# Category Archives: Uncategorized

Students frequently struggle with understanding what they read. There can be many reasons for this such as vocabulary issues, to struggles with just sounding out the text. Another common problem, frequently seen among native speakers of a language, is the students just read without taking a moment to think about what they read. This lack of reflection and intellectual wrestling with the text can make so that the student knows they read something but knows nothing about what they read.

In this post, we will look at several common strategies to support reading comprehension. These strategies include the following…

Walking a Student Through the Text

As students get older, there is a tendency for many teachers to ignore the need to guide students through a reading before the students read it. One way to improve reading comprehension is to go through the assigned reading and give an idea to the students of what to expect from the text.

Doing this provides a framework within the student’s mind in which they can add the details to as they do the reading. When walking through a text with students the teacher can provide insights into important ideas, explain complex words, explain visuals, and give general ideas as to what is important.

Asking question either before or after a reading is another great way to support students understanding. Prior questions give an idea of what the students should be expected to know after reading. On the other hand, questions after the reading should aim to help students to coalesce the ideals they were exposed to in the reading.

The type of questions is endless. The questions can be based on Bloom’s taxonomy in order to stimulate various thinking skills. Another skill is probing and soliciting responses from students through encouraging and asking reasonable follow-up questions.

Develop Relevance

Connecting what a student knows what they do not know is known as relevance.If a teacher can stretch a student from what they know and use it to understand what is new it will dramatically improve comprehension.

This is trickier than it sounds. It requires the teacher to have a firm grasp of the subject as well as the habits and knowledge of the students. Therefore, patience is required.

Conclusion

Reading is a skill that can improve a great deal through practice. However, mastery will require the knowledge and application of strategies. Without this next level of training, a student will often become more and more frustrated with reading challenging text.

Grading has recently been under attack with people bringing strong criticism against the practice. Some schools have even stopped using grades altogether. In this post, we will look at problems with grading as well as alternatives.

It Depends on the Subject

The weakness of grading is often seen much more clearly in subjects that have more of a subjective nature to them from the Social sciences and humanities such as English, History, or Music. Subjects from the hard sciences such as biology, math, and engineering are more objective in nature. If a student states that 2 + 2 = 5 there is little left to persuasion or critical thinking to influence the grade.

However, when it comes to judging thinking or musical performance it is much more difficult to assess this without bringing the subjectivity of opinion. This is not bad as a teacher should be an expert in their domain but it still brings an arbitrary unpredictability to the system of grading that is difficult to avoid.

Returning to the math problem, if a student stats 2 +2 =  4 this answer is always right whether the teacher likes the student or not. However, an excellent historical essay on slavery can be graded poorly if the history teacher has issues with the thesis of the student. To assess the essay requires subjective though into the quality of the student’s writing and subjectivity means that the assessment cannot be objective.

Obsession of Students

Many students become obsess and almost worship the grades they receive. This often means that the focus becomes more about getting an ‘A’ than on actually learning. This means that the students take no-risk in their learning and conform strictly to the directions of the teacher. Mindless conformity is not a sign of future success.

There are many comments on the internet about the differences between ‘A’ and ‘C’ students. How ‘A’ students are conformist and ‘C’ students are innovators. The point is that the better the academic performance of a student the better they are at obeying orders and not necessarily on thinking independently.

There are several alternatives to grading. One of the most common is Pass/fail. Either the student passes the course or they do not. This is common at the tertiary level especially in highly subjective courses such as writing a thesis or dissertation. In such cases, the student meets the “mysterious” standard or they do not.

Another alternative is has been the explosion in the use of gamification. As the student acquires the badges, hit points, etc. it is evidence of learning. Of course, this idea is applied primarily at the K-12 level but it the concept of gamification seems to be used in almost all of the game apps available on cellphones as well as many websites.

Lastly, observation is another alternative. In this approach, the teacher makes weekly observations of each student. These observations are then used to provide feedback for the students. Although time-consuming this is a way to support students without grades.

Conclusion

As long as there is education there must be some sort of way to determine if students are meeting expectations. Grades are the current standard. As with any system, grades have their strengths and weaknesses. With this in mind, it is the responsibility of teachers to always search for ways to improve how students are assessed.

# Supporting ESL Student’s Writing

ESL students usually need to learn to write in the second language. This is especially true for those who have academic goals. Learning to write is difficult even in one’s mother tongue let alone in a second language.

In this post, we will look at several practical ways to help students to learn to write in their L2. Below are some useful strategies

• Build on what they know
• Encourage coherency in writing
• Encourage collaboration
• Support Consistency

Build on Prior Knowledge

It is easier for most students to write about what they know rather than what they do not know.  As such, as a teacher, it is better to have students write about a familiar topic. This reduces the cognitive load on the students allows them to focus more on their language issues.

In addition, building on prior knowledge is consistent with constructivism. Therefore, students are deepening their learning through using writing to express ideas and opinions.

Support Coherency

Coherency has to do with whether the paragraph makes sense or not. In order to support this, the teacher needs to guide the students in developing main ideas and supporting details and illustrate how these concepts work together at the paragraph level. For more complex writing this involves how various paragraphs work together to support a thesis or purpose statement.

Students struggle tremendously with these big-picture ideas. This in part due to the average student’s obsession with grammar. Grammar is critical after the student has ideas to share clearer and never before that.

Encourage Collaboration

Students should work together to improve their writing. This can involve peer editing and or brainstorming activities. These forms of collaboration give students different perspectives on their writing beyond just depending on the teacher.

Collaboration is also consistent with cooperative learning. In today’s marketplace, few people are granted the privilege of working exclusively alone on anything.  In addition, working together can help the students to develop their English speaking communication skills.

Consistency

Writing needs to be scheduled and happen frequently in order to see progress at the ESL level. This is different from a native speaking context in which the students may have several large papers that they work on alone. In the ESL classroom, the students should write smaller and more frequent papers to provide more feedback and scaffolding.

Small incremental growth should be the primary goal for ESL students. This should be combined with support from the teacher through a consistent commitment to writing.

Conclusion

Writing is a major component of academic life. Many ESL students learning a second language to pursue academic goals. Therefore, it is important that teachers have ideas on how they can support ESL student to achieve the fluency they desire in their writing for further academic success.

# Tips for Teaching Online

Teaching online is a unique experience due in part to the platform of instruction. Often, there is no face to face interaction and all communication is in some sort of digital format. Although this can be a rewarding experience there are still several things to consider when teaching in this format. Some tips for successful online teaching include the following.

• Having a presence
• Being consistent

All teaching involves advance planning. However, there are those teaching moments in a regular classroom where a teacher can change midstream to hit a particular interest in the class. In addition, more experienced teachers tend to plan less as they are so comfortable with the content and have an intuitive sense of how to support students.

In online teaching, the entire course should be planned and laid out accordingly before the course starts. It is a nightmare to try and develop course material while trying to teach online. This is partially due to the fact that there are so many reminders and due dates sprinkled throughout the course that are inflexible. This means a teacher must know the end from the beginning in terms of what the curriculum covers and what assignments are coming. Changing midstream is really tough.

In addition, the asynchronous nature of online teaching means that instructional material must be thoroughly clear or students will be lost. This again places an emphasis on strong preparation. Online teaching isn’t really for the person who likes to live in the moment but rather for the person who plans ahead.

Have Presence

Having presence means making clear that you are monitoring progress and communicating with students frequently. When students complete assignments they should receive feedback. There should be announcements made in terms of assignments due, general feedback about activities, as well as Q&A with students.

Many people think that teaching online takes less time and can have larger classes. This is far from the case. Online teaching is as time intensive as regular teaching because you must provide feedback and communication or the students will often feel abandon.

An online teacher must be familiar and a proponent of technology. This does not mean that you know everything but rather you know how to get stuff done. You don’t need a master in web design but knowing the basics of HTML can really help when communicating with the IT people.

Whatever learning management system you use should actually be familiar with it and not just a consumer. Too many people just upload text for students to read and provide several forums and call that online learning. In many ways, that’s online boredom, especially for younger students.

Consistency

Consistency is about the user experience. The different modules in the course should have the same format with different activities. This way, students focus on learning and not trying to figure out what you want them to do. This applies across classes as well. There needs to be some sense of stability in terms of how content is delivered. There is no single best way but it needs to similar within and across courses for the sake of learning.

Conclusion

These are just some of many ideas to consider when teaching an online course. The main point is the need for preparation and dedication when teaching online.

# Principal Component Regression in R

This post will explain and provide an example of principal component regression (PCR). Principal component regression involves having the model construct components from the independent variables that are a linear combination of the independent variables. This is similar to principal component analysis but the components are designed in a way to best explain the dependent variable. Doing this often allows you to use fewer variables in your model and usually improves the fit of your model as well.

Since PCR is based on principal component analysis it is an unsupervised method, which means the dependent variable has no influence on the development of the components. As such, there are times when the components that are developed may not be beneficial for explaining the dependent variable.

Our example will use the “Mroz” dataset from the “Ecdat” package. Our goal will be to predict “income” based on the variables in the dataset. Below is the initial code

library(pls);library(Ecdat)
data(Mroz)
str(Mroz)
## 'data.frame':    753 obs. of  18 variables:
##  $work : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ... ##$ hoursw    : int  1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
##  $child6 : int 1 0 1 0 1 0 0 0 0 0 ... ##$ child618  : int  0 2 3 3 2 0 2 0 2 2 ...
##  $agew : int 32 30 35 34 31 54 37 54 48 39 ... ##$ educw     : int  12 12 12 12 14 12 16 12 12 12 ...
##  $hearnw : num 3.35 1.39 4.55 1.1 4.59 ... ##$ wagew     : num  2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
##  $hoursh : int 2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ... ##$ ageh      : int  34 30 40 53 32 57 37 53 52 43 ...
##  $educh : int 12 9 12 10 12 11 12 8 4 12 ... ##$ wageh     : num  4.03 8.44 3.58 3.54 10 ...
##  $income : int 16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ... ##$ educwm    : int  12 7 12 7 12 14 14 3 7 7 ...
##  $educwf : int 7 7 7 7 14 7 7 3 7 7 ... ##$ unemprate : num  5 11 5 5 9.5 7.5 5 5 3 5 ...
##  $city : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 1 1 1 1 ... ##$ experience: int  14 5 15 6 7 33 11 35 24 21 ...

Our first step is to divide our dataset into a train and test set. We will do a simple 50/50 split for this demonstration.

train<-sample(c(T,F),nrow(Mroz),rep=T) #50/50 train/test split
test<-(!train)

In the code above we use the “sample” function to create a “train” index based on the number of rows in the “Mroz” dataset. Basically, R is making a vector that randomly assigns different rows in the “Mroz” dataset to be marked as True or False. Next, we use the “train” vector and we assign everything or every number that is not in the “train” vector to the test vector by using the exclamation mark.

