Monthly Archives: January 2017

Factors that Affect Pronunciation

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Understanding and teaching pronunciation has been controversial in TESOL for many years. At one time, pronunciation was taught in a high bottom-up behavioristic manner. Students were drilled until they had the appropriate “accent” (American, British, Australian, etc.). To be understood meant capturing one of the established accents.

Now there is more of an emphasis on top-down features such as stress, tone, and rhythm. There is now an emphasis on being more non-directive and focus not on the sounds being generated by the student but the comprehensibility of what they say.

This post will explain several common factors that influence pronunciation. These common factors include

  • Motivation & Attitude
  • Age & Exposure
  • Native language
  • Natural ability

Motivation & Language Ego

For many people, it’s hard to get something done when they don’t care. Excellent pronunciation is often affected by motivation. If the student does not care they will probably not improve much. This is particularly true when the student reaches a level where people can understand them. Once they are comprehensible many students lose interests in further pronunciation development

Fortunately, a teacher can use various strategies to motivate students to focus on improving their pronunciation. Creating relevance is one way in which students intrinsic motivation can be developed.

Attitude is closely related to motivation. If the students have negative views of the target language and are worried that learning the target language is a cultural threat this will make language acquisition difficult. Students need to understand that language learning does involve learning of the culture of the target language.

Age & Exposure

Younger students, especially 1-12 years of age, have the best chance at developing native-like pronunciation. If the student is older they will almost always retain an “accent.” However, fluency and accuracy can achieve the same levels regards of the initial age at which language study began.

Exposure is closely related to age. The more authentic experiences that a student has with the language the better their pronunciation normally is. The quality of the exposure is the naturalness of the setting and the actual engagement of the student in hearing and interacting with the language.

For example, an ESL student who lives in America will probably have much more exposure to the actual use of English than someone in China. This, in turn, will impact their pronunciation.

Native Language

The similarities between the mother tongue and the target language can influence pronunciation. For example, it is much easier to move from Spanish to English pronunciation than from Chinese to English.

For the teacher, understanding the sound system’s of your students’ languages can help a great deal in helping them with difficulties in pronunciation.

Innate Ability

Lastly, some just get it while others don’t. Different students have varying ability to pick up the sounds of another language. A way around this is helping students to know their own strengths and weaknesses. This will allow them to develop strategies to improve.

Conclusion

Whatever your position on pronunciation. There are ways to improve your students’ pronunciation if you are familiar with what influences it. The examples in this post provided some basic insight into what affects this.

Tips for Developing Techniques for ESL Students

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Technique development is the actual practice of TESOL. All of the ideas expressed in approaches and methods are just ideas. The development of a technique is the application of knowledge in a way that benefits the students. This post would provide ideas and guidelines for developing speaking and listening techniques.

Techniques should Encourage Intrinsic Motivation

When developing techniques for your students. The techniques need to consider the goals, abilities, and interest of the students whenever possible. If the students are older adults who want to develop conversational skills heavy stress on reading would be demotivating. This is because reading was not on of the student’s goals.

When techniques do not align with student goals there is a loss of relevance, which is highly demotivating. Of course, as the teacher, you do not always give them what they want but general practice suggests some sort of dialog over the direction of the techniques.

Techniques should be Authentic

The point here is closely related to the first one on motivation. Techniques should generally be as authentic as possible. If you have a choice between real text and textbook it is usually better to go with real-world text.

Realistic techniques provide a context in which students can apply their skills in a setting that is similar to the world but within the safety of a classroom.

Techniques should Develop Skills through Integration and Isolation

When developing techniques there should be a blend of techniques that develop skill in an integrated manner, such as listening and speaking and or some other combination. There should also be an equal focus on techniques that develop on one skill such as writing.

The reason for this is so that the students develop balanced skills. Skill-integrated techniques are highly realistic but students can use one skill to compensate for weaknesses in others. For example, a talker just keeps on talking without ever really listening.

When skills our work on in isolation it allows for deficiencies to be clearly identified and work on. Doing this will only help the students in integrated situations.

Encourage Strategy Development

Through techniques, students need to develop their abilities to learn on their own autonomously. This can be done through having students practice learning strategies you have shown them in the past. Examples include context clues, finding main ideas, identifying  facts from opinions etc

The development of skills takes a well-planned approach to how you will teach and provide students with the support to succeed.

Conclusion

Understanding some of the criteria that can be used in creating techniques for the ESL classroom is beneficial for teachers. The ideas presented here provide some basic guidance for enabling technique development.

