Making Grade Categories in Moodle VIDEO

Video on making categories in the Moodle gradebook

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Developing Purpose to Improve Reading Comprehension

Many of us are familiar with the experience of being able to read almost anything but perhaps not being able to understand what it is that we read. As the ability to sound out words becomes automatic there is not always a corresponding increase in being able to comprehend text.

It is common, especially in school, for students to be required to read something without much explanation. For more mature readers, what is often needed is a sense of purpose for reading. In this post, we will look at ways to develop a sense of purpose in reading.

Purpose Provides Motivation

Students who know why they are reading know what the are looking for while reading. The natural result of this is that students are less likely to get distract by information that is not useful for them.

For example, if the teacher tells their students to read “the passage and identifying all of the animals in it and be ready to share tomorrow.” Students know what they are suppose to do (identifying all animals in the passage) and why they need to do it (share tomorrow). the clear directions prevent students from getting distracted by other information in the reading.

Providing purpose doesn’t necessarily require the students love and enjoy the rational but it is helpful if a teacher can provide a purpose that is motivating.

Different Ways to Instill Purpose

In addition to the example above there are several quick ways to provide purpose.

  • Provide vocabulary list-Having the students search for the meaning of specific words provides a clear sense of purpose and provides a context in which the words appear naturally. However, students often get bogged down with the minutia of the definitions and completely miss the overall meaning of the reading passage. This approach is great for beginning and low intermediate readers.
  • Identifying the main ideas in the reading-This is a great way to gets students to see the “big picture” of a reading. It is especially useful for short to moderately long readings such as articles and perhaps chapters and useful for intermediate to advanced readers in particular.
  •  Let students develop their own questions about the text-By fair my most favorite strategy. Students will initial skim the passage to get an idea of what it is about. After this, they develop several questions about the passage that they want to find the answer too. While reading the passage, the students answer their own questions. This approach provides opportunities for metacognition as well developing autonomous learning skills. This strategy is for advanced readers who are comfortable with vocabulary and summarizing text.

Conclusion

Students, like most people,  need a raison de faire (reason to do) something. The teacher can provide this, which has benefits. Another approach would be to allow the students to develop their own purpose. How this is done depends on the philosophy of the teacher as well as the abilities and tendencies of the students

Linear Discriminant Analysis in R

In this post we will look at an example of linear discriminant analysis (LDA). LDA is used to develop a statistical model that classifies examples in a dataset. In the example in this post, we will use the “Star” dataset from the “Ecdat” package. What we will do is try to predict the type of class the students learned in (regular, small, regular with aide) using their math scores, reading scores, and the teaching experience of the teacher. Below is the initial code

library(Ecdat)
library(MASS)
data(Star)

We first need to examine the data by using the “str” function

str(Star)
## 'data.frame':    5748 obs. of  8 variables:
##  $ tmathssk: int  473 536 463 559 489 454 423 500 439 528 ...
##  $ treadssk: int  447 450 439 448 447 431 395 451 478 455 ...
##  $ classk  : Factor w/ 3 levels "regular","small.class",..: 2 2 3 1 2 1 3 1 2 2 ...
##  $ totexpk : int  7 21 0 16 5 8 17 3 11 10 ...
##  $ sex     : Factor w/ 2 levels "girl","boy": 1 1 2 2 2 2 1 1 1 1 ...
##  $ freelunk: Factor w/ 2 levels "no","yes": 1 1 2 1 2 2 2 1 1 1 ...
##  $ race    : Factor w/ 3 levels "white","black",..: 1 2 2 1 1 1 2 1 2 1 ...
##  $ schidkn : int  63 20 19 69 79 5 16 56 11 66 ...
##  - attr(*, "na.action")=Class 'omit'  Named int [1:5850] 1 4 6 7 8 9 10 15 16 17 ...
##   .. ..- attr(*, "names")= chr [1:5850] "1" "4" "6" "7" ...

We will use the following variables

  • dependent variable = classk (class type)
  • independent variable = tmathssk (Math score)
  • independent variable = treadssk (Reading score)
  • independent variable = totexpk (Teaching experience)

We now need to examine the data visually by looking at histograms for our independent variables and a table for our dependent variable

hist(Star$tmathssk)

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hist(Star$treadssk)

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hist(Star$totexpk)

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prop.table(table(Star$classk))
## 
##           regular       small.class regular.with.aide 
##         0.3479471         0.3014962         0.3505567

The data mostly looks good. The results of the “prop.table” function will help us when we develop are training and testing datasets. The only problem is with the “totexpk” variable. IT is not anywhere near to be normally distributed. TO deal with this we will use the square root for teaching experience. Below is the code

star.sqrt<-Star
star.sqrt$totexpk.sqrt<-sqrt(star.sqrt$totexpk)
hist(sqrt(star.sqrt$totexpk))

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Much better. We now need to check the correlation among the variables as well and we will use the code below.

cor.star<-data.frame(star.sqrt$tmathssk,star.sqrt$treadssk,star.sqrt$totexpk.sqrt)
cor(cor.star)
##                        star.sqrt.tmathssk star.sqrt.treadssk
## star.sqrt.tmathssk             1.00000000          0.7135489
## star.sqrt.treadssk             0.71354889          1.0000000
## star.sqrt.totexpk.sqrt         0.08647957          0.1045353
##                        star.sqrt.totexpk.sqrt
## star.sqrt.tmathssk                 0.08647957
## star.sqrt.treadssk                 0.10453533
## star.sqrt.totexpk.sqrt             1.00000000

None of the correlations are too bad. We can now develop our model using linear discriminant analysis. First, we need to scale are scores because the test scores and the teaching experience are measured differently. Then, we need to divide our data into a train and test set as this will allow us to determine the accuracy of the model. Below is the code.

star.sqrt$tmathssk<-scale(star.sqrt$tmathssk)
star.sqrt$treadssk<-scale(star.sqrt$treadssk)
star.sqrt$totexpk.sqrt<-scale(star.sqrt$totexpk.sqrt)
train.star<-star.sqrt[1:4000,]
test.star<-star.sqrt[4001:5748,]

Now we develop our model. In the code before the “prior” argument indicates what we expect the probabilities to be. In our data the distribution of the the three class types is about the same which means that the apriori probability is 1/3 for each class type.