We are now ready to develop our model. Below is the code

set.seed(777)
pcr.fit<-pcr(income~.,data=Mroz,subset=train,scale=T,validation="CV")

To make our model we use the “pcr” function from the “pls” package. The “subset” argument tells r to use the “train” vector to select examples from the “Mroz” dataset. The “scale” argument makes sure everything is measured the same way. This is important when using a component analysis tool as variables with different scale have a different influence on the components. Lastly, the “validation” argument enables cross-validation. This will help us to determine the number of components to use for prediction. Below is the results of the model using the “summary” function.

summary(pcr.fit)
## Data:    X dimension: 381 17
##  Y dimension: 381 1
## Fit method: svdpc
## Number of components considered: 17
##
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
##        (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps  6 comps
## CV           12102    11533    11017     9863     9884     9524     9563
## adjCV        12102    11534    11011     9855     9878     9502     9596
##        7 comps  8 comps  9 comps  10 comps  11 comps  12 comps  13 comps
## CV        9149     9133     8811      8527      7265      7234      7120
## adjCV     9126     9123     8798      8877      7199      7172      7100
##        14 comps  15 comps  16 comps  17 comps
## CV         7118      7141      6972      6992
## adjCV      7100      7123      6951      6969
##
## TRAINING: % variance explained
##         1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps
## X        21.359    38.71    51.99    59.67    65.66    71.20    76.28
## income    9.927    19.50    35.41    35.63    41.28    41.28    46.75
##         8 comps  9 comps  10 comps  11 comps  12 comps  13 comps  14 comps
## X         80.70    84.39     87.32     90.15     92.65     95.02     96.95
## income    47.08    50.98     51.73     68.17     68.29     68.31     68.34
##         15 comps  16 comps  17 comps
## X          98.47     99.38    100.00
## income     68.48     70.29     70.39

There is a lot of information here.The VALIDATION: RMSEP section gives you the root mean squared error of the model broken down by component. The TRAINING section is similar the printout of any PCA but it shows the amount of cumulative variance of the components, as well as the variance, explained for the dependent variable “income.” In this model, we are able to explain up to 70% of the variance if we use all 17 components.

We can graph the MSE using the “validationplot” function with the argument “val.type” set to “MSEP”. The code is below.

validationplot(pcr.fit,val.type = "MSEP")

How many components to pick is subjective, however, there is almost no improvement beyond 13 so we will use 13 components in our prediction model and we will calculate the means squared error.

set.seed(777)
pcr.pred<-predict(pcr.fit,Mroz[test,],ncomp=13)
mean((pcr.pred-Mroz$income[test])^2) ## [1] 48958982 MSE is what you would use to compare this model to other models that you developed. Below is the performance of a least squares regression model set.seed(777) lm.fit<-lm(income~.,data=Mroz,subset=train) lm.pred<-predict(lm.fit,Mroz[test,]) mean((lm.pred-Mroz$income[test])^2)
## [1] 47794472

If you compare the MSE the least squares model performs slightly better than the PCR one. However, there are a lot of non-significant features in the model as shown below.

summary(lm.fit)
##
## Call:
## lm(formula = income ~ ., data = Mroz, subset = train)
##
## Residuals:
##    Min     1Q Median     3Q    Max
## -27646  -3337  -1387   1860  48371
##
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.215e+04  3.987e+03  -5.556 5.35e-08 ***
## workno      -3.828e+03  1.316e+03  -2.909  0.00385 **
## hoursw       3.955e+00  7.085e-01   5.582 4.65e-08 ***
## child6       5.370e+02  8.241e+02   0.652  0.51512
## child618     4.250e+02  2.850e+02   1.491  0.13673
## agew         1.962e+02  9.849e+01   1.992  0.04709 *
## educw        1.097e+02  2.276e+02   0.482  0.63013
## hearnw       9.835e+02  2.303e+02   4.270 2.50e-05 ***
## wagew        2.292e+02  2.423e+02   0.946  0.34484
## hoursh       6.386e+00  6.144e-01  10.394  < 2e-16 ***
## ageh        -1.284e+01  9.762e+01  -0.132  0.89542
## educh        1.460e+02  1.592e+02   0.917  0.35982
## wageh        2.083e+03  9.930e+01  20.978  < 2e-16 ***
## educwm       1.354e+02  1.335e+02   1.014  0.31115
## educwf       1.653e+02  1.257e+02   1.315  0.18920
## unemprate   -1.213e+02  1.148e+02  -1.057  0.29140
## cityyes     -2.064e+02  7.905e+02  -0.261  0.79421
## experience  -1.165e+02  5.393e+01  -2.159  0.03147 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6729 on 363 degrees of freedom
## Multiple R-squared:  0.7039, Adjusted R-squared:   0.69
## F-statistic: 50.76 on 17 and 363 DF,  p-value: < 2.2e-16

Removing these and the MSE is almost the same for the PCR and least square models

set.seed(777)
lm.fit2<-lm(income~work+hoursw+hearnw+hoursh+wageh,data=Mroz,subset=train)
lm.pred2<-predict(lm.fit2,Mroz[test,])
mean((lm.pred2-Mroz$income[test])^2) ## [1] 47968996 Conclusion Since the least squares model is simpler it is probably the superior model. PCR is strongest when there are a lot of variables involve and if there are issues with multicollinearity. # Leave One Out Cross Validation in R Leave one out cross validation. (LOOCV) is a variation of the validation approach in that instead of splitting the dataset in half, LOOCV uses one example as the validation set and all the rest as the training set. This helps to reduce bias and randomness in the results but unfortunately, can increase variance. Remember that the goal is always to reduce the error rate which is often calculated as the mean-squared error. In this post, we will use the “Hedonic” dataset from the “Ecdat” package to assess several different models that predict the taxes of homes In order to do this, we will also need to use the “boot” package. Below is the code. library(Ecdat);library(boot) data(Hedonic) str(Hedonic) ## 'data.frame': 506 obs. of 15 variables: ##$ mv     : num  10.09 9.98 10.45 10.42 10.5 ...
##  $crim : num 0.00632 0.02731 0.0273 0.03237 0.06905 ... ##$ zn     : num  18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
##  $indus : num 2.31 7.07 7.07 2.18 2.18 ... ##$ chas   : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $nox : num 28.9 22 22 21 21 ... ##$ rm     : num  43.2 41.2 51.6 49 51.1 ...
##  $age : num 65.2 78.9 61.1 45.8 54.2 ... ##$ dis    : num  1.41 1.6 1.6 1.8 1.8 ...
##  $rad : num 0 0.693 0.693 1.099 1.099 ... ##$ tax    : int  296 242 242 222 222 222 311 311 311 311 ...
##  $ptratio: num 15.3 17.8 17.8 18.7 18.7 ... ##$ blacks : num  0.397 0.397 0.393 0.395 0.397 ...
##  $lstat : num -3 -2.39 -3.21 -3.53 -2.93 ... ##$ townid : int  1 2 2 3 3 3 4 4 4 4 ...

First, we need to develop our basic least squares regression model. We will do this with the “glm” function. This is because the “cv.glm” function (more on this later) only works when models are developed with the “glm” function. Below is the code.

tax.glm<-glm(tax ~ mv+crim+zn+indus+chas+nox+rm+age+dis+rad+ptratio+blacks+lstat, data = Hedonic)

We now need to calculate the MSE. To do this we will use the “cv.glm” function. Below is the code.

cv.error<-cv.glm(Hedonic,tax.glm)
cv.error$delta ## [1] 4536.345 4536.075 cv.error$delta contains two numbers. The first is the MSE for the training set and the second is the error for the LOOCV. As you can see the numbers are almost identical.

We will now repeat this process but with the inclusion of different polynomial models. The code for this is a little more complicated and is below.