Generalized Additive Models in R

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In this post, we will learn how to create a generalized additive model (GAM). GAMs are non-parametric generalized linear models. This means that linear predictor of the model uses smooth functions on the predictor variables. As such, you do not need to specify the functional relationship between the response and continuous variables. This allows you to explore the data for potential relationships that can be more rigorously tested with other statistical models

In our example, we will use the “Auto” dataset from the “ISLR” package and use the variables “mpg”,“displacement”,“horsepower”, and “weight” to predict “acceleration”. We will also use the “mgcv” package. Below is some initial code to begin the analysis

library(mgcv)
library(ISLR)
data(Auto)

We will now make the model we want to understand the response of “acceleration” to the explanatory variables of “mpg”,“displacement”,“horsepower”, and “weight”. After setting the model we will examine the summary. Below is the code

model1<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight),data=Auto)
summary(model1)
## 
## Family: gaussian 
## Link function: identity 
## 
## Formula:
## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.54133    0.07205   215.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                   edf Ref.df      F  p-value    
## s(mpg)          6.382  7.515  3.479  0.00101 ** 
## s(displacement) 1.000  1.000 36.055 4.35e-09 ***
## s(horsepower)   4.883  6.006 70.187  < 2e-16 ***
## s(weight)       3.785  4.800 41.135  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.733   Deviance explained = 74.4%
## GCV = 2.1276  Scale est. = 2.0351    n = 392

All of the explanatory variables are significant and the adjust r-squared is .73 which is excellent. edf stands for “effective degrees of freedom”. This modified version of the degree of freedoms is due to the smoothing process in the model. GCV stands for generalized cross-validation and this number is useful when comparing models. The model with the lowest number is the better model.

We can also examine the model visually by using the “plot” function. This will allow us to examine if the curvature fitted by the smoothing process was useful or not for each variable. Below is the code.

plot(model1)

We can also look at a 3d graph that includes the linear predictor as well as the two strongest predictors. This is done with the “vis.gam” function. Below is the code

vis.gam(model1)

If multiple models are developed. You can compare the GCV values to determine which model is the best. In addition, another way to compare models is with the “AIC” function. In the code below, we will create an additional model that includes “year” compare the GCV scores and calculate the AIC. Below is the code.

model2<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight)+s(year),data=Auto)
summary(model2)
## 
## Family: gaussian 
## Link function: identity 
## 
## Formula:
## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) + 
##     s(year)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.54133    0.07203   215.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                   edf Ref.df      F p-value    
## s(mpg)          5.578  6.726  2.749  0.0106 *  
## s(displacement) 2.251  2.870 13.757 3.5e-08 ***
## s(horsepower)   4.936  6.054 66.476 < 2e-16 ***
## s(weight)       3.444  4.397 34.441 < 2e-16 ***
## s(year)         1.682  2.096  0.543  0.6064    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.733   Deviance explained = 74.5%
## GCV = 2.1368  Scale est. = 2.0338    n = 392
#model1 GCV
model1$gcv.ubre
##   GCV.Cp 
## 2.127589
#model2 GCV
model2$gcv.ubre
##   GCV.Cp 
## 2.136797

As you can see, the second model has a higher GCV score when compared to the first model. This indicates that the first model is a better choice. This makes sense because in the second model the variable “year” is not significant. To confirm this we will calculate the AIC scores using the AIC function.

AIC(model1,model2)
##              df      AIC
## model1 18.04952 1409.640
## model2 19.89068 1411.156

Again, you can see that model1 s better due to its fewer degrees of freedom and slightly lower AIC score.

Conclusion

Using GAMs is most common for exploring potential relationships in your data. This is stated because they are difficult to interpret and to try and summarize. Therefore, it is normally better to develop a generalized linear model over a GAM due to the difficulty in understanding what the data is trying to tell you when using GAMs.

Listening Techniques for the ESL Classroom

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Listening is one of the four core skills of language acquisition along with reading, writing, and speaking. This post will explain several broad categories of listening that can happen within the ESL classroom.

Reactionary Listening

Reactionary listening involves having the students listen to an utterance and repeat back to you as the teacher. The student is not generating any meaning. This can be useful perhaps for developing pronunciation in terms of speaking.

Common techniques that utilize reactionary listening are drills and choral speaking. Both of these techniques are commonly associated with audiolingualism.

Responsive Listening

Responsive listening requires the student to create a reply to something that they heard. Not only does the student have to understand what was said but they must also be able to generate a meaningful reply. The response can be verbal such as answering a question and or non-verbal such as obeying a command.

Common techniques that are responsive in nature includes anything that involves asking questions and or obeying commands. As such, almost all methods and approaches have some aspect of responsive listening in them.

Discriminatory Listening

Discriminatory listening techniques involve listening that is selective. The listener needs to identify what is important from a dialog or monologue. The listener might need to identify the name of a person, the location of something, or develop the main idea of the recording.

Discriminatory listening is probably a universal technique used by almost everyone. It is also popular with English proficiency test such as the IELTS.

Intensive Listening

Intensive listening is focused on breaking down what the student has heard into various aspect of grammar and speaking. Examples include intonation, stress, phonemes, contractions etc.

This is more of an analytical approach to listening. In particular, using intensive listening techniques may be useful to help learners understand the nuances of the language.

Extensive Listening

Extensive listening is about listening to a monologue or dialog and developing an overall summary and comprehension of it.  Examples of this could be having students listening to a clip from a documentary or a newscast.

Again, this is so common in language teaching that almost all styles incorporate this in one way or another.

Interactive Listening

Interactive listening is the mixing of all of the previously mentioned types of listening simultaneously. Examples include role plays, debates, and various other forms of group work.

