
prop.table(table(Males$maried))
##
## no yes
## 0.5610092 0.4389908
prop.table(table(Males$union))
##
## no yes
## 0.7559633 0.2440367
prop.table(table(Males$ethn))
##
## other black hisp
## 0.7284404 0.1155963 0.1559633
There is no time or space to explain the tables and histograms in detail. Only two things are worth mentioning. 1. Are “maried” are classifiying variable is mostly balance between those who are married and those who are not (56% to 44%) 2. The “health” variable is horribly unbalanced and needs to be removed (98% no vs 2% yes).
2. Develop and Evaluate the Model
We can now train our first model. The first model will be a one rule model, which means that R will develop one rule for classification purposes. In this post, we are not doing any prediction since we simply want to make a rule for the sample data. Below is the code.
We used the “OneR” function to create the model. This function analyzes the data and makes a single rule for it. We will now evaluate the model be first looking at the rule that was generated.
The “exper” variable was selected for generating the rule. To state the rule clearly it literally it means “If a man has lest than 7.5 years of experience he is not married if he has more than 7.5 years of experience but less than 12.5 years of experience he is married, if he has more than 12.5 years of experience but less than 13.5 years of experience he is not married, and if he has more than 13.5 years of experience he is married”
Explaining this can take many interpretations. Young guys have less experience so they aren’t ready to marry. After about 8 years they marry. However, after about 12 years of experience males are suddenly not married. This is probably due to divorce. After 13 years, the typical male is married again. This may be because his marriage survived the scary 12th year or may be due to remarriage.
However, as we look at the accuracy of the model we will see some problems a you will notice below after typing in the following code
summary(Males_1R)
##
## === Summary ===
##
## Correctly Classified Instances 1973 63.3387 %
## Incorrectly Classified Instances 1142 36.6613 %
## Kappa statistic 0.2287
## Mean absolute error 0.3666
## Root mean squared error 0.6055
## Relative absolute error 75.2684 %
## Root relative squared error 122.6945 %
## Coverage of cases (0.95 level) 63.3387 %
## Mean rel. region size (0.95 level) 50 %
## Total Number of Instances 3115
##
## === Confusion Matrix ===
##
## a b <-- classified as
## 1351 457 | a = no
## 685 622 | b = yes
We only correctly classified 63% of the data. This is pretty bad. Perhaps if we change our approach and develop more than one rule we will have more success.
We will now use the “JRip” function to develop multiple classification rules. Below is the code.
## JRIP rules:
## ===========
##
## (exper >= 7) and (occupation = Craftsmen, Foremen_and_kindred) and (school >= 9) and (residence = south) and (exper >= 8) and (union = yes) => maried=yes (28.0/3.0)
## (exper >= 6) and (exper >= 8) and (school >= 11) and (ethn = other) => maried=yes (649.0/238.0)
## (exper >= 6) and (residence = south) and (ethn = hisp) => maried=yes (102.0/36.0)
## (exper >= 5) and (school >= 14) and (school >= 15) => maried=yes (76.0/25.0)
## (exper >= 5) and (ethn = other) and (occupation = Craftsmen, Foremen_and_kindred) => maried=yes (232.0/93.0)
## => maried=no (2028.0/615.0)
##
## Number of Rules : 6
There are six rules all together below is there meaning
If a male has seven years are more of experience, is a craftsmen or foreman, has at least nine years of school, and his ethnicity is other then he is married.
If a male has at least 6 years of experience, has at least 11 years of school and his ethnicity is Hispanic then he is married.
If a male has at least 6 years of experience, resides in the south, and his ethnicity is Hispanic then he is married.
If a male has at least 5 years of and has at least 14 years of school then he is married.
If a male has at least 5 years of experience, his ethnicity is other, and his occupation is craftsmen or foremen then he is married
If not any of these then he is not married
Notice how all rules begin with “exper” this is one reason why the “OneR” function made its rule on experience. Experience is the best predictor of marriage in this dataset. However, are accuracy has not improve much as you will see in the following code.
summary(Males_JRip)
##
## === Summary ===
##
## Correctly Classified Instances 2105 67.5762 %
## Incorrectly Classified Instances 1010 32.4238 %
## Kappa statistic 0.3184
## Mean absolute error 0.4351
## Root mean squared error 0.4664
## Relative absolute error 89.3319 %
## Root relative squared error 94.5164 %
## Coverage of cases (0.95 level) 100 %
## Mean rel. region size (0.95 level) 100 %
## Total Number of Instances 3115
##
## === Confusion Matrix ===
##
## a b <-- classified as
## 1413 395 | a = no
## 615 692 | b = yes
We are only at 67% which is not much better. Since this is a demonstration the actually numbers do not matter as much.