cv.error=rep(0,5)
for (i in 1:5){
tax.loocv<-glm(tax ~ mv+poly(crim,i)+zn+indus+chas+nox+rm+poly(age,i)+dis+rad+ptratio+blacks+lstat, data = Hedonic)
cv.error[i]=cv.glm(Hedonic,tax.loocv)$delta[1] } cv.error ## [1] 4536.345 4515.464 4710.878 7047.097 9814.748 Here is what happen. 1. First, we created an empty object called “cv.error” with five empty spots, which we will use to store information later. 2. Next, we created a for loop that repeats 5 times 3. Inside the for loop, we create the same regression model except we added the “poly” function in front of “age”” and also “crim”. These are the variables we want to try polynomials 1-5 one to see if it reduces the error. 4. The results of the polynomial models are stored in the “cv.error” object and we specifically request the results of “delta” Finally, we printed “cv.error” to the console. From the results, you can see that the error decreases at a second order polynomial but then increases after that. This means that high order polynomials are not beneficial generally. Conclusion LOOCV is another option in assessing different models and determining which is most appropriate. As such, this is a tool that is used by many data scientist. # Conversational Analysis: Questions & Responses Conversational analysis (CA) is the study of social interactions in everyday life. In this post, we will look at how questions and responses are categorized in CA. Questions In CA, there are generally three types of questions and they are as follows… • Identification question • Polarity question • Confirmation question Identification Question Identification questions are questions that employees one of the five W’s (who, what, where, when, why). The response can be opened or closed-ended. An example is below Where are the keys? Polarity Question A polarity question is a question the calls for a yes/no response. Can you come to work tomorrow? Confirmation Question Similiar to the polarity question, a confirmation question is a question that is seeking to gather support for something the speaker already said. Didn’t Sam go to the store already? This question is seeking an affirmative yes. Responses There are also several ways in which people respond to a question. Below is a list of common ways. • Comply • Supply • Imply • Evade • Disclaim Comply Complying means give a clear direct answer to a question. Below is an example A: What time is it? B: 6:30pm Supply Supplying is the act of giving a partial response, that is often irrelevant and fails to answer the question. A: Is this your dog? B: Well…I do feed it once in awhile In the example above, person A asks a clear question. However, person B states what they do for the dog (feed it) rather than indicate if the dog belongs to them. Feeding the dog is irrelevant to ownership. Imply Implying is providing information indirectly to answer a question. A: What time do you want to leave? B: Not too late The response from person B does not indicate any sort of specific time to leave. This leaves it up to person A to determine what is meant by “too late.” Disclaim Disclaiming is the person stating they do not n]know the answer. A: Where are the keys? B: I don’t know Evade Evading is the act of answering with really answering the question A: Where is the car B: David needed to go shopping In the example above, person B never states where the car is. Rather, they share what someone is doing with the car. By doing this, the speaker never shares where the car is. Conclusions The interaction of a question and response can be interesting if it is examined more closely from a sociolinguistic perspective. The categories provided here can support the deeper analysis of conversation. # Make a Glossary in Moodle VIDEO How to make a glossary in Moodle # Terms Related to Language This post will examine different uses of the word language. There are several different ways that this word can be defined. We will look at the following terms for language. • Vernacular • Standard • National • Official • Lingua Franca Vernacular Language The term vernacular language can mean many different things. It can mean a language that is not standardized or a language that is not the standard language of a nation. Generally, a vernacular language is a language that lacks official status in a country. Standard Language A standard language is a language that has been codified. By this, it is meant that the language has dictionaries and other grammatical sources that describe and even prescribe the use of the language. Most languages have experienced codification. However, codification is just one part of being a standard language. A language must also be perceived of as prestigious and serve a high function. By prestigious it is meant that the language has influence in a community. For example, Japanese is a prestigious language in Japan. By high function, it is meant that the language is used in official settings such as government, business, etc., which Japanese is used for. National Language A national language is a language used for political and cultural reasons to unite a people. Many countries that have a huge number of languages and ethnic groups will select one language as a way to forge an identity. For example, in the Philippines, the national language is Tagalog even though hundreds of other languages are spoken. In Myanmar, Burmese is the national language even though dozens of other languages are spoken. The selection of the language is political motivate with the dominant group imposing their language on others. Official Language An official language is the language of government business. Many former colonized nations will still use an official language that comes from the people who colonized them. This is especially true in African countries such as Ivory Coast and Chad which use French as their official language despite having other indigenous languages available. Lingua Franca A lingua franca is a language that serves as a vehicle of communication between two language groups whose mother tongues are different. For example, English is often the de facto lingua franca of people who do not speak the same language. Multiple Categories A language can fit into more than one of the definitions above. For example, English is a vernacular language in many countries such as Thailand and Malaysia. However, English is not considered a vernacular language in the United States. To make things more confusing. English is the language of the United States but it is neither the National or Official Language as this has never been legislated. Yet English is a standard language as it has been codified and meets the other criteria for standardization. Currently, English is viewed by many as an international Lingua Franca with a strong influence on the world today. Lastly, a language can be in more than one category. Thai is the official, national, and standard language of Thailand. Conclusion Language is a term that is used that can also have many meanings. In this post, we looked at how there are different ways to see this word. # Code -Switching & Lexical Borrowing Code-switching involves a speaker changing languages as they talk. This post will explore some of the reasons behind why people code-switch. In addition, we will look at lexical borrowing and its use in communication Code-Switching Code-switching is most commonly caused by social factors and social dimensions of pragmatics. By social factors, it is meant the who, what, where, when and why of communication. Social dimensions involve distance, status, formality, emotions, referential traits. For example, two people from the same ethnicity may briefly switch to their language to say hello to each other before returning to English. The “what” is two people meeting each other and the use of the mother-tongue indicates high intimacy with each other. The topic of discussion can also lead to code-switching. For example, I have commonly seen students with the same mother-tongue switch to using English when discussion academic subjects. This may be because their academic studies use the English language as a medium of instruction. Switching can also take place for emotional reasons. For example, a person may switch languages to communicate anger such as a mother switching to the mother-tongue to scold their child. There is a special type of code-switching called metaphorical switching. This type of switching happens when the speaker switches languages for symbolic reasons. For example, when I person agrees about something they use their mother tongue. However, when they disagree about something they may switch to English. This switching back and forth is to indicate their opinion on a matter without having to express it too directly. Lexical Borrowing Lexical borrowing is used when a person takes a word from one language to replace an unknown word in a different language. Code-switching happens at the sentence level whereas lexical borrowing happens at the individual word level. Borrowing does not always happen because of a poor memory. Another reason for lexical borrowing is that some words do not translate into another language. This forces the speaker to borrow. For example, many langauges do not have a word for computer or internet. Therefore, these words are borrowed when speaking. Perceptions Often, people have no idea that the are code-switching or even borrowing. However, those who are conscious of it usually have a negative attitude towards it. The criticism of code-switching often involves complaints of how it destroys both languages. However, it takes a unique mastery of both languages to effectively code-switch or borrowing lexically. Conclusion Code-switching and lexical borrowing are characteristics of communication. For those who want to prescribe language, it may be frustrating to watch two languages being mixed together. However, from a descriptive perspective, this is a natural result of language interaction. # Social Dimensions of Language In sociolinguistics, social dimensions are the characteristics of the context that affect how language is used. Generally, there are four dimensions to the social context that are measured are analyzed through the use of five scales. The four dimension and five scales are as follows. • Social distance • Status • Formality • Functional (which includes a referential and affective function) This post will explore each of these four social dimensions of language. Social Distance Social distance is an indicator of how well we know someone that we are talking to. Many languages have different pronouns and even declensions in their verbs based on how well they know someone. For example, in English, a person might say “what’s up?” to a friend. However, when speaking to a stranger, regardless of the strangers status, a person may say something such as “How are you?”. The only reason for the change in language use is the lack of intimacy with the stranger as compared to the friend. Status Status is related to social ranking. The way we speak to peers is different than how we speak to superiors. Friends are called by their first name while a boss, in some cultures, is always referred to by Mr/Mrs or sir/madam. The rules for status can be confusing. Frequently we will refer to our parents as mom or dad but never Mr/Mrs. Even though Mr/Mrs is a sign of respect it violates the intimacy of the relationship between a parent and child. As such, often parents would be upset if their children called them Mr/Mrs. Formality Formality can be seen as the presence or absences of colloquial/slang in a person’s communication. In a highly formal setting, such as a speech, the language will often lack the more earthy style of speaking. Contractions may disappear, idioms may be reduced, etc. However, when spending time with friends at home a more laid-back manner of speaking will emerge However, when spending time with friends at home a more laid-back manner of speaking will emerge. One’s accent becomes more promeneint, slang terms are permissiable, etc. Function (Referential & Affective) Referential is a measure of the amount of information being shared in a discourse. The use of facts, statistics, directions, etc. Affective relates to the emotional content of communication and indicates how someone feels about the topic. Often referential and affective functions interrelated such as in the following example. James is a 45 year-old professor of research who has written several books but is still a complete idiot! This example above shares a lot of information as it shares the person’s name, job, and accomplishments. However, the emotions of the speaker are highly negative towards James as they call James a “complete idiot.” Conclusion The social dimensions of language are useful to know in order to understand what is affecting how people communicate. The concepts behind the four dimensions impact how we talk without most us knowing why or how. This can be frustrating but also empowering as people will understand why they adjust to various contexts of language use. # Journal Writing A journal is a log that a student uses to record their thoughts about something. This post will provide examples of journals as well as guidelines for using journals in the classroom. Types of Journals There are many different types of journals. Normally, all journals have some sort of dialog happening between the student and the teacher. This allows both parties to get to know each other better. Normally, journals will have a theme or focus. Examples in TESOL would include journals that focus on grammar, learning strategies, language-learning, or recording feelings. Most journals will focus on one of these to the exclusion of the others. Guidelines for Using Journals Journals can be useful if they are properly planned. As such, a teacher should consider the following when using journals. 1. Provide purpose-Students need to know why they are writing journals. Most students seem to despise reflection and will initially reject this learning experience 2. Forget grammar-Journals are for writing. Students need to set aside the obsession they have acquired for perfect grammar and focus on developing their thoughts about something. There is a time and place for grammar and that is for summative assessments such as final drafts of research papers. 3. Explain the grading process-Students need to know what they must demonstrate in order to receive adequate credit. 4. Provide feedback-Journals are a dialog. As such, the feedback should encourage and or instruct the students. The feedback should also be provided consistently at scheduled intervals. Journals take a lot of time to read and provide feedback too. In addition, the handwriting quality of students can vary radically which means that some students journals are unreadable. Conclusion Journaling is an experience that allows students to focus on the process of learning rather than the product. This is often neglected in the school experience. Through journals, students are able to focus on the development of ideas without wasting working memory capacity on grammar and syntax. As such, journals can be a powerful in developing critical thinking skills. # Cradle Approach to Portfolio Development Portfolio development is one of many forms of alternative assessment available to teachers. When this approach is used, generally the students collected their work and try to make sense of it through reflection. It is surprisingly easy for portfolio development to amount to nothing more than archiving work. However, the CRADLE approach was developed by Gottlieb to alleviate potential confusion over this process. CRADLE stands for the following C ollecting R eflecting A ssessing D ocumenting L inking E valuating Collecting Collecting is the process in which the students gather materials to include in their portfolio. It is left to the students to decide what to include. However, it is still necessary for the teacher to provide clear guidelines in terms of what can be potentially selected. Clear guidelines include stating the objectives as well as explaining how the portfolio will be assessed. It is also important to set aside class time for portfolio development. Some examples of work that can be included in a portfolio include the following. • tests, quizzes • compositions • electronic documents (powerpoints, pdfs, etc) Reflecting Reflecting happens through the student thinking about the work they have placed in the portfolio. This can be demonstrated many different ways. Common ways to reflect include the use of journals in which students comment on their work. Another way for young students is the use of checklist. Another way for young students is the use of a checklist. Students simply check the characteristics that are present in their work. As such, the teacher’s role is to provide class time so that students are able to reflect on their work. Assessing Assessing involves checking and maintaining the quality of the portfolio over time. Normally, there should a gradual improvement in work quality in a portfolio. This is a subjective matter that is negotiated by the student and teacher often in the form of conferences. Documenting Documenting serves more as a reminder than an action. Simply, documenting means that the teacher and student maintain the importance of the portfolio over the course of its usefulness. This is critical as it is easy to forget about portfolios through the pressure of the daily teaching experience. Linking Linking is the use of a portfolio to serve as a mode of communication between students, peers, teachers, and even parents. Students can look at each other portfolios and provide feedback. Parents can also examine the work of their child through the use of portfolios. Evaluating Evaluating is the process of receiving a grade for this experience. For the teacher, the goal is to provide positive washback when assessing the portfolios. The focus is normally less on grades and more qualitative in nature. Conclusions Portfolios provide rich opportunities for developing intrinsic motivation, individualize learning, and critical thinking. However, the trying to affix a grade to such a learning experience is often impractical. As such, portfolios are useful but it can be hard to prove that any learning took place. # Data Munging with Dplyr Data preparation aka data munging is what most data scientist spend the majority of their time doing. Extracting and transforming data is difficult, to say the least. Every dataset is different with unique problems. This makes it hard to generalize best practices for transforming data so that it is suitable for analysis. In this post, we will look at how to use the various functions in the “dplyr”” package. This package provides numerous ways to develop features as well as explore the data. We will use the “attitude” dataset from base r for our analysis. Below is some initial code. library(dplyr) data("attitude") str(attitude) ## 'data.frame': 30 obs. of 7 variables: ##$ rating    : num  43 63 71 61 81 43 58 71 72 67 ...
##  $complaints: num 51 64 70 63 78 55 67 75 82 61 ... ##$ privileges: num  30 51 68 45 56 49 42 50 72 45 ...
##  $learning : num 39 54 69 47 66 44 56 55 67 47 ... ##$ raises    : num  61 63 76 54 71 54 66 70 71 62 ...
##  $critical : num 92 73 86 84 83 49 68 66 83 80 ... ##$ advance   : num  45 47 48 35 47 34 35 41 31 41 ...