All of the examples mentioned require repeating what others say (reactionary), replying to others comments (responsive),  identifying main ideas (discriminatory & extensive), and perhaps some focus on intonation and stress (intensive).  As such, interactive listening is the goal of listening in a second language.

Interactive listening is used by most methods most notable communicative language  teaching, which has had a huge influence on the last 40 years of TESOL.

Conclusion

The listening technique categories provided here gives some insight into how one can organize various listening experiences in the classroom. What combination of techniques to employ depends on many different factors but knowing what’s available empowers the teacher to determine what course of action to take.

Using a Rubric to Mark an Assignment in Moodle VIDEO

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This video provides an example of using a rubric in Moodle

Wire Framing with Moodle

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Before teaching a Moodle course it is critical that a teacher design what they want to do. For many teachers, they believe that they begin the design process by going to Moodle and adding activity and other resources to their class. For someone who is thoroughly familiar with Moodle and has developed courses before this might work. However, for the majority online teachers, they need to wireframe what they want their Moodle course to look like online.

Why Wireframe a Moodle Course

In the world of web developers, a wireframe is a prototype of what a potential website will look like. The actual wireframe can be made in many different platforms from Word, Powerpoint, and even just paper and pencil. Since Moodle is online a Moodle course in many ways is a website so wireframing applies to this context.

It doesn’t matter how you wireframes their Moodle course. What matters is that you actually do this. Designing what you want to see in your course helps you to make decisions much faster when you are actually adding activities and resources to your Moodle course. It also helps your Moodle support to help you if they have a picture of what you want rather than wild hand gestures and frustration.

Wire farming a course also reduces the cognitive load on the teacher. Instead of designing and building the course a the same time. Wireframing splits this task into two steps, which are designing, and then building. This prevents extreme frustration as it is common for a teacher just to stare at the computer screen when trying to design and develop a Moodle course simultaneously.

You never see an architect making his plans while building the building. This would seem careless and even dangerous because the architect doesn’t even know what he wants while he is throwing around concrete and steel. The same analogy applies with designing Moodle courses. A teacher must know what they want, write it down, and then implement it by creating the course.

Another benefit of planning in Word is that it is easier to change things in Word when compared to Moodle. Moodle is amazing but it is not easy to use for those who are not tech-savvy. However, it’s easiest for most of us to copy, paste, and edit in Word.

One Way to Wire Frame a Moodle Course

When supporting teachers to wireframe a Moodle course, I always encourage them to start by developing the course in Microsoft Word. The reason is that the teacher is already familiar with Word and they do not have to struggle to make decisions when using it. This helps them to focus on content and not on how to use Microsoft Word.

One of the easiest ways to wireframe a Moodle course is to take the default topics of a course such as General Information, Week 1, Week 2, etc. and copy these headings into Word, as shown below.

Now, all that is needed is to type in using bullets exactly what activities and resources you want in each section. It is also possible to add pictures and other content to the Word document that can be added to Moodle later.  Below is a preview of a generic Moodle sample course with the general info and week 1 of the course completed.

You can see for yourself how this class is developed. The General Info section has an image to serve as a welcome and includes the name of the course. Under this the course outline and rubrics for the course. The information in the parentheses indicates what type of module it is.

For Week 1, there are several activities. There is a forum for introducing yourself. A page that shares the objectives of that week. Following this are the readings for the week, then a discussion forum, and lastly an assignment. This process completes for however many weeks are topics you have in the course.

Depending on your need to plan, you can even plan other pages on the site beside the main page. For example, I can wireframe what I want my “Objectives” page to look like or even the discussion topics for my “Discussion” forum.

Of course, the ideas for all these activities comes from the course outline or syllabus that was developed first. In other words, before we even wireframe we have some sort of curriculum document with what the course needs to cover.

Conclusion

The example above is an extremely simple way of utilizing the power of wireframing. With this template, you can confidently go to Moodle and find the different modules to make your class come to life. Trying to conceptualize this in your head is possible but much more difficult. As such, thorough planning is a hallmark of learning.

Generalized Models in R

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Generalized linear models are another way to approach linear regression. The advantage of of GLM is that allows the error to follow many different distributions rather than only the normal distribution which is an assumption of traditional linear regression.

Often GLM is used for response or dependent variables that are binary or represent count data. THis post will provide a brief explanation of GLM as well as provide an example.

Key Information

There are three important components to a GLM and they are

  • Error structure
  • Linear predictor
  • Link function

The error structure is the type of distribution you will use in generating the model. There are many different distributions in statistical modeling such as binomial, gaussian, poission, etc. Each distribution comes with certain assumptions that govern their use.

The linear predictor is the sum of the effects of the independent variables. Lastly, the link function determines the relationship between the linear predictor and the mean of the dependent variable. There are many different link functions and the best link function is the one that reduces the residual deviances the most.