Conclusion
Classification rules provide easy to understand rules for organizing data homogeneously. This yet another way to analyze data with machine learning approaches.
Teaching involves the use of various techniques in order to convey meaning for the students. The available methods that are available are highly varied. In this post, we will look at the use of examples and nonexamples in providing meaning for students.
Example
The term many of us are probably familiar with is example. In education, examples represent an idea or concept that a teacher is trying to teach their students.For example (no pun intended), if a teacher is trying to explain vocabulary they may use several different illustrations to explain the word. Consider the example below.
Teacher: Today’s vocab word is convoluted. Convoluted means something that is complicated. For example, the human body is very convoluted with all of its cells and systems.
This example above brief an illustration of the use of examples. Examples provide synonyms or other means of similarity with the unclear concept. Therefore, an example is always like or similar to whatever it is an example of.
Nonexample
Nonexamples are, as you can tell, the opposite of examples.Where examples provide an instance of similarity, nonexamples provide an instance of contrast. Below is the same situation with the use of convoluted is a sentence but this time the teacher shows the meaning through employing a nonexample.
Teacher: Today’s vocab word is convoluted. Convoluted means something that is complicated. Something that is not convoluted would be a rock or a ladder.
The example in the last sentence is an example of what convoluted is not. The contrast helps students to envision what the word is not and to develop their own ideas of what the word is.
Teaching Ideas for Examples and Nonexamples
Depending on the teaching method there are many practical ways to use examples and nonexamples. If direct instruction is used, it would be the teacher who provides the examples and nonexamples. If indirect instruction is employed, the students create the examples and none examples. In cooperative or inquiry classrooms, small groups develop examples and nonexamples.
For whatever reason, it is normally easier to develop examples rather than develop non-examples. The mind seems better adapted at seeing similarities rather than differences. For this reason, challenging students to develop nonexamples, may stretch their thinking more.
As a teacher, it is probably best to develop examples and nonexamples before teaching that are consistent with the goals and objectives of the learning experience. It’s difficult to create great teaching strategies while in front of the students. A methodological approach to developing teaching tools is always valuable.
Conclusions
Examples and nonexamples are tools that most teachers have been using without perhaps knowing it. This is especially true for examples. However, understanding how and why the tools work is highly beneficial in inspiring informed practice.
Robert Gagne was a psychologist in the field of education. One of his most influential ideas was his Nine Events of Instruction. The concept has had a significant impact in the instructional approach of many in the world of education.
This post will briefly explain and cover the Nine Events of Instruction and to explain their application in the classroom. The nine events are as follows.
Gain learners’ attention.
Inform learners of the objectives.
Stimulate recall of prior learning.
Present the content.
Provide “learning guidance”
Elicit performance (practice)
Provide feedback.
Assess performance.
Enhance retention and transfer to the real-world
Gain Learners Attention
Obtaining attention is critical in terms of information processing. Unfocused students cannot learn anything. How a teacher gains the attention of their students can vary. Some use classroom management techniques to obtain behavior such as ringing a bell or raising their hand to indicate that it is time to be quiet.
Inform Learners of the Objectives
It is hard for many to enjoy a journey when they do not know where they are going. The same idea applies to many students. You need to explain to them what they will do in order for them to enjoy doing it. This is one reason for sharing with the students the objectives or purpose of a class. It provides a sense of direction and perhaps relevance.
Stimulate Prior Learning
Stimulating prior learning allows students to connect new information with old. Review what they have learned in order to extend and build upon it. This is one aspect of constructivism. The review can be in the form of questions, game or some other method. Students need to see the connections among the information they are learning for schematic reasons as well.
Present Content
This the part of the teaching in which new material is presented. This can be done through any method of teaching including direct instruction, indirect instruction, cooperative learning, etc.
Provide Guidance
After learning new material, students need to use it. This first happens with a hands-on example with guidance. In other words, the first few problems are done together with teacher support. This is the scaffolding aspect of Vygotsky’s model. You as the teacher guide the students through the initial experience of using new information.