train.lda<-lda(classk~tmathssk+treadssk+totexpk.sqrt, data = 
train.star,prior=c(1,1,1)/3)
train.lda
## Call:
## lda(classk ~ tmathssk + treadssk + totexpk.sqrt, data = train.star, 
##     prior = c(1, 1, 1)/3)
## 
## Prior probabilities of groups:
##           regular       small.class regular.with.aide 
##         0.3333333         0.3333333         0.3333333 
## 
## Group means:
##                      tmathssk    treadssk totexpk.sqrt
## regular           -0.04237438 -0.05258944  -0.05082862
## small.class        0.13465218  0.11021666  -0.02100859
## regular.with.aide -0.05129083 -0.01665593   0.09068835
## 
## Coefficients of linear discriminants:
##                      LD1         LD2
## tmathssk      0.89656393 -0.04972956
## treadssk      0.04337953  0.56721196
## totexpk.sqrt -0.49061950  0.80051026
## 
## Proportion of trace:
##    LD1    LD2 
## 0.7261 0.2739

The printout is mostly readable. At the top is the actual code used to develop the model followed by the probabilities of each group. The next section shares the means of the groups. The coefficients of linear discriminants are the values used to classify each example. The coefficients are similar to regression coefficients. The computer places each example in both equations and probabilities are calculated. Whichever class has the highest probability is the winner. In addition, the higher the coefficient the more weight it has. For example, “tmathssk” is the most influential on LD1 with a coefficient of 0.89.

The proportion of trace is similar to principal component analysis

Now we will take the trained model and see how it does with the test set. We create a new model called “predict.lda” and use are “train.lda” model and the test data called “test.star”

predict.lda<-predict(train.lda,newdata = test.star)

We can use the “table” function to see how well are model has done. We can do this because we actually know what class our data is beforehand because we divided the dataset. What we need to do is compare this to what our model predicted. Therefore, we compare the “classk” variable of our “test.star” dataset with the “class” predicted by the “predict.lda” model.

table(test.star$classk,predict.lda$class)
##                    
##                     regular small.class regular.with.aide
##   regular               155         182               249
##   small.class           145         198               174
##   regular.with.aide     172         204               269

The results are pretty bad. For example, in the first row called “regular” we have 155 examples that were classified as “regular” and predicted as “regular” by the model. In rhe next column, 182 examples that were classified as “regular” but predicted as “small.class”, etc. To find out how well are model did you add together the examples across the diagonal from left to right and divide by the total number of examples. Below is the code

(155+198+269)/1748
## [1] 0.3558352

Only 36% accurate, terrible but ok for a demonstration of linear discriminant analysis. Since we only have two-functions or two-dimensions we can plot our model.  Below I provide a visual of the first 50 examples classified by the predict.lda model.

plot(predict.lda$x[1:50])
text(predict.lda$x[1:50],as.character(predict.lda$class[1:50]),col=as.numeric(predict.lda$class[1:100]))
abline(h=0,col="blue")
abline(v=0,col="blue")

Rplot01.jpeg

The first function, which is the vertical line, doesn’t seem to discriminant anything as it off to the side and not separating any of the data. However, the second function, which is the horizontal one, does a good of dividing the “regular.with.aide” from the “small.class”. Yet, there are problems with distinguishing the class “regular” from either of the other two groups.  In order improve our model we need additional independent variables to help to distinguish the groups in the dependent variable.

Factors that Affect Pronunciation

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 generate by the student but the comprehensibility of what they say.

This post will explain several common factors that influence pronunciation. This 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 loss 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 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

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 on developing speaking and listening techniques.

Techniques should Encourage Intrinsic Motivation

When developing techniques for your students. The techniques need 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 students goals.

When techniques do not align with student goals there is a lost of relevance, which is highly demotivating. Of course, as the teacher, you do not always give them what they want but general practice suggest 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 wold 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 ab 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

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 specific 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 “accleration” 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)

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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)

2136d310-b3f5-4c78-b166-4f6c4a1d0e12.png

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

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 involves 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 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.

Wire Framing with Moodle

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 have developed courses before this might work. However, for the majority online teachers they need to wire frame what they want their moodle course to look like online.

Why Wire frame a Moodle Course

In the world of  web developers a wire frame is a prototype of what a potential website will look like. The actual wire frame 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 wire framing applies to this context.

It doesn’t matter how a you wire frames 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 the you wants 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. Wire framing 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 and 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 wire frame a Moodle course, I always encourage them to start by developing the course in Microsoft Word. The reason being 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 wire frame 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.

Screenshot from 2017-01-20 09-15-19.png

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.

Screenshot from 2017-01-20 09-26-00.png

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 indicate 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 the your need to plan, you can even planned other pages on the site beside the main page. For example, I can wire frame 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 wire frame 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 wire framing. 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

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

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 includes 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 there 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 phrase 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 listening 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 exhaustive list but serves as a brief survey.

 

Types of Oral Language

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 form 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

Proportions are 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” pacakage. 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 proprtion 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 is used to compare 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 proprtion of students in our sample is different from a theortical proprition 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

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 know 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. A 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 naturally 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 various informal checklist and psychological test.

A teacher can also support students in developing a balance 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 an 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.

Theoretical Distribution and R

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)

75330880-13dc-49da-8f00-22073c759639.png

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))

feee73f0-cf66-4d64-8142-63845243eea4.png

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

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 plagarism. 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 plagarism

This post will 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 for 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 more 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 plagarism

  • 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 plagarism 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 up most 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

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 context.

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 context 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 frown 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 conclusion when confronted with this by people who are not from the same part of the world as 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 an explaining how this violates the norms of academia. In the context of academia, academic dishonesty in the forms of cheating and plagiarism are completely unacceptable.

One strategy that I have used to explain academic dishonesty is to compare academic dishonesty that is totally culturally repulsive locally. For example, I have compare plagiarism to wearing your shoes in someone’s house in Asia (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 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 than with the suggestion provided here. The primary point to remember is that academic dishonesty is not seen the same way be 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.

Probability Distribution and Graphs in R

In this post, we will use probability distributions and ggplot2 in R to solve a hypothetical example. This provides a practical example of the use of R in everyday life through the integration of several statistical and coding skills. Below is the scenario.

At a busing company the average number of stops for a bus is 81 with a standard deviation of 7.9. The data is normally distributed. Knowing this complete the following.