You can see we have seven variables and only 30 observations. Our first function that we will learn to use is the “select” function. This function allows you to select columns of data you want to use. In order to use this feature, you need to know the names of the columns you want. Therefore, we will first use the “names” function to determine the names of the columns and then use the “select”” function.

names(attitude)[1:3]
## [1] "rating"     "complaints" "privileges"
smallset<-select(attitude,rating:privileges)
head(smallset)
##   rating complaints privileges
## 1     43         51         30
## 2     63         64         51
## 3     71         70         68
## 4     61         63         45
## 5     81         78         56
## 6     43         55         49

The difference is probably obvious. Using the “select” function we have 3 instead of 7 variables. We can also exclude columns we do not want by placing a negative in front of the names of the columns. Below is the code

head(select(attitude,-(rating:privileges)))
##   learning raises critical advance
## 1       39     61       92      45
## 2       54     63       73      47
## 3       69     76       86      48
## 4       47     54       84      35
## 5       66     71       83      47
## 6       44     54       49      34

We can also use the “rename” function to change the names of columns. In our example below, we will change the name of the “rating” to “rates.” The code is below. Keep in mind that the new name for the column is to the left of the equal sign and the old name is to the right

attitude<-rename(attitude,rates=rating)
head(attitude)
##   rates complaints privileges learning raises critical advance
## 1    43         51         30       39     61       92      45
## 2    63         64         51       54     63       73      47
## 3    71         70         68       69     76       86      48
## 4    61         63         45       47     54       84      35
## 5    81         78         56       66     71       83      47
## 6    43         55         49       44     54       49      34

The “select”” function can be used in combination with other functions to find specific columns in the dataset. For example, we will use the “ends_with” function inside the “select” function to find all columns that end with the letter s.

s_set<-head(select(attitude,ends_with("s")))
s_set
##   rates complaints privileges raises
## 1    43         51         30     61
## 2    63         64         51     63
## 3    71         70         68     76
## 4    61         63         45     54
## 5    81         78         56     71
## 6    43         55         49     54

The “filter” function allows you to select rows from a dataset based on criteria. In the code below we will select only rows that have a 75 or higher in the “raises” variable.

bigraise<-filter(attitude,raises>75)
bigraise
##   rates complaints privileges learning raises critical advance
## 1    71         70         68       69     76       86      48
## 2    77         77         54       72     79       77      46
## 3    74         85         64       69     79       79      63
## 4    66         77         66       63     88       76      72
## 5    78         75         58       74     80       78      49
## 6    85         85         71       71     77       74      55

If you look closely all values in the “raise” column are greater than 75. Of course, you can have more than one criteria. IN the code below there are two.

filter(attitude, raises>70 & learning<67)
##   rates complaints privileges learning raises critical advance
## 1    81         78         56       66     71       83      47
## 2    65         70         46       57     75       85      46
## 3    66         77         66       63     88       76      72

The “arrange” function allows you to sort the order of the rows. In the code below we first sort the data ascending by the “critical” variable. Then we sort it descendingly by adding the “desc” function.

ascCritical<-arrange(attitude, critical)
head(ascCritical)
##   rates complaints privileges learning raises critical advance
## 1    43         55         49       44     54       49      34
## 2    81         90         50       72     60       54      36
## 3    40         37         42       58     50       57      49
## 4    69         62         57       42     55       63      25
## 5    50         40         33       34     43       64      33
## 6    71         75         50       55     70       66      41
descCritical<-arrange(attitude, desc(critical))
head(descCritical)
##   rates complaints privileges learning raises critical advance
## 1    43         51         30       39     61       92      45
## 2    71         70         68       69     76       86      48
## 3    65         70         46       57     75       85      46
## 4    61         63         45       47     54       84      35
## 5    81         78         56       66     71       83      47
## 6    72         82         72       67     71       83      31

The “mutate” function is useful for engineering features. In the code below we will transform the “learning” variable by subtracting its mean from its self

attitude<-mutate(attitude,learningtrend=learning-mean(learning))
head(attitude)
##   rates complaints privileges learning raises critical advance
## 1    43         51         30       39     61       92      45
## 2    63         64         51       54     63       73      47
## 3    71         70         68       69     76       86      48
## 4    61         63         45       47     54       84      35
## 5    81         78         56       66     71       83      47
## 6    43         55         49       44     54       49      34
##   learningtrend
## 1    -17.366667
## 2     -2.366667
## 3     12.633333
## 4     -9.366667
## 5      9.633333
## 6    -12.366667

You can also create logical variables with the “mutate” function.In the code below, we create a logical variable that is true when the “critical” variable” is higher than 80 and false when “critical”” is less than 80. The new variable is called “highCritical”

attitude<-mutate(attitude,highCritical=critical>=80)
head(attitude)
##   rates complaints privileges learning raises critical advance
## 1    43         51         30       39     61       92      45
## 2    63         64         51       54     63       73      47
## 3    71         70         68       69     76       86      48
## 4    61         63         45       47     54       84      35
## 5    81         78         56       66     71       83      47
## 6    43         55         49       44     54       49      34
##   learningtrend highCritical
## 1    -17.366667         TRUE
## 2     -2.366667        FALSE
## 3     12.633333         TRUE
## 4     -9.366667         TRUE
## 5      9.633333         TRUE
## 6    -12.366667        FALSE

The “group_by” function is used for creating summary statistics based on a specific variable. It is similar to the “aggregate” function in R. This function works in combination with the “summarize” function for our purposes here. We will group our data by the “highCritical” variable. This means our data will be viewed as either TRUE for “highCritical” or FALSE. The results of this function will be saved in an object called “hcgroups”

hcgroups<-group_by(attitude,highCritical)
head(hcgroups)
## # A tibble: 6 x 9
## # Groups:   highCritical [2]
##   rates complaints privileges learning raises critical advance
##
## 1    43         51         30       39     61       92      45
## 2    63         64         51       54     63       73      47
## 3    71         70         68       69     76       86      48
## 4    61         63         45       47     54       84      35
## 5    81         78         56       66     71       83      47
## 6    43         55         49       44     54       49      34
## # ... with 2 more variables: learningtrend , highCritical 

Looking at the data you probably saw no difference. This is because we are not done yet. We need to summarize the data in order to see the results for our two groups in the “highCritical” variable.

We will now generate the summary statistics by using the “summarize” function. We specifically want to know the mean of the “complaint” variable based on the variable “highCritical.” Below is the code

summarize(hcgroups,complaintsAve=mean(complaints))
## # A tibble: 2 x 2
##   highCritical complaintsAve
##
## 1        FALSE      67.31579
## 2         TRUE      65.36364

Of course, you could have learned this through doing a t.test but this is another approach.

Conclusion

The “dplyr” package is one powerful tool for wrestling with data. There is nothing new in this package. Instead, the coding is simpler than what you can excute using base r.

# Select the Word Questions in Moodle VIDEO

Select the missing word questions in moodle

# Guiding the Writing Process

How a teacher guides the writing process can depend on a host of factors. Generally, how you support a student at the beginning of the writing process is different from how you support them at the end. In this post, we will look at the differences between these two stages of writing.

The Beginning

At the beginning of writing, there are a lot of decisions that need to be made as well as extensive planning. Generally, at this point, grammar is not the deciding factor in terms of the quality of the writing. Rather, the teacher is trying to help the students to determine the focus of the paper as well as the main ideas.

The teacher needs to help the student to focus on the big picture of the purpose of their writing. This means that only major issues are addressed at least initially. You only want to point at potential disaster decisions rather than mundane details.

It is tempting to try and fix everything when looking at rough drafts. This not only takes up a great deal of your time but it is also discouraging to students as they deal with intense criticism while still trying to determine what they truly want to do. As such, it is better to view your role at this point as a counselor or guide and not as detail oriented control freak.

At this stage, the focus is on the discourse and not so much on the grammar.

The End

At the end of the writing process, there is a move from general comments to specific concerns. As the student gets closer and closer to the final draft the “little things” become more and more important. Grammar comes to the forefront. In addition, referencing and the strength of the supporting details become more important.

Now is the time to get “picky” this is because major decisions have been made and the cognitive load of fixing small stuff is less stressful once the core of the paper is in place. The analogy I like to give is that first, you build the house. Which involves lots of big movements such as pouring a foundation, adding walls, and including a roof. This is the beginning of writing. The end of building a house includes more refined aspects such as painting the walls, adding the furniture, etc. This is the end of the writing process.

Conclusion

For writers and teachers, it is important to know where they are in the writing process. In my experience, it seems as if it is all about grammar from the beginning when this is not necessarily the case. At the beginning of a writing experience, the focus is on ideas. At the end of a writing experience, the focus is on grammar. The danger is always in trying to do too much at the same time.

# Review of “First Encyclopedia of the Human Body”

The First Encyclopedia of the Human Body (First Encyclopedias)by Fiona Chandler (pp. 64) provides insights into science for young children.

The Summary
This book explains all of the major functions of the human body as well as some aspects of health and hygiene. Students will learn about the brain, heart, hormones, where babies come from, as well as healthy eating and visiting the doctor.

The Good
This book is surprisingly well-written. The author was able to take the complexities of
the human body and word them in a way that a child can
understand. In addition, the illustrations are rich and interesting. For example, there are pictures of an infare-red scan of a child’s hands, x-rays of broken bones, as well as
pictures of people doing things with their bodies such as running or jumping.

There is also a good mix of small and large photos which allows this book to be used individually or for whole class reading. The large size of the text also allows for younger readers to appreciate not only the pictures but also the reading.

There are also several activities in the book at different places. For example, students are invited to take their pulse, determine how much air is in their lungs, as well as an activity for testing your sense of touch.

In every section of the book, there are links to online activities as well. It seems as though this book has every angle covered in terms of learning.

There is little to criticize in this book. It’s a really fun text. Perhaps if you are an expert in the human body you may find things that are disappointing. However, for a layman called to teach young people science, this text is more than adequate.

The Recommendation
I would give this book 5/5 stars. My students loved it and I was able to use it in so many different ways to build activities and discussions. I am sure that the use of this book would be beneficial to almost any teacher in any classroom

# Reading Assessment at the Perceptual and Selective Level

This post will provide examples of assessments that can be used for reading at the perceptual and selective level.

# Perceptual Level

The perceptual level is focused on bottom-up processing of text. Comprehension ability is not critical at this point. Rather, you are just determining if the student can accomplish the mechanical process of reading.

Examples

Reading Aloud-How this works is probably obvious to most teachers. The students read a text out loud in the presence of an assessor.