Elicit Performance
At this step, the students are executing the new skill without immediate feedback. Students need the freedom to perform without instant critique even from the teacher. However, this is only temporary.
Provide Feedback
Now the students learn how they did. This can happen through going over the answers or discuss various opinions about a subjective subject. This event provides students with a way to compare their performance with that of others or some external standard.
Assess Performance
This is the giving of some sort of grade or indication of progress. There are several different methods for giving marks or grades.
Enhance Retention through Transfer to Real World
Students need to see how the knowledge they attain can be used in the real world. Therefore, the teacher needs to assist in this transfer. This can be through discussion on how to do this or through the use of some sort of authentic assessment.
Conclusion
Gagne’s Nine Events of Instruction is a fantastic model to follow when trying to teach and interact with students. The order is the most common flow and there are natural exceptions to the order developed by Gagne. However, a teacher chooses to do this they should keep in mind the nine events in order to support student learning.
Community Language Learning (CLL) is a humanistic approach to language learning based on psychological insights of Carl Rogers. The role of the teacher shifts to that of a counselor and the role of the student shifts to that of a client. The difference is that in CLL the counselor is a knower and the client is a learner.
This post will discuss the beliefs of CLL as well as its curriculum.
The Philosophy
CLL is based on interaction between learners and between learners and knowers. The goal is to strengthen social ties in order to establish a community. This is defined as intimacy in CLL lingo.
The interaction between learners and knowers goes through five stages.
The learner explains what they want to say
He tries to become self-assertive without success
The learner becomes resentful of their dependency
The learner becomes tolerant of their dependency
The learner becomes independent
This five-stage process is based on the development of babies as the move from helplessness to independence.
The roles of teachers and students has already been alluded too. Learning is viewed as collaborative in CLL. This explains why learners are consistently working together. The learners need to move from one affective crisis to another. These crises are what encourage development in the language skills of the learners. A crisis is any challenge that pushes the learners.
The teacher’s role, in addition to being a knower, is to provide a stable learning environment in which learners collaborate. In addition, the teacher provides the various affective crises in order to encourage learning.
Curriculum
The primary goal of CLL is oral proficiency. As such, interaction is a primary characteristic of a CLL curriculum. Common activities in a CLL classroom include conversation, listening, translating, and transcribing.
Materials are developed by the teacher and are suited for the local context. The actual procedures vary and are not agreed upon among proponents of CLL.
Conclusion
CLL is an approach that is focused on providing students with an opportunity to learn from each other and the teacher. The environment is one in which learners are supported by a knower who provides guidance and language knowledge to the students.
Classification rules represent knowledge in an if-else format. These types of rules involve the terms antecedent and consequent. The antecedent is the before and consequent is after. For example, I may have the following rule.
If the students studies 5 hours a week then they will pass the class with an A
This simple rule can be broken down into the following antecedent and consequent.
Antecedent–If the student studies 5 hours a week
Consequent-then they will pass the class with an A
The antecedent determines if the consequent takes place. For example, the student must study 5 hours a week to get an A. This is the rule in this particular context.
This post will further explain the characteristics and traits of classification rules.
Classification Rules and Decision Trees
Classification rules are developed on current data to make decisions about future actions. They are highly similar to the more common decision trees. The primary difference is that decision trees involve a complex step-by-step process to make a decision.
Classification rules are stand-alone rules that are abstracted from a process. To appreciate a classification rule you do not need to be familiar with the process that created it. While with decision trees you do need to be familiar with the process that generated the decision.
One catch with classification rules in machine learning is that the majority of the variables need to be nominal in nature. As such, classification rules are not as useful for large amounts of numeric variables. This is not a problem with decision trees.
The Algorithm
Classification rules use algorithms that employ a separate and conquer heuristic. What this means is that the algorithm will try to separate the data into smaller and smaller subset by generating enough rules to make homogeneous subsets. The goal is always to separate the examples in the data set into subgroups that have similar characteristics.
Common algorithms used in classification rules include the One Rule Algorithm and the RIPPER Algorithm. The One Rule Algorithm analyzes data and generates one all-encompassing rule. This algorithm works by finding the single rule that contains the less amount of error. Despite its simplicity, it is surprisingly accurate.
The RIPPER algorithm grows as many rules as possible. When a rule begins to become so complex that in no longer helps to purify the various groups the rule is pruned or the part of the rule that is not beneficial is removed. This process of growing and pruning rules is continued until there is no further benefit.