In our example, we will try to predict if a house will have air conditioning based on the interactioon between number of bedrooms and bathrooms, number of stories, and the price of the house. To do this, we will use the “Housing” dataset from the “Ecdat” package. Below is some initial code to get started.

library(Ecdat)
data("Housing")

The dependent variable “airco” in the “Housing” dataset is binary. This calls for us to use a GLM. To do this we will use the “glm” function in R. Furthermore, in our example, we want to determine if there is an interaction between number of bedrooms and bathrooms. Interaction means that the two independent variables (bathrooms and bedrooms) influence on the dependent variable (aircon) is not additive, which means that the combined effect of the independnet variables is different than if you just added them together. Below is the code for the model followed by a summary of the results

model<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price, family=binomial)
summary(model)
## 
## Call:
## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms + 
##     Housing$stories + Housing$price, family = binomial)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7069  -0.7540  -0.5321   0.8073   2.4217  
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      -6.441e+00  1.391e+00  -4.632 3.63e-06
## Housing$bedrooms                  8.041e-01  4.353e-01   1.847   0.0647
## Housing$bathrms                   1.753e+00  1.040e+00   1.685   0.0919
## Housing$stories                   3.209e-01  1.344e-01   2.388   0.0170
## Housing$price                     4.268e-05  5.567e-06   7.667 1.76e-14
## Housing$bedrooms:Housing$bathrms -6.585e-01  3.031e-01  -2.173   0.0298
##                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 681.92  on 545  degrees of freedom
## Residual deviance: 549.75  on 540  degrees of freedom
## AIC: 561.75
## 
## Number of Fisher Scoring iterations: 4

To check how good are model is we need to check for overdispersion as well as compared this model to other potential models. Overdispersion is a measure to determine if there is too much variablity in the model. It is calcualted by dividing the residual deviance by the degrees of freedom. Below is the solution for this

549.75/540
## [1] 1.018056

Our answer is 1.01, which is pretty good because the cutoff point is 1, so we are really close.

Now we will make several models and we will compare the results of them

Model 2

#add recroom and garagepl
model2<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price + Housing$recroom + Housing$garagepl, family=binomial)
summary(model2)
## 
## Call:
## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms + 
##     Housing$stories + Housing$price + Housing$recroom + Housing$garagepl, 
##     family = binomial)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6733  -0.7522  -0.5287   0.8035   2.4239  
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      -6.369e+00  1.401e+00  -4.545 5.51e-06
## Housing$bedrooms                  7.830e-01  4.391e-01   1.783   0.0745
## Housing$bathrms                   1.702e+00  1.047e+00   1.626   0.1039
## Housing$stories                   3.286e-01  1.378e-01   2.384   0.0171
## Housing$price                     4.204e-05  6.015e-06   6.989 2.77e-12
## Housing$recroomyes                1.229e-01  2.683e-01   0.458   0.6470
## Housing$garagepl                  2.555e-03  1.308e-01   0.020   0.9844
## Housing$bedrooms:Housing$bathrms -6.430e-01  3.054e-01  -2.106   0.0352
##                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 681.92  on 545  degrees of freedom
## Residual deviance: 549.54  on 538  degrees of freedom
## AIC: 565.54
## 
## Number of Fisher Scoring iterations: 4
#overdispersion calculation
549.54/538
## [1] 1.02145

Model 3

model3<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price + Housing$recroom + Housing$fullbase + Housing$garagepl, family=binomial)
summary(model3)
## 
## Call:
## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms + 
##     Housing$stories + Housing$price + Housing$recroom + Housing$fullbase + 
##     Housing$garagepl, family = binomial)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6629  -0.7436  -0.5295   0.8056   2.4477  
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      -6.424e+00  1.409e+00  -4.559 5.14e-06
## Housing$bedrooms                  8.131e-01  4.462e-01   1.822   0.0684
## Housing$bathrms                   1.764e+00  1.061e+00   1.662   0.0965
## Housing$stories                   3.083e-01  1.481e-01   2.082   0.0374
## Housing$price                     4.241e-05  6.106e-06   6.945 3.78e-12
## Housing$recroomyes                1.592e-01  2.860e-01   0.557   0.5778
## Housing$fullbaseyes              -9.523e-02  2.545e-01  -0.374   0.7083
## Housing$garagepl                 -1.394e-03  1.313e-01  -0.011   0.9915
## Housing$bedrooms:Housing$bathrms -6.611e-01  3.095e-01  -2.136   0.0327
##                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 681.92  on 545  degrees of freedom
## Residual deviance: 549.40  on 537  degrees of freedom
## AIC: 567.4
## 
## Number of Fisher Scoring iterations: 4
#overdispersion calculation
549.4/537
## [1] 1.023091

Now we can assess the models by using the “anova” function with the “test” argument set to “Chi” for the chi-square test.

anova(model, model2, model3, test = "Chi")
## Analysis of Deviance Table
## 
## Model 1: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + 
##     Housing$price
## Model 2: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + 
##     Housing$price + Housing$recroom + Housing$garagepl
## Model 3: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + 
##     Housing$price + Housing$recroom + Housing$fullbase + Housing$garagepl
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1       540     549.75                     
## 2       538     549.54  2  0.20917   0.9007
## 3       537     549.40  1  0.14064   0.7076

The results of the anova indicate that the models are all essentially the same as there is no statistical difference. The only criteria on which to select a model is the measure of overdispersion. The first model has the lowest rate of overdispersion and so is the best when using this criteria. Therefore, determining if a hous has air conditioning depends on examining number of bedrooms and bathrooms simultenously as well as the number of stories and the price of the house.