Picture-Cued-Students are shown a picture. At the bottom of the picture are words. The students read the word and point to a visual example of it in the picture. For example, if the picture has a cat in it. At the bottom of the picture would be the word cat. The student would read the word cat and point to the actual cat in the picture.

This can be extended by using sentences instead of words. For example, if the actual picture shows a man driving a car. There may be a sentence at the bottom of the picture that says “a man is driving a car”. The student would then point to the man in the actual picture who is driving.

Another option is T/F statements. Using our cat example from above. We might write that “There is one cat in the picture” the student would then select T/F.

Other Examples-These includes multiple-choice and written short answer.

# Selective Level

The selective level is the next above perceptual. At this level, the student should be able to recognize various aspects of grammar.

Examples

Editing Task-Students are given a reading passage and are asked to fix the grammar. This can happen many different ways. They could be asked to pick the incorrect word in a sentence or to add or remove punctuation.

Pictured-Cued Task-This task appeared at the perceptual level. Now it is more complicated. For example, the students might be required to read statements and label a diagram appropriately, such as the human body or aspects of geography.

Other Examples-Includes multiple-choice and matching. The multiple-choice may focus on grammar, vocabulary, etc. Matching attempts to assess a students ability to pair similar items.

Conclusion

Reading assessment can take many forms. The examples here provide ways to deal with this for students who are still highly immature in their reading abilities. As fluency develops more complex measures can be used to determine a students reading capability.

# Types of Speaking in ESL

In the context of ESL teaching, ~there are at least five types of speaking that take place in the classroom. This post will define and provide examples of each. The five types are as follows…

• Imitative
• Intensive
• Responsive
• Interactive
• Extensive

The list above is ordered from simplest to most complex in terms of the requirements of oral production for the student.

Imitative

At the imitative level, it is probably already clear what the student is trying to do. At this level, the student is simply trying to repeat what was said to them in a way that is understandable and with some adherence to pronunciation as defined by the teacher.

It doesn’t matter if the student comprehends what they are saying or carrying on a conversation. The goal is only to reproduce what was said to them. One common example of this is a “repeat after me” experience in the classroom.

Intensive

Intensive speaking involves producing a limit amount of language in a highly control context. An example of this would be to read aloud a passage or give a direct response to a simple question.

Competency at this level is shown through achieving certain grammatical or lexical mastery. This depends on the teacher’s expectations.

Responsive

Responsive is slightly more complex than intensive but the difference is blurry, to say the least. At this level, the dialog includes a simple question with a follow-up question or two. Conversations take place by this point but are simple in content.

Interactive

The unique feature of intensive speaking is that it is usually more interpersonal than transactional. By interpersonal it is meant speaking for maintaining relationships. Transactional speaking is for sharing information as is common at the responsive level.

The challenge of interpersonal speaking is the context or pragmatics The speaker has to keep in mind the use of slang, humor, ellipsis, etc. when attempting to communicate. This is much more complex than saying yes or no or giving directions to the bathroom in a second language.

Extensive

Extensive communication is normal some sort of monolog. Examples include speech, story-telling, etc. This involves a great deal of preparation and is not typically improvisational communication.

It is one thing to survive having a conversation with someone in a second language. You can rely on each other’s body language to make up for communication challenges. However, with extensive communication either the student can speak in a comprehensible way without relying on feedback or they cannot. In my personal experience, the typical ESL student cannot do this in a convincing manner.

# Intensive Listening and ESL

Intensive listening is listening for the elements (phonemes, intonation, etc.) in words and sentences. This form of listening is often assessed in an ESL setting as a way to measure an individual’s phonological,  morphological, and ability to paraphrase. In this post, we will look at these three forms of assessment with examples.

Phonological Elements

Phonological elements include phonemic consonant and phonemic vowel pairs. Phonemic consonant pair has to do with identifying consonants. Below is an example of what an ESL student would hear followed by potential choices they may have on a multiple-choice test.

Recording: He’s from Thailand

Choices:
(a) He’s from Thailand
(b) She’s from Thailand

The answer is clearly (a). The confusion is with the adding of ‘s’ for choice (b). If someone is not listening carefully they could make a mistake. Below is an example of phonemic pairs involving vowels

Recording: The girl is leaving?

Choices:
(a)The girl is leaving?
(b)The girl is living?

Again, if someone is not listening carefully they will miss the small change in the vowel.

Morphological Elements

Morphological elements follow the same approach as phonological elements. You can manipulate endings, stress patterns, or play with words.  Below is an example of ending manipulation.

Recording: I smiled a lot.

Choices:
(a) I smiled a lot.
(b) I smile a lot.

I sharp listener needs to hear the ‘d’ sound at the end of the word ‘smile’ which can be challenging for ESL student. Below is an example of stress pattern

Recording: My friend doesn’t smoke.

Choices:
(a) My friend doesn’t smoke.
(b) My friend does smoke.

The contraction in the example is the stress pattern the listener needs to hear. Below is an example of a play with words.

Recording: wine

Choices:
(a) wine
(b) vine

This is especially tricky for languages that do not have both a ‘v’ and ‘w’ sound, such as the Thai language.

Paraphrase recognition

Paraphrase recognition involves listening to an example of being able to reword it in an appropriate manner. This involves not only listening but also vocabulary selection and summarizing skills. Below is one example of sentence paraphrasing

Recording: My name is James. I come from California

Choices:
(a) James is Californian
(b) James loves Calfornia

This is trickier because both can be true. However, the goal is to try and rephrase what was heard.  Another form of paraphrasing is dialogue paraphrasing as shown below

Recording:

Man: My name is Thomas. What is your name?
Woman: My name is Janet. Nice to meet you. Are you from Africa
Man: No, I am an American

Choices:
(a) Thomas is from America
(b)Thomas is African

You can see the slight rephrase that is wrong with choice (b). This requires the student to listen to slightly longer audio while still have to rephrase it appropriately.

Conclusion

Intensive listening involves the use of listening for the little details of an audio. This is a skill that provides a foundation for much more complex levels of listening.

# Recommendation Engines in R

In this post, we will look at how to make a recommendation engine. We will use data that makes recommendations about movies. We will use the “recommenderlab” package to build several different engines. The data comes from

At this link, you need to download the “ml-latest.zip”. From there, we will use the “ratings” and “movies” files in this post. Ratings provide the ratings of the movies while movies provide the names of the movies. Before going further it is important to know that the “recommenderlab” has five different techniques for developing recommendation engines (IBCF, UBCF, POPULAR, RANDOM, & SVD). We will use all of them for comparative purposes Below is the code for getting started.

library(recommenderlab)
ratings <- read.csv("~/Downloads/ml-latest-small/ratings.csv")
movies <- read.csv("~/Downloads/ml-latest-small/movies.csv")

We now need to merge the two datasets so that they become one. This way the titles and ratings are in one place. We will then coerce our “movieRatings” dataframe into a “realRatingMatrix” in order to continue our analysis. Below is the code

movieRatings<-merge(ratings, movies, by='movieId') #merge two files
movieRatings<-as(movieRatings,"realRatingMatrix") #coerce to realRatingMatrix

We will now create two histograms of the ratings. The first is raw data and the second will be normalized data. The function “getRatings” is used in combination with the “hist” function to make the histogram. The normalized data includes the “normalize” function. Below is the code.

hist(getRatings(movieRatings),breaks =10)

hist(getRatings(normalize(movieRatings)),breaks =10)

We are now ready to create the evaluation scheme for our analysis. In this object we need to set the data name (movieRatings), the method we want to use (cross-validation), the amount of data we want to use for the training set (80%), how many ratings the algorithm is given during the test set (1) with the rest being used to compute the error. We also need to tell R what a good rating is (4 or higher) and the number of folds for the cross-validation (10). Below is the code for all of this.

set.seed(123)
eSetup<-evaluationScheme(movieRatings,method='cross-validation',train=.8,given=1,goodRating=4,k=10)

Below is the code for developing our models. To do this we need to use the “Recommender” function and the “getData” function to get the dataset. Remember we are using all six modeling techniques

ubcf<-Recommender(getData(eSetup,"train"),"UBCF")
ibcf<-Recommender(getData(eSetup,"train"),"IBCF")
svd<-Recommender(getData(eSetup,"train"),"svd")
popular<-Recommender(getData(eSetup,"train"),"POPULAR")
random<-Recommender(getData(eSetup,"train"),"RANDOM")

The models have been created. We can now make our predictions using the “predict” function in addition to the “getData” function. We also need to set the argument “type” to “ratings”. Below is the code.

ubcf_pred<-predict(ubcf,getData(eSetup,"known"),type="ratings")
ibcf_pred<-predict(ibcf,getData(eSetup,"known"),type="ratings")
svd_pred<-predict(svd,getData(eSetup,"known"),type="ratings")
pop_pred<-predict(popular,getData(eSetup,"known"),type="ratings")
rand_pred<-predict(random,getData(eSetup,"known"),type="ratings")

We can now look at the accuracy of the models. We will do this in two steps. First, we will look at the error rates. After completing this, we will do a more detailed analysis of the stronger models. Below is the code for the first step

ubcf_error<-calcPredictionAccuracy(ubcf_pred,getData(eSetup,"unknown")) #calculate error
ibcf_error<-calcPredictionAccuracy(ibcf_pred,getData(eSetup,"unknown"))
svd_error<-calcPredictionAccuracy(svd_pred,getData(eSetup,"unknown"))
pop_error<-calcPredictionAccuracy(pop_pred,getData(eSetup,"unknown"))
rand_error<-calcPredictionAccuracy(rand_pred,getData(eSetup,"unknown"))
error<-rbind(ubcf_error,ibcf_error,svd_error,pop_error,rand_error) #combine objects into one data frame
rownames(error)<-c("UBCF","IBCF","SVD","POP","RAND") #give names to rows
error
##          RMSE      MSE       MAE
## UBCF 1.278074 1.633473 0.9680428
## IBCF 1.484129 2.202640 1.1049733
## SVD  1.277550 1.632135 0.9679505
## POP  1.224838 1.500228 0.9255929
## RAND 1.455207 2.117628 1.1354987