  • Calculate the interval value to use using the 68-95-99.7 rule
  • Calculate the density curve
  • Graph the normal curve
  • Evaluate the probability of a bus having less then 65 stops
  • Evaluate the probability of a bus having more than 93 stops

Calculate the Interval Value

Our first step is to calculate the interval value. This is the range in which 99.7% of the values falls within. Doing this requires knowing the mean and the standard deviation and subtracting/adding the standard deviation as it is multiplied by three from the mean. Below is the code for this.

busStopMean<-81
busStopSD<-7.9
busStopMean+3*busStopSD
## [1] 104.7
busStopMean-3*busStopSD
## [1] 57.3

The values above mean that we can set are interval between 55 and 110 with 100 buses in the data. Below is the code to set the interval.

interval<-seq(55,110, length=100) #length here represents 
100 fictitious buses

Density Curve

The next step is to calculate the density curve. This is done with our knowledge of the interval, mean, and standard deviation. We also need to use the “dnorm” function. Below is the code for this.

densityCurve<-dnorm(interval,mean=81,sd=7.9)

We will now plot the normal curve of our data using ggplot. Before we need to put our “interval” and “densityCurve” variables in a dataframe. We will call the dataframe “normal” and then we will create the plot. Below is the code.

library(ggplot2)
normal<-data.frame(interval, densityCurve)
ggplot(normal, aes(interval, densityCurve))+geom_line()+ggtitle("Number of Stops for Buses")

282deee2-ff95-488d-ad97-471b74fe4cb8

Probability Calculation

We now want to determine what is the provability of a bus having less than 65 stops. To do this we use the “pnorm” function in R and include the value 65, along with the mean, standard deviation, and tell R we want the lower tail only. Below is the code for completing this.

pnorm(65,mean = 81,sd=7.9,lower.tail = TRUE)
## [1] 0.02141744

As you can see, at 2% it would be unusually to. We can also plot this using ggplot. First, we need to set a different density curve using the “pnorm” function. Combine this with our “interval” variable in a dataframe and then use this information to make a plot in ggplot2. Below is the code.

CumulativeProb<-pnorm(interval, mean=81,sd=7.9,lower.tail = TRUE)
pnormal<-data.frame(interval, CumulativeProb)
ggplot(pnormal, aes(interval, CumulativeProb))+geom_line()+ggtitle("Cumulative Density of Stops for Buses")

9667dd01-f7d3-4025-8995-b6441a3735d0.png

Second Probability Problem

We will now calculate the probability of a bus have 93 or more stops. To make it more interesting we will create a plot that shades the area under the curve for 93 or more stops. The code is a little to complex to explain so just enjoy the visual.

pnorm(93,mean=81,sd=7.9,lower.tail = FALSE)
## [1] 0.06438284
x<-interval  
ytop<-dnorm(93,81,7.9)
MyDF<-data.frame(x=x,y=densityCurve)
p<-ggplot(MyDF,aes(x,y))+geom_line()+scale_x_continuous(limits = c(50, 110))
+ggtitle("Probabilty of 93 Stops or More is 6.4%")
shade <- rbind(c(93,0), subset(MyDF, x > 93), c(MyDF[nrow(MyDF), "X"], 0))

p + geom_segment(aes(x=93,y=0,xend=93,yend=ytop)) +
        geom_polygon(data = shade, aes(x, y))

b42a7c19-1992-4df1-95cc-40ea097058de

Conclusion

A lot of work was done but all in a practical manner. Looking at realistic problem. We were able to calculate several different probabilities and graph them accordingly.

Dealing with Classroom Management

Classroom management is one of the most difficult aspects of teaching. Despite the difficulties of behavioral problems there are several steps teachers can make to mitigate this problem. This post will provide some practical ways to reduce or even eliminate the headache of classroom management.

Deal with the Learning Space

The learning space is another name for the classroom that the teacher have authority over. If a teacher is fortunate enough to have their own classroom (this is not always the case) he or she may need to consider some of the following.

  • A clean, neat, visually appealing classroom helps in settling students.
  • The temperature should be moderate. Too cold or too hot leads to problems
  • The acoustics of the classroom affects performance. If it’s hard to hear each other it makes direct instruction impossible as well as any whole-class discussion. This includes noise coming from outside the classroom

If the teacher do not have their own classroom, he may need to work with the administration or the teachers in whose classroom he teaches to deal with some of these issues.

Dealing with Seating Arrangements

There are essential four seating arrangements in a classroom

  • Rows
  • Full circle
  • Half circle
  • Groups

Each of these arrangements have there advantages and disadvantages. Rows are use for a teacher-centered classroom and lecture style. They are for individual work as well. However, rows limit interaction among students. Despite this, at the beginning of the year it may be better to start with rows until a teacher has a handle on the students.

Full/Half circle or great for whole-class discussion. Students are able to all make eye-contact and this helps with supporting a discussion. However, this also makes it hard to concentrate if there is some sort of assignment that needs to be completed. As such, the full/half circle approach  is normally used for special occasions.

Groups are used for high interaction settings. In groups, students, can work together on project or support each other for regular assignments. Normally, groups lead to the largest amount of management problems. As such, groups are great for teachers who have more experience with classroom management.

Dealing with Presence

Presence has to do with the voice and body language of a teacher. Learning to control the voice is a common problem for new teachers and losing one’s voice happens frequently. The voice of a teacher most project without yelling and this requires practice, which can be accelerated through taking voice lessons. Speaking must also be done at a reasonable rate. Too fast or slow will make it hard to pay attention.

The body language of teacher should project a sense of calm, confidence, and optimism. This can be done by moving about the room while teaching, feigning confidence even if the teacher don’t have it, and always maintaining composure no matter what the students do. A teacher losing control of their temper means the students have control and they will enjoy laughing at the one who is suppose to be in control.

Conclusion

Teachers need to exert the authority that they are the leader of the classroom. This requires being organized and confident while having a sense of direction in where the lesson is going. This is not easy but is often necessary when dealing with students.

Planning Groupwork in the ESL Classroom

In teaching, as a teacher gives autonomy over to the students it often requires an increase in the preparation of the teacher. This is due to the unpredictable nature of entrusting students with the freedom to complete a task on their own.

For teachers who use groupwork, they need to make sure that they have carefully planned what they want the groups to attempt to achieve. Failure to do so could  lead to listless groups that never achieve the learning objectives of the lesson.

In this post, we will look at steps to take when planning groupwork for the language learning classroom.

Establish the Technique

Before groupwork begins some direct instruction is almost always necessary, which means explain to the class what they will do. There are many different techniques consistent with groupwork. These include role plays, brainstorming, interviews, jigsaw, problem-solving etc.