Conclusion

The post explained how to use and interpret GLM in R. GLM can be used primarilyy for fitting data to disrtibutions that are not normal.

Common Challenges with Listening for ESL Students

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Listening is always a challenge as students acquire any language. Both teachers and students know that it takes time to developing comprehension when listening to a second language.

This post will explain some of the common obstacles to listening for ESL students. Generally, some common roadblocks include the following.

  • Slang
  • Contractions
  • Rate of Delivery
  • Emphasis in speech
  • Clustering
  • Repetition
  • Interaction

Slang

Slang or colloquial language is a major pain for language learners. There are so many ways that we communicate in English that does not meet the prescribed “textbook” way. This can leave ESL learners completely lost as to what is going on.

A simple example would be to say “what’s up”. Even the most austere English teacher knows what this means but this is in no way formal English. For someone new to English it would be confusing at least initially.

Contractions

Contractions are unique form of slang or colloquialism that is more readily accept as standard English. A challenge with contractions is their omission of information. With this missing information, there can be confusion.

An example would be “don’t” or “shouldn’t”. Other more complicated contractions can include “djeetyet” for “did you eat yet”. These common phrases leave out or do not pronounce important information.

Rate of Delivery 

When listening to someone in a second language it always seems too fast. The speed at which we speak our own language is always too swift for someone learning it.

Pausing at times during the delivery is one way to allow comprehension with actually slowing the speed at which one speaks. The main way to overcome this is to learn to listen faster if this makes any sense.

Emphasis in Speech

In many languages, there are complex rules for understanding which vowels to stress, which do not make sense to a non-native speaker. In fact, native speakers do not always agree on the vowels to stress. English speakers have been arguing or how to pronounce potato and tomato for ages.

Another aspect is the intonation. The inflection in many languages can change when asking a question, a statement, or being bored, angry or some other emotion. These little nuances of language as difficult to replicate and understand.

Clustering

Clustering is the ability to break language down into phrases. This helps in capturing the core of a language and is not easy to do. Language learners normally try to remember everything which leads to them understanding nothing.

For the teacher,  the students need help in determining what is essential information and what is not. This takes practice and demonstrations of what is considered critical and not in listening comprehension.

Repetition

Repetition is closely related to clustering and involves the redundant use of words and phrases. Constantly re-sharing the same information can become confusing for students. An example would be someone saying “you know” and  “you see what I’m saying.” This information is not critical to understanding most conversations and can throw of the comprehension of a language learner.

Interaction

Interaction has to do with a language learner understanding how to negotiate a conversation. This means being able to participate in a discussion, ask questions, and provide feedback.

The ultimate goal of listening is to speak. Developing interactive skills is yet another challenge to listening as students must develop participatory skills.

Conclusion

The challenges mentioned here are intended to help teachers to be able to identify what may be impeding their students from growing in their ability to listen. Naturally, this is not an exhaustive list but serves as a brief survey.

Types of Oral Language

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Within communication and language teaching there are actually many different forms or types of oral language. Understanding this is beneficial if a teacher is trying to support students to develop their listening skills. This post will provide examples of several oral language forms.

Monologues 

A monologue is the use of language without any feedback verbally from others. There are two types of monologue which are planned and unplanned. Planned monologues include such examples as speeches, sermons, and verbatim reading.

When a monologue is planned there is little repetition of the ideas and themes of the subject. This makes it very difficult for ESL students to follow and comprehend the information. ESL students need to hear the content several times to better understand what is being discussed.

Unplanned monologues are more improvisational in nature. Examples can include classroom lectures and one-sided conversations. There is usually more repetition in unplanned monologues which is beneficial. However, the stop and start of unplanned monologues can be confusing at times as well.

Dialogues

A dialogue is the use of oral language involving two or more people. Within dialogues, there are two main sub-categories which are interpersonal and transactional. Interpersonal dialogues encourage the development of personal relationships. Such dialogues that involve asking people how are they or talking over dinner may fall in this category.

Transactional dialogue is dialogue for sharing factual information. An example might be  if someone you do not know asks you “where is the bathroom.” Such a question is not for developing relationships but rather for seeking information.

Both interpersonal and transactional dialogues can be either familiar or unfamiliar. Familiarity has to do with how well the people speaking know each other. The more familiar the people talking are the more assumptions and hidden meanings they bring to the discussion. For example, people who work at the same company in the same department use all types of acronyms to communicate with each other that outsiders do not understand.

When two people are unfamiliar with each other, effort must be made to provide information explicitly to avoid confusion. This carries over when a native speaker speaks in a familiar manner to ESL students. The style of communication is inappropriate because of the lack of familiarity of the ESL students with the language.

Conclusion

The boundary between monologue and dialogue is much clear than the boundaries between the other categories mentioned such as planned/unplanned, interpersonal/transactional, and familiar/unfamiliar. In general, the ideas presented here represent a continuum and not either or propositions.

Proportion Test in R

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Proportions are a fraction or “portion” of a total amount. For example, if there are ten men and ten women in a room the proportion of men in the room is 50% (5 / 10). There are times when doing an analysis that you want to evaluate proportions in our data rather than individual measurements of mean, correlation, standard deviation etc.