The results indicate that the “RAND” and “IBCF” models are clearly worse than the remaining three. We will now move to the second step and take a closer look at the “UBCF”, “SVD”, and “POP” models. We will do this by making a list and using the “evaluate” function to get other model evaluation metrics. We will make a list called “algorithms” and store the three strongest models. Then we will make an objectcalled “evlist” in this object we will use the “evaluate” function as well as called the evaluation scheme “esetup”, the list (“algorithms”) as well as the number of movies to assess (5,10,15,20)

algorithms<-list(POPULAR=list(name="POPULAR"),SVD=list(name="SVD"),UBCF=list(name="UBCF"))
evlist<-evaluate(eSetup,algorithms,n=c(5,10,15,20))
avg(evlist)
## $POPULAR ## TP FP FN TN precision recall TPR ## 5 0.3010965 3.033333 4.917105 661.7485 0.09028443 0.07670381 0.07670381 ## 10 0.4539474 6.214912 4.764254 658.5669 0.06806016 0.11289681 0.11289681 ## 15 0.5953947 9.407895 4.622807 655.3739 0.05950450 0.14080354 0.14080354 ## 20 0.6839912 12.653728 4.534211 652.1281 0.05127635 0.16024740 0.16024740 ## FPR ## 5 0.004566269 ## 10 0.009363021 ## 15 0.014177091 ## 20 0.019075070 ## ##$SVD
##           TP        FP       FN       TN  precision     recall        TPR
## 5  0.1025219  3.231908 5.115680 661.5499 0.03077788 0.00968336 0.00968336
## 10 0.1808114  6.488048 5.037390 658.2938 0.02713505 0.01625454 0.01625454
## 15 0.2619518  9.741338 4.956250 655.0405 0.02620515 0.02716656 0.02716656
## 20 0.3313596 13.006360 4.886842 651.7754 0.02486232 0.03698768 0.03698768
##            FPR
## 5  0.004871678
## 10 0.009782266
## 15 0.014689510
## 20 0.019615377
##
## $UBCF ## TP FP FN TN precision recall TPR ## 5 0.1210526 2.968860 5.097149 661.8129 0.03916652 0.01481106 0.01481106 ## 10 0.2075658 5.972259 5.010636 658.8095 0.03357173 0.02352752 0.02352752 ## 15 0.3028509 8.966886 4.915351 655.8149 0.03266321 0.03720717 0.03720717 ## 20 0.3813596 11.978289 4.836842 652.8035 0.03085246 0.04784538 0.04784538 ## FPR ## 5 0.004475151 ## 10 0.009004466 ## 15 0.013520481 ## 20 0.018063361 Well, the numbers indicate that all the models are terrible. All metrics are scored rather poorly. True positives, false positives, false negatives, true negatives, precision, recall, true positive rate, and false positive rate are low for all models. Remember that these values are averages of the cross-validation. As such, for the “POPULAR” model when looking at the top five movies on average, the number of true positives was .3. Even though the numbers are terrible the “POPULAR” model always performed the best. We can even view the ROC curve with the code below plot(evlist,legend="topleft",annotate=T) We can now determine individual recommendations. We first need to build a model using the POPULAR algorithm. Below is the code. Rec1<-Recommender(movieRatings,method="POPULAR") Rec1 ## Recommender of type 'POPULAR' for 'realRatingMatrix' ## learned using 9066 users. We will now pull the top five recommendations for the first two raters and make a list. The numbers are the movie ids and not the actual titles recommend<-predict(Rec1,movieRatings[1:5],n=5) as(recommend,"list") ##$1
## [1] "78"  "95"  "544" "102" "4"
##
## $2 ## [1] "242" "232" "294" "577" "95" ## ##$3
## [1] "654" "242" "30"  "232" "287"
##
## $4 ## [1] "564" "654" "242" "30" "232" ## ##$5
## [1] "242" "30"  "232" "287" "577"

Below we can see the specific score for a specific movie. The names of the movies come from the original “ratings” dataset.

rating<-predict(Rec1,movieRatings[1:5],type='ratings')
rating
## 5 x 671 rating matrix of class 'realRatingMatrix' with 2873 ratings.
movieresult<-as(rating,'matrix')[1:5,1:3]
colnames(movieresult)<-c("Toy Story","Jumanji","Grumpier Old Men")
movieresult
##   Toy Story  Jumanji Grumpier Old Men
## 1  2.859941 3.822666         3.724566
## 2  2.389340 3.352066         3.253965
## 3  2.148488 3.111213         3.013113
## 4  1.372087 2.334812         2.236711
## 5  2.255328 3.218054         3.119953

This is what the model thinks the person would rate the movie. It is the difference between this number and the actual one that the error is calculated. In addition, if someone did not rate a movie you would see an NA in that spot

Conclusion

This was a lot of work. However, with additional work, you can have your own recommendation system based on data that was collected.

# Clustering Mixed Data in R

One of the major problems with hierarchical and k-means clustering is that they cannot handle nominal data. The reality is that most data is mixed or a combination of both interval/ratio data and nominal/ordinal data.

One of many ways to deal with this problem is by using the Gower coefficient. This coefficient compares the pairwise cases in the data set and calculates a dissimilarity between. By dissimilar we mean the weighted mean of the variables in that row.

Once the dissimilarity calculations are completed using the gower coefficient (there are naturally other choices), you can then use regular kmeans clustering (there are also other choices) to find the traits of the various clusters. In this post, we will use the “MedExp” dataset from the “Ecdat” package. Our goal will be to cluster the mixed data into four clusters. Below is some initial code.

library(cluster);library(Ecdat);library(compareGroups)
data("MedExp")
str(MedExp)
## 'data.frame':    5574 obs. of  15 variables:
##  $med : num 62.1 0 27.8 290.6 0 ... ##$ lc      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $idp : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 1 1 ... ##$ lpi     : num  6.91 6.91 6.91 6.91 6.11 ...
##  $fmde : num 0 0 0 0 0 0 0 0 0 0 ... ##$ physlim : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 1 1 1 ...
##  $ndisease: num 13.7 13.7 13.7 13.7 13.7 ... ##$ health  : Factor w/ 4 levels "excellent","good",..: 2 1 1 2 2 2 2 1 2 2 ...
##  $linc : num 9.53 9.53 9.53 9.53 8.54 ... ##$ lfam    : num  1.39 1.39 1.39 1.39 1.1 ...
##  $educdec : num 12 12 12 12 12 12 12 12 9 9 ... ##$ age     : num  43.9 17.6 15.5 44.1 14.5 ...
##  $sex : Factor w/ 2 levels "male","female": 1 1 2 2 2 2 2 1 2 2 ... ##$ child   : Factor w/ 2 levels "no","yes": 1 2 2 1 2 2 1 1 2 1 ...
##  $black : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ... You can clearly see that our data is mixed with both numerical and factor variables. Therefore, the first thing we must do is calculate the gower coefficient for the dataset. This is done with the “daisy” function from the “cluster” package. disMat<-daisy(MedExp,metric = "gower") Now we can use the “kmeans” to make are clusters. This is possible because all the factor variables have been converted to a numerical value. We will set the number of clusters to 4. Below is the code. set.seed(123) mixedClusters<-kmeans(disMat, centers=4) We can now look at a table of the clusters table(mixedClusters$cluster)
##
##    1    2    3    4
## 1960 1342 1356  916

The groups seem reasonably balanced. We now need to add the results of the kmeans to the original dataset. Below is the code

MedExp$cluster<-mixedClusters$cluster

We now can built a descriptive table that will give us the proportions of each variable in each cluster. To do this we need to use the “compareGroups” function. We will then take the output of the “compareGroups” function and use it in the “createTable” function to get are actual descriptive stats.

group<-compareGroups(cluster~.,data=MedExp)
clustab<-createTable(group)
clustab
##
## --------Summary descriptives table by 'cluster'---------
##
## __________________________________________________________________________
##                    1            2            3            4      p.overall
##                  N=1960       N=1342       N=1356       N=916
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## med            211 (1119)   68.2 (333)   269 (820)   83.8 (210)   <0.001
## lc            4.07 (0.60)  4.05 (0.60)  0.04 (0.39)  0.03 (0.34)   0.000
## idp:                                                              <0.001
##     no        1289 (65.8%) 922 (68.7%)  1123 (82.8%) 781 (85.3%)
##     yes       671 (34.2%)  420 (31.3%)  233 (17.2%)  135 (14.7%)
## lpi           5.72 (1.94)  5.90 (1.73)  3.27 (2.91)  3.05 (2.96)  <0.001
## fmde          6.82 (0.99)  6.93 (0.90)  0.00 (0.12)  0.00 (0.00)   0.000
## physlim:                                                          <0.001
##     no        1609 (82.1%) 1163 (86.7%) 1096 (80.8%) 789 (86.1%)
##     yes       351 (17.9%)  179 (13.3%)  260 (19.2%)  127 (13.9%)
## ndisease      11.5 (8.26)  10.2 (2.97)  12.2 (8.50)  10.6 (3.35)  <0.001
## health:                                                           <0.001
##     excellent 910 (46.4%)  880 (65.6%)  615 (45.4%)  612 (66.8%)
##     good      828 (42.2%)  382 (28.5%)  563 (41.5%)  261 (28.5%)
##     fair      183 (9.34%)   74 (5.51%)  137 (10.1%)  42 (4.59%)
##     poor       39 (1.99%)   6 (0.45%)    41 (3.02%)   1 (0.11%)
## linc          8.68 (1.22)  8.61 (1.37)  8.75 (1.17)  8.78 (1.06)   0.005
## lfam          1.05 (0.57)  1.49 (0.34)  1.08 (0.58)  1.52 (0.35)  <0.001
## educdec       12.1 (2.87)  11.8 (2.58)  12.0 (3.08)  11.8 (2.73)   0.005
## age           36.5 (12.0)  9.26 (5.01)  37.0 (12.5)  9.29 (5.11)   0.000
## sex:                                                              <0.001
##     male      893 (45.6%)  686 (51.1%)  623 (45.9%)  482 (52.6%)
##     female    1067 (54.4%) 656 (48.9%)  733 (54.1%)  434 (47.4%)
## child:                                                             0.000
##     no        1960 (100%)   0 (0.00%)   1356 (100%)   0 (0.00%)
##     yes        0 (0.00%)   1342 (100%)   0 (0.00%)   916 (100%)
## black:                                                            <0.001
##     yes       1623 (82.8%) 986 (73.5%)  1148 (84.7%) 730 (79.7%)
##     no        337 (17.2%)  356 (26.5%)  208 (15.3%)  186 (20.3%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

The table speaks for itself. Results that utilize factor variables have proportions to them. For example, in cluster 1, 1289 people or 65.8% responded “no” that the have an individual deductible plan (idp). Numerical variables have the mean with the standard deviation in parentheses. For example, in cluster 1 the average family size was 1 with a standard deviation of 1.05 (lfam).

Conclusion

Mixed data can be partition into clusters with the help of the gower or another coefficient. In addition, kmeans is not the only way to cluster the data. There are other choices such as the partitioning around medoids. The example provided here simply serves as a basic introduction to this.

# Hierarchical Clustering in R

Hierarchical clustering is a form of unsupervised learning. What this means is that the data points lack any form of label and the purpose of the analysis is to generate labels for our data points. IN other words, we have no Y values in our data.

Hierarchical clustering is an agglomerative technique. This means that each data point starts as their own individual clusters and are merged over iterations. This is great for small datasets but is difficult to scale. In addition, you need to set the linkage which is used to place observations in different clusters. There are several choices (ward, complete, single, etc.) and the best choice depends on context.