The role of the teacher at this point is simply to provide a sense of purpose for the class. This allows the students to focus on understand why they are doing something. This also helps the students to see why they are working in groups. This is particularly useful for those who do not enjoy groupwork.

Demonstrate the Technique

Actions always speak louder than words, what this means for groupwork is that the students need to see how the technique is done. This is particularly try if it is a complex task and or the students have never done it before.

Naturally, it may be impossible to model a group technique alone. This necessitates the need to use student volunteers as you demonstrate the technique. Most students will claim shyness but they usually enjoy participating in such activities.

While going through the technique the teacher needs to narrate what is happening so the students can follow along. After completing the technique, the teacher than examples verbally what to do. This allows the students to receive additional direction through a different medium, which helps in retention of the information.

Create Groups

There are a variety of ways to divide and place students in groups. Groups can be base don proficiency, experience, age, gender, native language, randomly, etc. The decision for the creation of groups is left to the teacher but should be consistent with the goals of the assignment.

After groups are formed it is almost always necessary to go to each group and check for understanding of the instructions. A strange phenomenon in a classroom is how understanding decrease as you move from whole-class instruction, to group, to individual. When students are in groups they are often much more comfortable in sharing misgivings than when in a whole-class setting. As such, a teacher has to re-teach every group as there is always some form of misunderstanding. Once this is done, the students are thoroughly prepared to start the task.

Conclusion

Groupwork can be frustrating and this can normally be due to a lack of planning. It is not enough to just throw students together and have “fun”. A teacher must plan carefully for groupwork in order to prepare for the un-expected

Critiques of Groupwork in ESL Classrooms

Many ESL teachers adhere to the principles of Communicative Language Teaching which includes such characteristics as cooperative language learning and groupwork. However, not everyone has embraced the emphasis on groupwork in modern language classrooms.

This post will explain some of the common objectives to groupwork in order to inform language teachers as to what concerns some have with the popularity of groupwork.

Use of the L1 Groupwork

If a class has a large number of students who share the same L1 there is a risk that the students will use their L1 when working in groups. This is a particular risk in EFL classrooms. However, there are several ways to address this problem

  • Make sure the task is of moderate difficulty. Too hard or too easy will encourage L1 use
  • Provide clear directions. If the students don’t understand what to do they will communicate frustration  in their own language
  • Emphasis the use of the L2. This provides relevance and accountability

Lost of Control

Groupwork usually looks chaotic and messy. Some teachers and administrators do not like the appearance of groupwork even if learning is taking place. Dealing with this problem requires the use of a reduce emphasis on groupwork but not the total removal of it.

There are times when group work should be avoided because of control issues. Below are some examples

  • Difficult students
  • Extremely large class sizes (how large depends on the teacher)
  • Inexperience teacher

Any of these situations calls for caution for the teacher. Furthermore, it is necessary for the teacher to circulate throughout the room and try to support the various groups. This is difficult but normally easier than trying to support all students individually.

L2 Use in Groups will Reinforce Errors

Some argue that students using the L2 with proper feedback will develop bad habits. This true but bad habits in the L2 may be better than not using the L2. For some, broken English is better than no English.

The concern here is looking at fluency vs accuracy. Each teacher can have their preference but constant correction often discourages language use. As such, free flowing conversation with the teacher looking the other can help in developing fluency.

Working Alone

Some students prefer to work alone. However, communication is a group experience. This means that the quiet ones must experience at least some groupwork in order to develop their language skills. Therefore, the teacher needs to encourage some groupwork regardless of student preference.

Conclusion

Groupwork should be a part of most language classrooms. The question is trying to find the appropriate balance of groupwork with other forms of learning. This is left for each teacher to decide for themselves.

 

Group Work in the ESL Classroom

Working in groups is a popular activity in many classes. Students and even teachers enjoy working together to complete task in the classroom. This post will look at the use of groups in the ESL classroom. In particular, we will look at 4 benefits of groups for ESL students.

Interactive Opportunities

Group work is especially useful for large classes where chances to speak are fewer. Students placed in groups can talk with each other and not wait for a turn in a whole-class setting.

In small groups, there is an increase in the quantity or amount of speaking opportunities as well as an increase in the quality or type of communication that takes place. Many teachers are always looking to improve these to factors in their language classrooms.

Responsibility

Large, whole-class activities allows students to hide and not really learn or do anything. This problem is alleviated when students are placed in groups. Small groups compel students to participate and develop autonomy.

For many teachers, developing autonomous, responsible students is a goal of their teaching. As such,  a wise use of small groups in a large class can help to at least partially achieve this goal.

Supports Mixed Abilities

The  use of groups can help to support students of varying abilities. Through combining strong students with those of moderate and low ability, the students are able to support one another in order to group. This can actual be a form of differentiated instruction support not by the teacher but by the students.

Instead of the teacher adjusting their teaching for each student. The strong students adjust how they explain and do things to accommodate the struggling students. This takes careful group selection on the part of the teacher but can be a powerful tool.

Social

For the outgoing members of the class, group work is just an enjoyable experience. It is common for students to gain energy just from being around each other. Group work can create a synergy that is difficult to capture in a larger whole-class experience

In addition, for those who are shy, group work allows for chances to share and speak in a smaller setting. This allows for students to communicate with a lower risk of criticism. This allows for students to focus on meaning and the exchange of ideas rather than on looking good.

Conclusion

Group work is by no means a cure-all for the problems in a classroom.  Rather, group work provides one way in which to stimulate language acquisition. Like any strategy, group work should be used in combination with other teaching strategies in the classroom.

 

Student and Teacher Talk in the ESL Classroom

Student and Teacher talk refers to the variety of ways in which a language teacher communicates with their students in the classroom. Generally, teacher talk can be divided into indirect and direct influences that shape the interaction of the students with the teacher and each other. Student talk is more complex to explain but has some common traits. This post will explain the two types of influence that are under teacher talk as well as common characteristics of student talk.

Indirect Influences

Indirect influences is teacher talk that is focused on feelings, asking questions, and using student ideas. The focus on feelings is accepting and acknowledging how students feel. This can also involve praising the students for their work by explaining what they have done well.

Other forms of indirect influences includes using student ideas. The ideas can be summarized by the teacher or they can be repeated verbatim. Either way allows the students to contribute to the class discussion.

Lastly, another indirect influence is asking questions. This is a common way to stimulate discussion. The questions asked must be ones in which the teacher actually expects an answer.