In this post we will learn how to do a test of proportions using R. We will use the dataset “Default” which is found in the “ISLR” package. We will compare the proportion of those who are students in the dataset to a theoretical value. We will calculate the results using the z-test and the binomial exact test. Below is some initial code to get started.

library(ISLR)
data("Default")

We first need to determine the actual number of students that are in the sample. This is calculated below using the “table” function.

table(Default$student)
## 
##   No  Yes 
## 7056 2944

We have 2944 students in the sample and 7056 people who are not students. We now need to determine how many people are in the sample. If we sum the results from the table below is the code.

sum(table(Default$student))
## [1] 10000

There are 10000 people in the sample. To determine the proportion of students we take the number 2944 / 10000 which equals 29.44 or 29.44%. Below is the code to calculate this

table(Default$student) / sum(table(Default$student))
## 
##     No    Yes 
## 0.7056 0.2944

The proportion test compares a particular value with a theoretical value. For our example, the particular value we have is 29.44% of the people were students. We want to compare this value with a theoretical value of 50%. Before we do so it is better to state specificallt what are hypotheses are. NULL = The value of 29.44% of the sample being students is the same as 50% found in the population ALTERNATIVE = The value of 29.44% of the sample being students is NOT the same as 50% found in the population.

Below is the code to complete the z-test.

prop.test(2944,n = 10000, p = 0.5, alternative = "two.sided", correct = FALSE)
## 
##  1-sample proportions test without continuity correction
## 
## data:  2944 out of 10000, null probability 0.5
## X-squared = 1690.9, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
##  0.2855473 0.3034106
## sample estimates:
##      p 
## 0.2944

Here is what the code means. 1. prop.test is the function used 2. The first value of 2944 is the total number of students in the sample 3. n = is the sample size 4. p= 0.5 is the theoretical proportion 5. alternative =“two.sided” means we want a two-tail test 6. correct = FALSE means we do not want a correction applied to the z-test. This is useful for small sample sizes but not for our sample of 10000

The p-value is essentially zero. This means that we reject the null hypothesis and conclude that the proportion of students in our sample is different from a theortical proportion of 50% in the population.

Below is the same analysis using the binomial exact test.

binom.test(2944, n = 10000, p = 0.5)
## 
##  Exact binomial test
## 
## data:  2944 and 10000
## number of successes = 2944, number of trials = 10000, p-value <
## 2.2e-16
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
##  0.2854779 0.3034419
## sample estimates:
## probability of success 
##                 0.2944

The results are the same. Whether to use the “prop.test”” or “binom.test” is a major argument among statisticians. The purpose here was to provide an example of the use of both

Learning Styles and Strategies

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All students have distinct traits in terms of how they learn and what they do to ensure that they learn. These two vague categories of how a student learns and what they do to learn are known as learning styles and learning strategies.

This post will explain what learning styles and learning strategies are.

Learning Styles

Learning styles are consistent traits that are long-lasting over time. For example, the various learning styles identified by Howard Gardner such as auditory, kinesthetic, or musical learner. An auditory learner prefers to learn through hearing things.

Learning styles are also associated with personality. For example, introverts prefer quiet time and fewer social interaction when compared to extroverts. This personality trait of introversion my affect an introverts ability to learn while working in small groups but not necessarily.

Learning Strategies

Strategies are specific methods a student uses to master and apply information. Examples include asking friends for help,  repeating information to one’s self, rephrasing, and or using context clues to determine the meaning of unknown words.

Strategies are much more unpredictable and flexible than styles are. Students can acquire styles through practice and exposure. In addition, it is common to use several strategies simultaneously to learn and use information.

Successful Students

Successful students understand what their style and strategies are. Furthermore, they can use these tendencies in learning and acquiring knowledge to achieve goals. For example, an introvert who knows they prefer to be alone and not work in groups will know when there are times when this natural tendency must be resisted.

The key to understanding one’s styles and strategies is self-awareness. A teacher can support a student in understanding what their style and strategies are through the use of the various informal checklist and psychological test.

A teacher can also support students in developing a balanced set of strategies through compensatory activities. These are activities that force students to use strategies they are weak. For example, having auditory learners learn through kinesthetic means. This helps students to acquire skills that may be highly beneficial in their learning in the future.

To help students to develop compensatory skills requires that the teacher know and understand the strengths and weaknesses of their students. This naturally takes time and implies that compensatory activities should not take place at the beginning of a semester or should they be pre-planned into a unit plan before meeting students.

Conclusion

Strategies can play a powerful role in information processing. As such, students need to be aware of how they learn and what they do to learn. The teacher can provide support in this by helping students to figure out who they are as a learner.

Practical Application of the Use of the Assignment Activity in Moodle Video

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Below is a video on setting up the assignment activity in Moodle

Theoretical Distribution and R

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This post will explore an example of testing if a dataset fits a specific theoretical distribution. This is a very important aspect of statistical modeling as it allows to understand the normality of the data and the appropriate steps needed to take to prepare for analysis.