In this post, we will make a hierarchical clustering analysis of the “MedExp” data from the “Ecdat” package. We are trying to identify distinct subgroups in the sample. The actual hierarchical cluster creates what is a called a dendrogram. Below is some initial code.

library(cluster);library(compareGroups);library(NbClust);library(HDclassif);library(sparcl);library(Ecdat)
data("MedExp")
str(MedExp)
## 'data.frame':    5574 obs. of  15 variables:
##  $med : num 62.1 0 27.8 290.6 0 ... ##$ lc      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $idp : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 1 1 ... ##$ lpi     : num  6.91 6.91 6.91 6.91 6.11 ...
##  $fmde : num 0 0 0 0 0 0 0 0 0 0 ... ##$ physlim : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 1 1 1 ...
##  $ndisease: num 13.7 13.7 13.7 13.7 13.7 ... ##$ health  : Factor w/ 4 levels "excellent","good",..: 2 1 1 2 2 2 2 1 2 2 ...
##  $linc : num 9.53 9.53 9.53 9.53 8.54 ... ##$ lfam    : num  1.39 1.39 1.39 1.39 1.1 ...
##  $educdec : num 12 12 12 12 12 12 12 12 9 9 ... ##$ age     : num  43.9 17.6 15.5 44.1 14.5 ...
##  $sex : Factor w/ 2 levels "male","female": 1 1 2 2 2 2 2 1 2 2 ... ##$ child   : Factor w/ 2 levels "no","yes": 1 2 2 1 2 2 1 1 2 1 ...
##  $black : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ... Currently, for the purposes of this post. The dataset is too big. IF we try to do the analysis with over 5500 observations it will take a long time. Therefore, we will only use the first 1000 observations. In addition, We need to remove factor variables as hierarchical clustering cannot analyze factor variables. Below is the code. MedExp_small<-MedExp[1:1000,] MedExp_small$sex<-NULL
MedExp_small$idp<-NULL MedExp_small$child<-NULL
MedExp_small$black<-NULL MedExp_small$physlim<-NULL
MedExp_small$health<-NULL We now need to scale are data. This is important because different scales will cause different variables to have more or less influence on the results. Below is the code MedExp_small_df<-as.data.frame(scale(MedExp_small)) We now need to determine how many clusters to create. There is no rule on this but we can use statistical analysis to help us. The “NbClust” package will conduct several different analysis to provide a suggested number of clusters to create. You have to set the distance, min/max number of clusters, the method, and the index. The graphs can be understood by looking for the bend or elbow in them. At this point is the best number of clusters. numComplete<-NbClust(MedExp_small_df,distance = 'euclidean',min.nc = 2,max.nc = 8,method = 'ward.D2',index = c('all')) ## *** : The Hubert index is a graphical method of determining the number of clusters. ## In the plot of Hubert index, we seek a significant knee that corresponds to a ## significant increase of the value of the measure i.e the significant peak in Hubert ## index second differences plot. ##  ## *** : The D index is a graphical method of determining the number of clusters. ## In the plot of D index, we seek a significant knee (the significant peak in Dindex ## second differences plot) that corresponds to a significant increase of the value of ## the measure. ## ## ******************************************************************* ## * Among all indices: ## * 7 proposed 2 as the best number of clusters ## * 9 proposed 3 as the best number of clusters ## * 6 proposed 6 as the best number of clusters ## * 1 proposed 8 as the best number of clusters ## ## ***** Conclusion ***** ## ## * According to the majority rule, the best number of clusters is 3 ## ## ## ******************************************************************* numComplete$Best.nc
##                     KL       CH Hartigan     CCC    Scott      Marriot
## Number_clusters 2.0000   2.0000   6.0000  8.0000    3.000 3.000000e+00
## Value_Index     2.9814 292.0974  56.9262 28.4817 1800.873 4.127267e+24
##                   TrCovW   TraceW Friedman   Rubin Cindex     DB
## Number_clusters      6.0   6.0000   3.0000  6.0000  2.000 3.0000
## Value_Index     166569.3 265.6967   5.3929 -0.0913  0.112 1.0987
##                 Silhouette   Duda PseudoT2  Beale Ratkowsky     Ball
## Number_clusters     2.0000 2.0000   2.0000 2.0000    6.0000    3.000
## Value_Index         0.2809 0.9567  16.1209 0.2712    0.2707 1435.833
##                 PtBiserial Frey McClain   Dunn Hubert SDindex Dindex
## Number_clusters     6.0000    1   3.000 3.0000      0  3.0000      0
## Value_Index         0.4102   NA   0.622 0.1779      0  1.9507      0
##                   SDbw
## Number_clusters 3.0000
## Value_Index     0.5195

Simple majority indicates that three clusters is most appropriate. However, four clusters are probably just as good. Every time you do the analysis you will get slightly different results unless you set the seed.

To make our actual clusters we need to calculate the distances between clusters using the “dist” function while also specifying the way to calculate it. We will calculate distance using the “Euclidean” method. Then we will take the distance’s information and make the actual clustering using the ‘hclust’ function. Below is the code.

distance<-dist(MedExp_small_df,method = 'euclidean')
hiclust<-hclust(distance,method = 'ward.D2')

We can now plot the results. We will plot “hiclust” and set hang to -1 so this will place the observations at the bottom of the plot. Next, we use the “cutree” function to identify 4 clusters and store this in the “comp” variable. Lastly, we use the “ColorDendrogram” function to highlight are actual clusters.

plot(hiclust,hang=-1, labels=F)
comp<-cutree(hiclust,4) ColorDendrogram(hiclust,y=comp,branchlength = 100)

We can also create some descriptive stats such as the number of observations per cluster.

table(comp)
## comp
##   1   2   3   4
## 439 203 357   1

We can also make a table that looks at the descriptive stats by cluster by using the “aggregate” function.

aggregate(MedExp_small_df,list(comp),mean)
##   Group.1         med         lc        lpi       fmde     ndisease
## 1       1  0.01355537 -0.7644175  0.2721403 -0.7498859  0.048977122
## 2       2 -0.06470294 -0.5358340 -1.7100649 -0.6703288 -0.105004408
## 3       3 -0.06018129  1.2405612  0.6362697  1.3001820 -0.002099968
## 4       4 28.66860936  1.4732183  0.5252898  1.1117244  0.564626907
##          linc        lfam    educdec         age
## 1  0.12531718 -0.08861109  0.1149516  0.12754008
## 2 -0.44435225  0.22404456 -0.3767211 -0.22681535
## 3  0.09804031 -0.01182114  0.0700381 -0.02765987
## 4  0.18887531 -2.36063161  1.0070155 -0.07200553

Cluster 1 is the most educated (‘educdec’). Cluster 2 stands out as having higher medical cost (‘med’), chronic disease (‘ndisease’) and age. Cluster 3 had the lowest annual incentive payment (‘lpi’). Cluster 4 had the highest coinsurance rate (‘lc’). You can make boxplots of each of the stats above. Below is just an example of age by cluster.

MedExp_small_df$cluster<-comp boxplot(age~cluster,MedExp_small_df) Conclusion Hierarchical clustering is one way in which to provide labels for data that does not have labels. The main challenge is determining how many clusters to create. However, this can be dealt with through using recommendations that come from various functions in R. # Attendance Module in Moodle VIDEO How to setup the attendance module in Moodle # Forum Options in Moodle VIDEO Options for forums in Moodle # Q&A Forums in Moodle VIDEO Creating Q&A forums in Moodle # Each Person Post One Discussion Forum in Moodle VIDEO Using the Moodle forum option of each person posts one discussion # Simple Discussion Forum in Moodle VIDEO How to create a simple discussion forum in Moodle # Validating a Logistic Model in R In this post, we are going to continue our analysis of the logistic regression model from the post on logistic regression in R. We need to rerun all of the code from the last post to be ready to continue. As such the code form the last post is all below library(MASS);library(bestglm);library(reshape2);library(corrplot); library(ggplot2);library(ROCR) data(survey) survey$Clap<-NULL
survey$W.Hnd<-NULL survey$Fold<-NULL
survey$Exer<-NULL survey$Smoke<-NULL
survey$M.I<-NULL survey<-na.omit(survey) pm<-melt(survey, id.var="Sex") ggplot(pm,aes(Sex,value))+geom_boxplot()+facet_wrap(~variable,ncol = 3) pc<-cor(survey[,2:5]) corrplot.mixed(pc) set.seed(123) ind<-sample(2,nrow(survey),replace=T,prob = c(0.7,0.3)) train<-survey[ind==1,] test<-survey[ind==2,] fit<-glm(Sex~.,binomial,train) exp(coef(fit)) train$probs<-predict(fit, type = 'response')
train$predict<-rep('Female',123) train$predict[train$probs>0.5]<-"Male" table(train$predict,train$Sex) mean(train$predict==train$Sex) test$prob<-predict(fit,newdata = test, type = 'response')
test$predict<-rep('Female',46) test$predict[test$prob>0.5]<-"Male" table(test$predict,test$Sex) mean(test$predict==test$Sex) Model Validation We will now do a K-fold cross validation in order to further see how our model is doing. We cannot use the factor variable “Sex” with the K-fold code so we need to create a dummy variable. First, we create a variable called “y” that has 123 spaces, which is the same size as the “train” dataset. Second, we fill “y” with 1 in every example that is coded “male” in the “Sex” variable. In addition, we also need to create a new dataset and remove some variables from our prior analysis otherwise we will confuse the functions that we are going to use. We will remove “predict”, “Sex”, and “probs” train$y<-rep(0,123)
train$y[train$Sex=="Male"]=1
my.cv<-train[,-8]
my.cv$Sex<-NULL my.cv$probs<-NULL

We now can do our K-fold analysis. The code is complicated so you can trust it and double check on your own.

bestglm(Xy=my.cv,IC="CV",CVArgs = list(Method="HTF",K=10,REP=1),family = binomial)
## Morgan-Tatar search since family is non-gaussian.
## CV(K = 10, REP = 1)
## BICq equivalent for q in (6.66133814775094e-16, 0.0328567092272112)
## Best Model:
##                Estimate Std. Error   z value     Pr(>|z|)
## (Intercept) -45.2329733 7.80146036 -5.798014 6.710501e-09
## Height        0.2615027 0.04534919  5.766425 8.097067e-09