Generally, indirect influences are often soft and passive in nature. This is in direct contrast to direct influences

Direct Influences 

Direct influences are more proactive and sometimes aggressive in nature.  Examples include give directions and or information. In each case, the teacher is clearly in control and trying to lead the class.

Other types of direct influences includes criticism. Criticism can be of student behavior or of the response of a student. This is clearly sending a message to the student and perhaps the class about what are acceptable actions in discussions.

Student Talk

When students talk it is usually to give a specific or open-ended response. A specific response is one in which there is only one answer. An open-ended response can have a multitude of answers.

Students can also respond with silence. This can happen as a result of the inability to express oneself or not understanding the question. Confusion happens when students are all speaking at the same time.

Student may also respond using their native language. This is normally avoided in TESOL but there are times where native language responses are needed for clarification.

Conclusion

Communication in the classroom can show itself in many different. The insights provided hear give examples of the various forms of communication that can happen in a language classroom.

Understanding Techniques in Language Teaching

Technique is a core term in the jargon of language teaching. This leads to people using a term without really knowing what in means. In simple  terms, a technique is any task/activity that is planned in a language course. Such a broad term makes it difficult to make sense of what exactly a technique is.

This post will try to provide various ways to categorize the endless see of techniques available in language teaching.

Manipulation to Interaction

One way to assess techniques is along a continuum from manipulation to interaction. A manipulative technique is one in which the teacher has complete control and expects a specific response from the students. Examples of this includes reading aloud, choral repetition, dictation

Interactive techniques are ones in which the students response is totally open. Examples of interactive techniques includes role play, free writing, presentations, etc.

Automatic, Purposeful, Communicative Drills

Another continuum that can be used is seeing techniques as automatic, purposeful, or communicative drill. An automatic drill technique has only one correct response. An example would be a repetition drill in which the students repeat what the teacher said.

A purposeful drill technique has several acceptable answers. For example, if the teacher ask the students “where is the dog”? The students can say “it’s outside” or “the dog is outside” etc.

Restricted to Free

The last continuum that can be used is restricted to free. This continuum looks at techniques from the position of who has the power. Generally, restricted techniques are ones that are teacher-centered, closed-ended, with high manipulation. Free techniques are often student-centered, open-ended, with unplanned responses.

Conclusion

All levels of language teaching should have a mixture of techniques from all over any of the continuums mentioned in this post. It is common for teachers to have manipulative and automatic techniques for beginners and interactive and free techniques for advanced students. This is often detrimental particularly to the beginning students.

The continuums here are simply for attempting to provide structure when a teacher is trying to choose techniques. It is not a black and white matter in classifying techniques. Different teachers while classify the same techniques in different places in a the continuum. As such, the continuums should guide one’s thinking and not control it.

Institutional Context of Language Teaching

The context in which language teaching happens influences how the language is taught to the students and how the teacher approaches language instruction. Generally, the two most two most common context in which formal language instruction takes places is at the primary/secondary and tertiary levels.

How language is viewed at these two levels depends on whether they see the mother-tongue of the students as subtractive (negative) or additive (positive) to acquiring the target language. The purpose of this post is to explain how language teaching is approached based on these two context.

Primary/Secondary

There are several language models used at the primary/secondary level. Some of these models include submersion, immersion, and bilingualism.

Submersion is a model in which the student is thrown into the new language without any or little support. This is derived from a subtractive view of the mother tongue. Naturally, many students struggle for years with this approach.

Immersion allows for students to have content-area classes with other students who have the same mother tongue with support from a trained ESL teacher. The mother tongue is seen as additive in this context

Bilingualism involves receiving instruction in both the first and second language. This can be done for the purpose of transitioning completely to the second language or to try and maintain or enrich the first language.

Tertiary

At the tertiary level, many of the same models of language are employed but given slightly different names. Common models at the tertiary level include pre-academic, EAP, ESP, and social.

Pre-Academic language teaching is the tertiary equivalent of submersion. Generally, the students are taught English with a goal of submerging them in the target language when they begin formal studies. This is the same as mainstreaming which is one form of submersion

EAP or English for Academic Purposes is essentially advanced language teaching that focuses on scholarly type subject matter in pre-academic language programs.  This is often difficult to teach as it requires a refinement of how the student approaches the language.

ESP or English for Specific Purposes is a general form of EAP. Instead of the focus being academic as in EAP, ESP can be focused on business, tourism, transportation, etc. Students learn English focused on a specific  industry or occupation.

Social programs for English provide a brief exposure to English for the sake of enjoyment. Students learn the basics of listening and speaking in a non-academic context.

Conclusion

There are various programs available to support students in acquiring a language. The programs vary in essentially no support to support in maintaining both languages. Which program to adopt at an institution depends on the context of learning and the philosophy of the school.

Teaching Advanced ESL Students

Advanced ESL students have their own unique set of traits and challenges that an ESL teacher must deal. This post will explain some of these unique traits as well as how to support advanced ESL students.

Supporting Advanced ESL Students

By this point, the majority of the language processing is automatic. This means that the teacher no longer needs to change the speed at which they talk in most situations.

In addition, the students have become highly independent. This necessitates that the teacher focus on supporting the learning experience  of the students rather than trying to play a more directive role.

The learning activities used in the classroom can now cover a full range of possibilities. Almost all causal reading material is appropriate. Study skills can be addressed at a much deeper level. Such skills as skimming, scanning, determining purpose, etc. can be taught and addressed in the learning. Students can also enjoy debates and author opinion generating experiences.

The Challenges of Advanced ESL Students

One of the challenges of advanced students is they often have a habit of asking the most detailed questions about the most obscure aspects of the target language. To deal this requires a PhD in linguistics or the ability to know what the students really need to know and steer away from mundane grammatical details. It is very tempting to try and answer these types of questions but the average native-speaker does not know all the details of imperfect past tense but rather are much more adept at using it.

Another frustrating problem with advanced students is the ability to continue to make progress in their language development. With any skill, as one gets closer to mastery, the room for improvement becomes smaller and smaller. To move from an advanced student to a superior student takes make several small rather than sweeping adjustments.

This is one reason advanced students often like to ask those minute grammar questions. These small question is where they know they are weak when it comes to communicating. This can be especially stressful if the student is a few points away from reaching some sort of passing score on an English proficiency exam (IELTS, TOEFL, etc.). Minor adjustments need to reach the minimum score are difficult to determine and train.