In our example, we will use the “Auto” dataset from the “ISLR” package. We will check if the horsepower of the cars in the dataset is normally distributed or not. Below is some initial code to begin the process.

library(ISLR)
library(nortest)
library(fBasics)
data("Auto")

Determining if a dataset is normally distributed is simple in R. This is normally done visually through making a Quantile-Quantile plot (Q-Q plot). It involves using two functions the “qnorm” and the “qqline”. Below is the code for the Q-Q plot

qqnorm(Auto$horsepower)

We now need to add the Q-Q line to see how are distribution lines up with the theoretical normal one. Below is the code. Note that we have to repeat the code above in order to get the completed plot.

qqnorm(Auto$horsepower)
qqline(Auto$horsepower, distribution = qnorm, probs=c(.25,.75))

The “qqline” function needs the data you want to test as well as the distribution and probability. The distribution we wanted is normal and is indicated by the argument “qnorm”. The probs argument means probability. The default values are .25 and .75. The resulting graph indicates that the distribution of “horsepower”, in the “Auto” dataset is not normally distributed. That are particular problems with the lower and upper values.

We can confirm our suspicion by running a statistical test. The Anderson-Darling test from the “nortest” package will allow us to test whether our data is normally distributed or not. The code is below

ad.test(Auto$horsepower)
##  Anderson-Darling normality test
## 
## data:  Auto$horsepower
## A = 12.675, p-value < 2.2e-16

From the results, we can conclude that the data is not normally distributed. This could mean that we may need to use non-parametric tools for statistical analysis.

We can further explore our distribution in terms of its skew and kurtosis. Skew measures how far to the left or right the data leans and kurtosis measures how peaked or flat the data is. This is done with the “fBasics” package and the functions “skewness” and “kurtosis”.

First we will deal with skewness. Below is the code for calculating skewness.

horsepowerSkew<-skewness(Auto$horsepower)
horsepowerSkew
## [1] 1.079019
## attr(,"method")
## [1] "moment"

We now need to determine if this value of skewness is significantly different from zero. This is done with a simple t-test. We must calculate the t-value before calculating the probability. The standard error of the skew is defined as the square root of six divided by the total number of samples. The code is below

stdErrorHorsepower<-horsepowerSkew/(sqrt(6/length(Auto$horsepower)))
stdErrorHorsepower
## [1] 8.721607
## attr(,"method")
## [1] "moment"

Now we take the standard error of Horsepower and plug this into the “pt” function (t probability) with the degrees of freedom (sample size – 1 = 391) we also put in the number 1 and subtract all of this information. Below is the code

1-pt(stdErrorHorsepower,391)
## [1] 0
## attr(,"method")
## [1] "moment"

The value zero means that we reject the null hypothesis that the skew is not significantly different form zero and conclude that the skew is different form zero. However, the value of the skew was only 1.1 which is not that non-normal.

We will now repeat this process for the kurtosis. The only difference is that instead of taking the square root divided by six we divided by 24 in the example below.

horsepowerKurt<-kurtosis(Auto$horsepower)
horsepowerKurt
## [1] 0.6541069
## attr(,"method")
## [1] "excess"
stdErrorHorsepowerKurt<-horsepowerKurt/(sqrt(24/length(Auto$horsepower)))
stdErrorHorsepowerKurt
## [1] 2.643542
## attr(,"method")
## [1] "excess"
1-pt(stdErrorHorsepowerKurt,391)
## [1] 0.004267199
## attr(,"method")
## [1] "excess"

Again the pvalue is essentially zero, which means that the kurtosis is significantly different from zero. With a value of 2.64 this is not that bad. However, when both skew and kurtosis are non-normally it explains why our overall distributions was not normal either.

Conclusion

This post provided insights into assessing the normality of a dataset. Visually inspection can take place using  Q-Q plots. Statistical inspection can be done through hypothesis testing along with checking skew and kurtosis.

Overcoming Plagiarism in an ESL Context

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Academic dishonesty in the form of plagiarism is a common occurrence in academia. Generally, most students know that cheating is inappropriate on exams and what they are really doing is hoping that they are not caught.

However, plagiarism is much more sticky and subjective offense for many students. This holds especially true for ESL students. Writing in a second language is difficult for everybody regardless of one’s background. As such, students often succumb to the temptation of plagiarism to complete writing assignments.

Many ideas are being used to reduce plagiarism. Software like turnitin do work but they lead to an environment of mistrust and an arms race between students and teachers. Other measures should be considered for dealing with plagiarism.

This post will explain how seeing writing from the perspective of a process rather than a product can reduce the chances of plagiarism in the ESL context.

 Writing as a Product

In writing pedagogy, the two most common views on writing are writing as a product and writing as a process. Product writing views writing as the submission of a writing assignment that meets a certain standard is grammatically near perfection, and highly structured. Students are given examples of excellence and are expected to emulate them.

Holding to this view is fine but it can contribute to plagiarism in many ways.