The results confirm what we alreaedy knew that only the “Height” variable is valuable in predicting Sex. We will now create our new model using only the recommendation of the kfold validation analysis. Then we check the new model against the train dataset and with the test dataset. The code below is a repeat of prior code but based on the cross-validation

reduce.fit<-glm(Sex~Height, family=binomial,train)
train$cv.probs<-predict(reduce.fit,type='response') train$cv.predict<-rep('Female',123)
train$cv.predict[train$cv.probs>0.5]='Male'
table(train$cv.predict,train$Sex)
##
##          Female Male
##   Female     61   11
##   Male        7   44
mean(train$cv.predict==train$Sex)
## [1] 0.8536585
test$cv.probs<-predict(reduce.fit,test,type = 'response') test$cv.predict<-rep('Female',46)
test$cv.predict[test$cv.probs>0.5]='Male'
table(test$cv.predict,test$Sex)
##
##          Female Male
##   Female     16    7
##   Male        1   22
mean(test$cv.predict==test$Sex)
## [1] 0.826087

The results are consistent for both the train and test dataset. We are now going to create the ROC curve. This will provide a visual and the AUC number to further help us to assess our model. However, a model is only good when it is compared to another model. Therefore, we will create a really bad model in order to compare it to the original model, and the cross validated model. We will first make a bad model and store the probabilities in the “test” dataset. The bad model will use “age” to predict “Sex” which doesn’t make any sense at all. Below is the code followed by the ROC curve of the bad model.

bad.fit<-glm(Sex~Age,family = binomial,test)
test$bad.probs<-predict(bad.fit,type='response') pred.bad<-prediction(test$bad.probs,test$Sex) perf.bad<-performance(pred.bad,'tpr','fpr') plot(perf.bad,col=1) The more of a diagonal the line is the worst it is. As we can see the bad model is really bad. What we just did with the bad model we will now repeat for the full model and the cross-validated model. As before, we need to store the prediction in a way that the ROCR package can use them. We will create a variable called “pred.full” to begin the process of graphing the original full model from the last blog post. Then we will use the “prediction” function. Next, we will create the “perf.full” variable to store the performance of the model. Notice, the arguments ‘tpr’ and ‘fpr’ for true positive rate and false positive rate. Lastly, we plot the results pred.full<-prediction(test$prob,test$Sex) perf.full<-performance(pred.full,'tpr','fpr') plot(perf.full, col=2) We repeat this process for the cross-validated model pred.cv<-prediction(test$cv.probs,test$Sex) perf.cv<-performance(pred.cv,'tpr','fpr') plot(perf.cv,col=3) Now let’s put all the different models on one plot plot(perf.bad,col=1) plot(perf.full, col=2, add=T) plot(perf.cv,col=3,add=T) legend(.7,.4,c("BAD","FULL","CV"), 1:3) Finally, we can calculate the AUC for each model auc.bad<-performance(pred.bad,'auc') auc.bad@y.values ## [[1]] ## [1] 0.4766734 auc.full<-performance(pred.full,"auc") auc.full@y.values ## [[1]] ## [1] 0.959432 auc.cv<-performance(pred.cv,'auc') auc.cv@y.values ## [[1]] ## [1] 0.9107505 The higher the AUC the better. As such, the full model with all variables is superior to the cross-validated or bad model. This is despite the fact that there are many high correlations in the full model as well. Another point to consider is that the cross-validated model is simpler so this may be a reason to pick it over the full model. As such, the statistics provide support for choosing a model but they do not trump the ability of the research to pick based on factors beyond just numbers. # Logistic Regression in R In this post, we will conduct a logistic regression analysis. Logistic regression is used when you want to predict a categorical dependent variable using continuous or categorical dependent variables. In our example, we want to predict Sex (male or female) when using several continuous variables from the “survey” dataset in the “MASS” package. library(MASS);library(bestglm);library(reshape2);library(corrplot) data(survey) ?MASS::survey #explains the variables in the study The first thing we need to do is remove the independent factor variables from our dataset. The reason for this is that the function that we will use for the cross-validation does not accept factors. We will first use the “str” function to identify factor variables and then remove them from the dataset. We also need to remove in examples that are missing data so we use the “na.omit” function for this. Below is the code survey$Clap<-NULL
survey$W.Hnd<-NULL survey$Fold<-NULL
survey$Exer<-NULL survey$Smoke<-NULL
survey$M.I<-NULL survey<-na.omit(survey) We now need to check for collinearity using the “corrplot.mixed” function form the “corrplot” package. pc<-cor(survey[,2:5]) corrplot.mixed(pc) corrplot.mixed(pc) We have an extreme correlation between “We.Hnd” and “NW.Hnd” this makes sense because people’s hands are normally the same size. Since this blog post is a demonstration of logistic regression we will not worry about this too much. We now need to divide our dataset into a train and a test set. We set the seed for. First, we need to make a variable that we call “ind” that is randomly assigned 70% of the number of rows of survey 1 and 30% 2. We then subset the “train” dataset by taking all rows that are 1’s based on the “ind” variable and we create the “test” dataset for all the rows that line up with 2 in the “ind” variable. This means our data split is 70% train and 30% test. Below is the code set.seed(123) ind<-sample(2,nrow(survey),replace=T,prob = c(0.7,0.3)) train<-survey[ind==1,] test<-survey[ind==2,] We now make our model. We use the “glm” function for logistic regression. We set the family argument to “binomial”. Next, we look at the results as well as the odds ratios. fit<-glm(Sex~.,family=binomial,train) summary(fit) ## ## Call: ## glm(formula = Sex ~ ., family = binomial, data = train) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.9875 -0.5466 -0.1395 0.3834 3.4443 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -46.42175 8.74961 -5.306 1.12e-07 *** ## Wr.Hnd -0.43499 0.66357 -0.656 0.512 ## NW.Hnd 1.05633 0.70034 1.508 0.131 ## Pulse -0.02406 0.02356 -1.021 0.307 ## Height 0.21062 0.05208 4.044 5.26e-05 *** ## Age 0.00894 0.05368 0.167 0.868 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 169.14 on 122 degrees of freedom ## Residual deviance: 81.15 on 117 degrees of freedom ## AIC: 93.15 ## ## Number of Fisher Scoring iterations: 6 exp(coef(fit)) ## (Intercept) Wr.Hnd NW.Hnd Pulse Height ## 6.907034e-21 6.472741e-01 2.875803e+00 9.762315e-01 1.234447e+00 ## Age ## 1.008980e+00 The results indicate that only height is useful in predicting if someone is a male or female. The second piece of code shares the odds ratios. The odds ratio tell how a one unit increase in the independent variable leads to an increase in the odds of being male in our model. For example, for every one unit increase in height there is a 1.23 increase in the odds of a particular example being male. We now need to see how well our model does on the train and test dataset. We first capture the probabilities and save them to the train dataset as “probs”. Next we create a “predict” variable and place the string “Female” in the same number of rows as are in the “train” dataset. Then we rewrite the “predict” variable by changing any example that has a probability above 0.5 as “Male”. Then we make a table of our results to see the number correct, false positives/negatives. Lastly, we calculate the accuracy rate. Below is the code. train$probs<-predict(fit, type = 'response')
train$predict<-rep('Female',123) train$predict[train$probs>0.5]<-"Male" table(train$predict,train$Sex) ## ## Female Male ## Female 61 7 ## Male 7 48 mean(train$predict==train$Sex) ## [1] 0.8861789 Despite the weaknesses of the model with so many insignificant variables it is surprisingly accurate at 88.6%. Let’s see how well we do on the “test” dataset. test$prob<-predict(fit,newdata = test, type = 'response')
test$predict<-rep('Female',46) test$predict[test$prob>0.5]<-"Male" table(test$predict,test$Sex) ## ## Female Male ## Female 17 3 ## Male 0 26 mean(test$predict==test$Sex) ## [1] 0.9347826 As you can see, we do even better on the test set with an accuracy of 93.4%. Our model is looking pretty good and height is an excellent predictor of sex which makes complete sense. However, in the next post we will use cross-validation and the ROC plot to further assess the quality of it. # Gradebook Views in Moodle VIDEO Gradebook views in Moodle # 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 specify the functional relationship between the response and continuous variables. This allows you to explore the data for potential relationships that can be more rigorously tested with other statistical models In our example, we will use the “Auto” dataset from the “ISLR” package and use the variables “mpg”,“displacement”,“horsepower”, and “weight” to predict “acceleration”. We will also use the “mgcv” package. Below is some initial code to begin the analysis library(mgcv) library(ISLR) data(Auto) We will now make the model we want to understand the response of “acceleration” to the explanatory variables of “mpg”,“displacement”,“horsepower”, and “weight”. After setting the model we will examine the summary. Below is the code model1<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight),data=Auto) summary(model1) ## ## Family: gaussian ## Link function: identity ## ## Formula: ## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 15.54133 0.07205 215.7 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(mpg) 6.382 7.515 3.479 0.00101 ** ## s(displacement) 1.000 1.000 36.055 4.35e-09 *** ## s(horsepower) 4.883 6.006 70.187 < 2e-16 *** ## s(weight) 3.785 4.800 41.135 < 2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## R-sq.(adj) = 0.733 Deviance explained = 74.4% ## GCV = 2.1276 Scale est. = 2.0351 n = 392 All of the explanatory variables are significant and the adjust r-squared is .73 which is excellent. edf stands for “effective degrees of freedom”. This modified version of the degree of freedoms is due to the smoothing process in the model. GCV stands for generalized cross-validation and this number is useful when comparing models. The model with the lowest number is the better model. We can also examine the model visually by using the “plot” function. This will allow us to examine if the curvature fitted by the smoothing process was useful or not for each variable. Below is the code. plot(model1) We can also look at a 3d graph that includes the linear predictor as well as the two strongest predictors. This is done with the “vis.gam” function. Below is the code vis.gam(model1) If multiple models are developed. You can compare the GCV values to determine which model is the best. In addition, another way to compare models is with the “AIC” function. In the code below, we will create an additional model that includes “year” compare the GCV scores and calculate the AIC. Below is the code. model2<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight)+s(year),data=Auto) summary(model2) ## ## Family: gaussian ## Link function: identity ## ## Formula: ## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) + ## s(year) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 15.54133 0.07203 215.8 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(mpg) 5.578 6.726 2.749 0.0106 * ## s(displacement) 2.251 2.870 13.757 3.5e-08 *** ## s(horsepower) 4.936 6.054 66.476 < 2e-16 *** ## s(weight) 3.444 4.397 34.441 < 2e-16 *** ## s(year) 1.682 2.096 0.543 0.6064 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## R-sq.(adj) = 0.733 Deviance explained = 74.5% ## GCV = 2.1368 Scale est. = 2.0338 n = 392 #model1 GCV model1$gcv.ubre
##   GCV.Cp
## 2.127589
#model2 GCV
model2\$gcv.ubre
##   GCV.Cp
## 2.136797

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

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

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

Conclusion

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

# Creating Assignments in Moodle Video

Below is a simple and brief video on how to create assignments in Moodle

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.

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 focuses 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 Ph.D. 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 several small rather than sweeping adjustments.

This is one reason advanced students often like to ask those minute grammar questions. These small questions are 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 somewhat specializes and not easy to articulate for many teachers.