Conclusion

After beginners, teaching advanced esl students is perhaps the next most challenging teaching experience. Advanced ESL students have a strong sense of what they know and do not know. What makes this challenging is the information they need to understand can be considered some what specializes and not easy to articulate for many teachers.

Using Groups and Groupings in Activities in Moodle

Making groups and groupings are two features in Moodle that can be used for collaboration and or for organizational purposes in a class. This post will provide examples of how to use groups in an activity in Moodle

Using Groups/Groupings in a Forum

Groups and Groupings can be used in a Forum in order to allow groups to interact during a discussion topic. It is assumed that you already know how to make a forum in Moodle.  Therefore, the instruction in this post will start from the settings window for forums in Moodle.

  1.  The option that we need to adjust to use groups in forums is the “Common Module Settings”. If you click on this, you will see the following.

Screenshot from 2016-12-05 09-43-54.png

2. Depending on your goals there are several different ways that groups can be used.

  • Group mode can be set to visible or separate groups. If groups are visible different groups can see each others discussion but they can only post in their own groups discussion.
  • If separate group is selected. Groups will only be able to see their own group’s discussion and no other.
  • If the grouping feature is used. Only the groups that are a part of that grouping are added to the forum. The group mode determines if the groups can see each other or not.

In this example we will select group mode set to “visible groups” and groupings to “none once you click “save and display” you will see the following.

Screenshot from 2016-12-05 11-24-56.png

3. To see what each group said in their discussion click “all participants” and a drop down menu will be displayed that shows each group.

Using Grouping for Assignments

To use groups in assignments you repeat the steps above. In this example, we will use the grouping feature.

  1. The features are viewable in the picture below. I selected “separate groups” and I selected the grouping I wanted. This means only groups in the grouping will have this assignment available to them

screenshot-from-2016-12-05-11-28-00

2. Another set a features you want to set for an assignment is the “group submission settings”. The options are self-explanatory but here is what I selected.

Screenshot from 2016-12-05 11-31-12.png

3. Click save a display and you will see the following

Screenshot from 2016-12-05 11-32-33.png

The red messages just states that some people are in more than one group or not in any group. For this example, this is not a problem as I did not assign all students to a group.

Conclusion

The concepts presented here for forums and assignments apply to most activities involving groups in Moodle. Group is very useful for large classes in which students need a space in which they can having meaningful communication with a handful of peers.

Making Auto-Groups and the Grouping Feature in Moodle

In a prior post, we looked at how to make groups manually in Moodle. In this post, we will look at two additional features in making groups and they are

  • The Auto-group feature
  • The Grouping feature

Making Auto-Groups

Auto-groups allows you to have Moodle make groups based on a criteria you give it. If the  characteristics of the groups doesn’t matter that is a fast convenient way to put students in groups. Below are the steps

  1. After logging in and going to a course where you have administrative privilege go to course administration->users->groups. If you do this correctly you should see the following

screenshot-from-2016-11-30-08-30-37

2. Click on Auto-Create Groups and you will see the following

Screenshot from 2016-12-02 08-07-05.png

3. The page is mostly self explanatory. Groups can be formed based on the number of groups you want or the number of people per group. Group formation can also be limited by role in the class or by last name, ID, etc. Before groups are finalized you can use the preview button to look at the potential groups. Below is an example of a completed group formation

Screenshot from 2016-12-02 08-12-52.png

The auto-group feature made 12 groups and the names of the members are listed in the table. Once you are satisfied you click submit and return to the previous page

Screenshot from 2016-12-02 08-14-02.png

Using Groupings

Groupings allows you to place several groups into a “grouping” this allows you to add several groups to an activity at once. In order to use groupings you must first make groups which we have already done. Just like with the group feature in which the same person can be a member of several groups so can one group be a member of several groupings. Below are the steps to making groupings

  1. On the groups page, click on grouping and you will see the following

Screenshot from 2016-12-02 08-22-03.png

2. Click on  create grouping and you will see the following

Screenshot from 2016-12-02 08-23-26.png

3. We will give the grouping a name and click save changes and this will send you to the previous page shown below

Screenshot from 2016-12-02 08-34-43.png

4. To add a group to the grouping, you need to click on the people icon under the edit column and you will see the following

Screenshot from 2016-12-02 08-36-06.png

5. Now we will pick several groups to add to our grouping and click add as shown below

screenshot-from-2016-12-02-08-37-37

6. When you are done adding groups you click on back to groupings to finish the process as shown below

Screenshot from 2016-12-02 08-38-46.png

Conclusion

We now know how to make groups manually and automatically. We also know how to create groupings. However we have not yet learn how to actually using groups and or groupings in Moodle learning experiences. This will be a topic of a future post

Making Groups in Moodle

One of the many features available for teachers to use is the group mode for activities within a course in Moodle. This post will look at how to setup groups in a Moodle course.

What to Use the Group Mode For?

As with other features in Moodle, the challenge with the group mode is that you can use it for almost anything. The unlimited variety in terms of the application of the group mode makes it challenge for novices to understand and appreciate it. This is because as humans we often want a single clear way  to use something. Below are several different ways in which the group mode can be used in a Moodle course.

  • If the same Moodle course is used for two or more different sections the group mode can be used to put students in the same moodle course into different groups by section. For example, if a teacher is teaching two sections of English 101, section 1 would be one group and section 2 would be the other group.
  • Groups can also be used so that only certain groups see certain things in a Moodle course. In Moodle, you can limit who sees what be restricting to a certain group.
  • A more traditional use is to have students placed in groups to complete group assignments. Placing them in groups allows the group to submit one assignment that Moodle gives all members of the group credit for when it is marked.

If this is not confusing enough, you can also have students in several different groups simultaneously if you wanted. Therefore, whenever you are trying to use Moodle you need to consider what your goal is rather than whether it is possible to do it in Moodle. As stated before, the problem is the flexibility of Moodle and not its inability to facilitate a learning task.

In this post, we are only going to learn how to make groups. In a future post, we will look at using groups in terms of teaching and assignments.

Creating Groups in Moodle

  1. After logging into Moodle and selecting a course, you need to go to course administration->users->groups. If you do this correctly you should see the following

Screenshot from 2016-11-30 08-19-06.png

2. There are several things to mention before continuing

First, there are two different ways to create groups. You can create them manually by clicking on “create groups” or you can have Moodle make the groups using the “Auto-create groups” button. The auto-group option will be explained in a later post as welling as the grouping feature.