  • Students cannot meet the expectation of grammatical perfection. This encourages them to copy excellently written English from Google into their papers.
  • Focus on grammar leads to over-correction of the final paper. The overwhelming red pen marks from the teacher on the paper can stifle a desire for students to write in fear of additional correction.
  • The teacher often provides little guidance beyond providing examples. Without daily, constant feedback, students have no idea what to do and rely on Google.
  • People who write in a second language often struggle to structure their thoughts because we all think much shallower in a second language with reduced vocabulary. Therefore, an ESL paper is always messier because of the difficulty of executing complex cognitive processes in a second language.

These pressures mentioned above can contribute to a negative classroom environment in which students do not really want to write but survive a course however it takes. For native-speakers, this works but is really hard for ESL students to have success.

Writing as a Process

The other view of writing is writing as a process. This approach sees writing as the teacher providing constant one-on-one guidance through the writing process. Students begin to learn how they write and develop an understanding of the advantages of rewriting and revisions. Teacher and peer feedback are utilized throughout the various drafts of the paper.

The view of writing as a product has the following advantages for avoiding plagiarism

  • Grammar is slowly fixed over time through feedback from the teacher. This allows the students to make corrections before the final submission.
  • Any instances of plagiarism can be caught before final submission. Many teachers do not give credit for rough drafts. Therefore, plagiarism in a rough draft normally does not affect the final grade.
  • The teacher can coach the students on how to reword plagiarize statements and also how to give appropriate credit through using APA.
  • The de-emphasis on perfection allows the student to grow and mature as a writer on the constant support of the teacher and peers.
  • Guiding the students thought process is especially critically across cultures as communication style vary widely across the world. Learning to write for a Western academic audience requires training in how Western academics think and communicate. This cannot be picked up alone and is another reason why plagiarism is useful because the stole idea is communicated appropriately.

In a writing as a process environment, the students and teacher work together to develop papers that meet standards in the students own words. It takes much more time and effort but it can reduce the temptation of just copying from whatever Google offers.

Conclusion

Grammar plays a role in writing but the shaping of ideas and their communication is of upmost concern for many in TESOL. The analogy I use is that grammar is like the paint on the walls of a house or the tile on the floor. It makes the house look nice but is not absolutely necessary. The ideas and thoughts of a paper are like the foundation, walls, and roof. Nobody wants to live in a house that lacks tile or is not painted but you cannot live in a house that does not have walls and a roof.

The stress on native-like communication stresses out ESL students to the point of not even trying to write at times. With a change in view on the writing experience from product to process this can be alleviated. We should only ask our students to do what we are able to do. If we cannot write in a second language in a fluent manner how can we ask them?

Academic Dishonesty and Cultural Difference

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Academic dishonesty, which includes plagiarism and cheating, are problems that most teachers have dealt with in their career. Students sometimes succumb to the temptation of finding ways to excel or just survive a course by doing things that are highly questionable. This post will attempt to deal with some of the issues related to academic dishonesty. In particular, we will look at how perceptions of academic dishonesty vary across contexts.

Cultural Variation

This may be frustrating to many but there is little agreement in terms of what academic dishonesty is once one leaves their own cultural context. In the West, people often believe that a person can create and “own” an idea, that people should “know” their stuff, and that “credit” should be giving one using other people’s ideas. These foundational assumptions shape how teachers and students view using others ideas and using the answers of friends to complete assignments

However, in other cultures there is more of an “ends justifies the means” approach. This manifests itself in using ideas without giving credit because ideas belong to nobody and having friends “help” you to complete an assignment or quiz because they know the answer and you do not if the situation was different you would give them the answer. Therefore, in many contexts doesn’t matter how the assignment or quiz is completed as long as it is done.

This has a parallel in many situations. If you are working on a project for your boss and got stuck. Would it be deceptive to ask for help from a colleague to get the project done? Most of us have done this at one time or another. The problem is that this is almost always frowned upon during an assignment or assessment in the world of academics.

The purpose here is not to judge one side or the other but rather to allow people to identify the assumptions they have about academic dishonesty so that they avoid jumping to conclusions when confronted with this by people who are not from the same part of the world like them.

Our views on academic dishonesty are shaped in the context we grow up in

Clear Communication

One way to deal with the misunderstandings of academic dishonesty across cultures is for the teacher to clearly define what academic dishonesty is to them. This means providing examples explaining how this violates the norms of academia. In the context of academia, academic dishonesty in the forms of cheating and plagiarism is completely unacceptable.

One strategy that I have used to explain academic dishonesty is to compare academic dishonesty to something that is totally culturally repulsive locally. For example, I have compared plagiarism to wearing your shoes in someone’s house in Asia (a major no-no in most parts). Students never understand what plagiarism is when defined in isolation abstractly (or so they say). However, when plagiarism is compared to wearing your shoes in someone’s house, they begin to see how much academics hate this behavior. They also realize how they need to adjust their behavior for the context they are in.

By presenting a cultural argument against plagiarism and cheating rather than a moral one, students are able to understand how in the context of school this is not acceptable. Outside of school, there are normally different norms of acceptable behavior.

Conclusion

The steps to take with people who share the same background are naturally different from the suggestion provided here. The primary point to remember is that academic dishonesty is not seen the same way by everyone. This requires that the teacher communicate what they mean when referring to this and to provide a relevant example of academic dishonesty so the students can understand.