RIPPER algorithm rules are more complex than One Rule Algorithm. This allows for the development of complex models. The drawback is that the rules can become too complex to make practical sense.
Conclusion
Classification rules are a useful way to develop clear principles as found in the data. The advantage of such an approach is simplicity. However, numeric data is harder to use when trying to develop such rules.
Dealing with people or students involves dealing with personalities. Everyone has a unique personality that has varying degrees of similarities and difference from others. Those post will introduce some commonly proposed factors that shape and influence personality.
Genetics
There is evidence that our personality is affected by our genetics. Thomas Bouchard published a paper on twins separated at birth and found that despite never meeting each other, the twins had very similar personalities. Indicating that there is more to personality than common family experiences.
It is no longer a question of if genetics influences personality but rather how much genetics influences personality. This has led to the controversy called nativism-empiricism, which is a scholarly term for nature vs nurture. Nativists believe that personality is primarily genetically determined whereas empiricists believe that personality is primarily determined by experience.
Traits
Traits can be defined loosely as consistently displayed or performed behaviors. For example, some people show the trait of a love of sweets while others do not. The person with the trait for loving sweets will consistently enjoy eating sweet foods.
Traits are developed one of two ways. One way is through learning. For example, a person who loves sweet food may have been exposed to sweet food since they were born. Thus, the acquired a taste for it.
The second way is by genetics. For example, some people display are more emotional than others regardless of their background. One explanation of this is that their emotional character traits are a result of their genetic makeup.
Culture
Culture influences personality through prescribing what behaviors are acceptable. For example, different cultures have different rules in regard to marriage, raising children, food, money, etc. These norms restrain and promote various behaviors.
Again the argument here is not whether or not culture plays a role but rather how much of a role. Some personality theories believe culture is significantly important such as Erickson, Alder, and Horney.
Strange Assertion
Some of the more unusual proposed determinants of personality include the existential-humanistic considerations and unconscious mechanisms. Existential-humanistic considerations believe personality is shaped by how people give meaning to the situations they find themselves in. In other words, the individual shapes their own destiny by how they answer the big questions in life such as why am I here?, what happens when I die?
Unconscious mechanisms assert that personality is shaped by unconscious forces in childhood. Uncovering these forces involves the use of such techniques as hypnosis and dream analysis.
Conclusion
This post provided some explanations for how personalities are shaped and formed. Naturally, there are many more reasons for why people behave the way they do. However, the information provided here provides an introductory insight into why people act the way they do.
Total Physical Response (TPR) is another lesser known method of teaching language. It relies on speech and action to help students to acquire the language. In this post, we will look at the background, assumptions, and curriculum approach of this method.
Background
TPR is based on a theory in psychology called trace theory. Trace theory proposes that the more frequently a memory connection is made the easier it is to recall it. For example, if a student is given the verbal command “stand up” enough times, they will quickly learn what “stand up” means.
This, of course, assumes that the student eventual understands what “stand up” means. This assumption of comprehension is based on the Comprehension Approach. This approach states that people understand something before they can reproduce it verbally.
Anyone who has ever seen a toddler can attest to this theory. A toddler can obey commands much earlier than they can speak.
Assumptions
TPR is heavily based on imperatives as they are easy to understand as they are non-abstract. For example, it is easier to tell someone to sit down (non-abstract) than to ask them why they think rice is the best food (abstract).
TPR also takes a lot of assumptions from behaviorism and the concepts of stimulus-response. The continuous repetition of command and execution allows for the acquisition of language. Just as we see in children.
The teacher’s role is to be the center of the classroom. This is because they are the ones providing the imperatives for the students. The teacher does not really teach but provides learning opportunities and feedback for the students. The learner’s role is primarily as a listener who becomes a performer.
Curriculum
The primary goal in TPR is to teach oral and listening skills. The teacher provides a large number of imperatives that the students execute, often over the first 120 hours. Eventually, the students should be using the imperatives with each other.
It is usually up to the teacher to develop the activities as there are few if any books for classroom use involving TPR. As such, TPR is a useful part of a larger learning experience and probably should not be used exclusively.
Conclusion
TPR is fun for getting students out of their chairs and experiencing language. However, it is limited in developing deeper language and communication skills. As such, TPR can be used for adding variety and stimulation but other approaches and methods are useful if the goal of the students is more than just lower-level communication.