Second, there is a tab called “grouping” this is a feature that allows you to create a group of groups. In other words, several groups can be assigned to a grouping.  This allows you to assign several groups to an activity simultaneously rather than having to add each on manually. This is a great feature for a course that has two sections and each section has group activities. For now we will learn how to make groups manually.

Lastly, the column on the left, called “groups” will display the name of any groups that are created while the column on the left, called “members of” will contain the names of people who are a part of the group. Right now both are empty because there are no groups yet.

3. Click on the “create group” group button and you will see the following.

Screenshot from 2016-11-30 08-26-46.png

4. You now need to give the group a name. You also have the privilege to add other information if you want such as description or even a picture to represent the group. After providing the needed information you need to click “save changes” in order to see the following.

Screenshot from 2016-11-30 08-30-37.png

5. To add members to our practice group we need to click on the “add/remove” button. After doing this, you will see the following.

Screenshot from 2016-11-30 08-33-46.png

6. There are two columns, “potential members” and “group members.” To add people to the “group members” section just highlight whoever you want in the “potential members” side and click “add”. Below is an example of this

Screenshot from 2016-11-30 08-53-02.png

Just a note, at the bottom of both the “group member” and “potential member” list is a search function that can be used to find specific people in either section.

7. After placing people in the group, you can click on the “back to group” button. You will see the following.

Screenshot from 2016-11-30 09-01-57.png

The group name is displayed on the left and the members of the group are displayed on the right.

Conclusion

In this post we learned how to create groups. However, we have not learned yet how to use groups in a moodle course yet. This will be explained in a future post.

Teaching Intermediate ESL Students

Intermediate ESL students are often the easiest group of students to teach. Usually, they have basic skills in the language while still having plenty of untapped upside potential to develop.

Unlike beginners who have no language skills and thus require a patient and thorough teacher and advanced students who need advanced knowledge minute knowledge of the language, intermediates have some skill without expertise. Therefore, for beginning teachers, it is usual best to start their teaching career working with beginners.

This post will provide some suggestions on how to approach  and teach intermediate level ESL students.

Automaticity and the Role of the Teacher

By this level, students are somewhat automatic in their speaking process. This allows the teacher to back off from being the center of the classroom in order to allow more student-student interaction as the student are able to be much more creative in their learning experience. Therefore, the learning can now be much more learner-centered with a significant reduction in the amount of talking the teacher does.

Again, for beginner teachers, the students know enough to not require intensive hand-holding but not enough to challenge the expertise of the teacher. This combines to create an excellent initial teaching experience for many.

Focus on Perfection

Intermediate students begin to become obsess with grammar. They want everything they say to be “perfect.” This focus on over analyzing everything they say can impair fluency and accuracy as they criticized themselves for every slip up.

The goal of the teacher at this point  is to help the students take their focus off of the accuracy of what they are saying and focus on the flow of the conversation. They should be accurate enough to be understood with more complex correction coming later. Grammar has it place in a limited manner but should not dominant the learning experience.

Learning Activities and Techniques

Intermediate students can learn in a more cooperative environment. Some examples of activities suitable for intermediate  students includes role-plays, discussion, problem-solving and interviews.

The teacher takes on more of a supervisory role in the learning of the students. The provides guidance as necessary as the students determine what to do themselves.

Conclusion

Teaching at the intermediate level is good for many people new to teaching a language. A new teacher can focus on working with students with some competency without the pressure of exit-examines are people have have no clue about the language.

Inquiry Learning

From the archives

educational research techniques

Inquiry learning is form of indirect instruction. Indirect instruction is teaching in which the students are actively involved in their learning by seeking solutions to problems or questions. In inquiry learning, students develop and investigate questions that they may have. The focus in inquiry learning is on what the students want to learn with some support from the teacher about a topic. Below are the steps of inquiry learning.

  1. Ask
  2. Investigate
  3. Create
  4. Discuss
  5. Reflect

Step 1: Ask

The teacher begins this process by taking the topic of the lesson and turning it into a question for the students to consider. For example, if the topic of a lesson is about flowers, a question to ask would be “How are flowers different from each other?” This is called the teacher-initiated stage of asking.

The student then develop their own questions that should help to answer the main question posed by the…

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Teaching Beginning ESL Students

Beginning ESL students have unique pedagogical needs that make them the most difficult to teach. It’s similar to the challenge of teaching kindergarten. The difficulty is not the content  but rather stripping what is already a basic content into something that is understandable for the most undeveloped of students. Some of the best teachers cannot do this.

This post will provide some suggestions on how to deal with beginning ESL students.

Take Your Time

Beginning students need a great deal of repetition. If you have ever tried to learn a language you probably needed to hear phrases many times to understand them. Repetition helps students to remember and imitate what they heard.

This means that the teacher needs to limit the amount of words, phrases, and sentences they teach. This is not easy, especially for new teachers who are often put in charge of teaching beginners and race through the curriculum to the frustration of the beginning students.

Repetition and a slow pace helps students to develop the automatic processing they need in order to achieve fluency. This can also be enhanced by focusing on purpose in communication rather than the grammatical structure of language.

The techniques use din class should short and simple with a high degree variety to offset the boredom of repetition. In other words, find many ways to teach one idea or concept.

Who’s the Center

Beginning students are highly teacher-dependent because of their lack of skills. Therefore, at least initially, the classroom should probably be teacher-centered until the students develop some basic skills.  In general, whenever you are dealing with a new subject the students are totally unfamiliar with it is better to have a higher degree of control of the learning experience.

Being the center of the learning experiences requires the teacher to provide most of the examples of well-spoken, written English. Your feedback is critical for the students to develop their own language skills. The focus should be more towards fluency rather than accuracy.

However, with time cooperative and student-centered activities can become more prominent. In the beginning, too much freedom can be frustrating for language learners who lack any sort of experience to draw upon to complete activities. Within a controlled environment, student creativity can blossom.

Conclusion

Being a beginning level ESL teacher is a tough job. It requires a skill set of patience, perseverance, and a gift at simplicity.  Taking your time and determining who the center of learning is are ways in which to enhances success for those teaching beginners

 

Extrinsic & Intrinsic Motivation

Extrinsic and Intrinsic motivation are two extremes of a continuum of motivation. Extrinsic motivation  is the desire to do something coming from outside of the person. Intrinsic motivation is the desire to do something coming from within a person. This post will explain some of the pros and cons of each type of motivation as they relate to education.

Extrinsic Motivation

Extrinsic motivation is an external force that compels someone to do something. For example, it is common for students to study in order to prepare for a test. The test provides an extrinsic motivation to study. If there was no test, the students probably would not study.

This leads to one of the first problems with extrinsic motivation which is its addictive nature. A student will get use to the extrinsic motivation and never become motivated themselves to complete a task.

Extrinsic motivation can also lead to either of the  following. In some situations extrinsic motivation can lead to a competitive classroom environment in which students try to out do each other due to the pressure. In other situations, the students will band together to push back against the extrinsic motivation by the teacher. Either situation can lead to academically dishonesty practice such as cheating and plagiarism.

Generally, extrinsic motivation is negative. When people are doing something willing and then are told to do it they often lose motivation. This is because something that used to be done by choice is now forced upon them.

The only exception to this is positive feedback. When people are given compliments on how they are doing something it helps them to stick to the task.

Intrinsic Motivation

Intrinsic motivation is the desire to complete something coming from within. For many, intrinsic motivation is one of the ultimate goals of learning. Teachers often want students to develop a desire to learn and grow on their own after they complete their studies.

To achieve this, a teacher must become a facilitator of learning. A facilitator of learning is one who provides students with a context in which the students can set their own learning goals. A primary component of this is allowing choice in the classroom. Choice can be given in types of assignment, how to complete assignments, or other ways.

There are also affective measures that can be taken. Examples include developing a positive relationships with students, having a relaxing classroom environment, and increasing self-confidence.

Content-based and cooperative  learning activities both provide opportunities for students to develop intrinsic motivation. The goal is to develop independent learners who can set their own goals and achieve them.

Conclusion

Motivation is necessary. The question  is where will the motivation come from. In education both forms of motivation are present. However, the goal should normally be to strive for intrinsic motivation when this is possible.

First and Target Language Conflict and Compromise

In an interesting contradiction of language acquisition it is a given fact that the greatest challenge and blessing in learning a second language is the first language. For many people they wonder how the first language can be an advantage and a disadvantage at the same time.

In order to understand this mystery of second language acquisition we will look at interference, facilitating, as well as suggestion for teachers tot help students to deal with the challenges of the first language in second language acquisition.

Interference and Facilitating

A person’s first language can be a problem through what is called interfering. Interference is the assumptions a person brings from their first language to the second language.

Each language has distinct rules that governs in use in the form of syntax, semantics, morphology, phonology, etc. When a person learns a new language they bring these rules with them to the new language. Therefore, they are breaking the rules of the target language do to their obedience to the rules of their native language.

Below is an example of a native English speaker trying to speak Spanish

English Sentence: I want the red car
Spanish with English rules: Yo quiero el rojo coche
Correct Spanish Version: Yo quiero el coche rojo

In the simple example above, the native English speaker said “rojo coche” (red car) instead of “coche rojo” (car red) in Spanish. In other words, the English speaker  put the adjective before the noun instead of the noun before the adjective. This is a minor problem but it does sound strange to a native Spanish speaker.

It needs to be noted that the first language can also help in communicating in the second and this is called facilitating. In the example above, the majority of what the English speaker said is correct. The subject verb object order was correct as an example. This is because when the rules of the language are the same the facilitate the person’s learning of the target language and when the rules are different they interfere.

Helping with Interference and Facilitating

The goal of a teacher is to help a student to discard interference and hold on to facilitating. To do this a teacher needs to listen to the errors a student makes to understand what the problems are. Often it is good to explain the error the student is making and what native language rule they are clinging to that is causing the problem.

Another goal is to encourage direct thinking in the target language. This prevents translation and all of the errors that come with that.

Lastly, recognizing the benefits of facilitating by showing how the two languages are similar can help students. Generally, teachers focus on interference rather than facilitating but an occasional acknowledge of facilitation is beneficial.

Conclusion

A teacher needs to understand that the first language  of their students is not always an enemy. The first language provides a foundation for the development of the target language. Through working with what the students already know the teacher can help to develop strong language skills in the target.

Language Ego

Imagine that you are working as an ESL teacher at a university. Specifically, you are working with international students who are trying to complete their English language  proficiency in order to study for their PhD.

These students are without a doubt intelligent. They all have a master degrees. However, despite their talent and abilities, they are still babies when it comes to fluency in English. The students become exceedingly frustrated as they have to be reduce to such an elementary experience of drills and skits in order to be prepared for graduate studies. In order to achieve their dream they must develop an identity in the English language.

To make an even stronger example, imagine  you are an English teacher in your country where English is a Foreign Language and have been teaching English for years. You decide to go for a PhD in an English speaking country. You take the TOEFL or IELTS and the results indicate that you need to take ESL courses before you can study. Here you are, an experienced English teacher back home, sitting through intermediate/advanced ESL courses. This is a serious but common wake up call for many non-native ESL teachers with advanced degree aspirations.

This experience frustration and fragility  as one learns a new language is called language ego. This post will define language ego as well as strategies for making this experience more tolerably for students.

Defining Language Ego

Language ego is a sense of inferiority as one tries to learn a new language. People are excellent at communicating in their own language and communicate boldly in it. This confidence in one’s native language makes one highly resilient in one’s mother tongue. This why native speaker’s often ignore comments on how to communicate in the target language when these comments come from non-native speakers and even from native-speakers. We all know our own language and care little for feedback from others

However, this confidence, stubbornness, and resilience disappears when learning another language. Now, it is common for people to become defensive and sensitive as they try to communicate with limited tools.

This experience only becomes worst as one gets older. Children already have limited cognitive ability compared to adults so when they communicate in a new language they have much lower expectations in terms of talking and communicating. For adults, who often have complex, abstract ideas to share, it is frustrating to have to be reduce to speaking about mundane topics in a second language.

Helping Student with Language Ego

In order to support students during this experience it is important to remember the following points.

  • Task should be  challenging but not overwhelming.  This is a general concept in education but much more important in language teaching. Excessive failure will destroy the fragile ego of many ESL students.
  • Different students will struggle in different ways. This means a teacher should be strategic in terms of who they call on, correct publicly, the level of toughness, etc. as all of these decisions will affect students in different ways.
  • Acknowledging the frustration as the students learn the language can also help with coping.

Conclusion

Learning a language involves changes to one’s self. This means that the ego is often threaten when acquiring a language. The intensity of this is only increase when one learns a language a an adult when compared to a child. As such, teachers need to support adults and children during this experience.