Monthly Archives: June 2015

Types of Schools During the Colonial Period in the United States

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In a prior post, we looked at the three regions of the colony period and how they differed in their approach to education. This post takes a closer look at the different types of schools that were found in the American colonies. Furthermore, we will look at some of the teaching materials used during this period.

Types of Schools

Among some of the schools found in the American colonies includes the following

  • Parochial/Private schools
  • Town schools
  • Latin Grammar Schools
  • Academies
  • Colleges

Parochial/Private Schools

The parochial/private schools were found in the middle and southern colonies. The difference between parochial and private schools is that the former was religiously focus while the latter often was not. The focus of these schools was on reading. writing, and arithmetic, which came to be known as the “three R’s”. Parochial schools would always include a religious element to it such as memorizing sermons, singing hymns, or Bible study.

Town Schools

Towns schools were found in the New England colonies. These schools were locally controlled and conditions were not always accommodating for learning. The schools were often only one room and weather would affect attendance severely.  The primary goal was on memorization which was assessed by the teacher.

Latin Grammar Schools

Latin Grammars schools were in many ways a type of high school for the affluent. They were intended for those who were going to enter some form of profession such as medicine, law, or business. A boy (no ladies at this time) would enter the school at around 8 and complete their studies around the age of 16-17.

The focus of the curriculum was on the classics. In many ways, perennialism had its roots in this system. Students studied Greek, Latin, rhetoric, logic, and other subjects. This was a humanist curriculum and had much in common with education in Europe.

Academies

The origin of academies was in developing an educational model for those who were not going to college. Academies were invented by Benjamin Franklin. The curriculum focused on vocational skills and was much more secular in nature. Students would study history in place of the bible. The academies form the foundation for vocational curriculum later in American history.

Colleges

Students who completed their studies at the Latin Grammar school often went on to college. College originally was a place to further train ministers of the gospel. Both Yale and Harvard were started for this purpose. The curriculum of this schools focused on the Latin, Greek, astronomy, ethics, natural sciences, and more. Colleges were not only for ministers but this was one of its main purposes.

Conclusion

There were different schools for different needs. From educating children occasionally to higher education that focused on the preparation of an educated core of leaders, education was highly diverse at this time. One concern was the lack of uniformity and control over the system. This is a problem that will be recurring throughout history in American education.

Introduction to Vectors Part III: Logical Vectors and More

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Logical vectors are vectors that compare values. The response R gives is either TRUE which means that the comparision statement is correct or FALSE which means the comparision statement is incorrect.

Logical vectors use various operators that indicate ‘greater than’, ‘less than’, ‘equal to’, etc. As this is an abstract concept, it is better to work through several examples to understand.

You want to know how many times James scored more than 20 points in a game. to determine this you develop a simple equation that R will answer with a logical vector

  • > points.of.James <- c(12, 15, 30, 25, 23, 32)
> points.of.James
[1] 12 15 30 25 23 32
> points.of.James > 20
[1] FALSE FALSE  TRUE  TRUE  TRUE  TRUE

Here is what we did

  1. We inputted the values for ‘points.of.James
  2. We then entered the equation ‘points.of.James > 20’ which means which values in the variable ‘points.of.James’ are greater than 20′.
  3. R replied by stating that the first to values are not greater than 20, which is why they are FALSE and that the last 4 values are greater than 20, which is why they are TRUE.

Logical vectors can also be used to compare values in different vectors. This involves the use of the function ‘which(). The function which() is used for comparing different vectors. Below is an example

You want to know which games that James scored more points than Kevin. Below is the code for doing this

  • > points.of.James <- c(12, 15, 30, 25, 23, 32)
> points.of.Kevin <- c(20, 19, 25, 30, 31, 22)
> the.best <- points.of.Kevin < points.of.James 
> which(the.best) 
[1] 3 6

Here is what happen

  1. You set the values for the variables ‘points.of.James’ and ‘points.of.Kevin’
  2. You create a new variable called ‘the.best’. In this variable you set the equation that compares when Kevin scored less than James by comparing the values in the variable ‘points.of.Kevin’ with ‘points.of.James
  3. You then used the ‘which()’ function for it to tell which times that Kevin scored less than James.
  4. R responds by telling you that Kevin scored less than James the 3rd time and 6th time

You can also find not each time that Kevin scored less than James but instead find out how many times Kevin scored less than James by using the ‘sum()’ function instead of the ‘which()’ function.

> points.of.James <- c(12, 15, 30, 25, 23, 32)
> points.of.Kevin <- c(20, 19, 25, 30, 31, 22)
> the.best <- points.of.Kevin < points.of.James 
> sum(the.best)
[1] 2

R explains that Kevin scored less than James two times.

Naturally, there is much more that can be done with vectors than what was covered here. This is just a glimpse at what is possible.

Origin of Colonial Schools in the United States

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Colonial education in the United States dates back to the 17th century. In general, the characteristics of education in the United States can be divided into three regions, which are…

  • New England schools
  • Middle colonies
  • Southern Colonies

We will explore each of these areas briefly in this post.

New England Schools

New England Schools included the colonies of Massachusetts, Rhode Island and other colonies of the utmost northern part of the American colonies. Schools were initially started in this area through the establishment of various laws. For example, Massachusetts, in 1642, passed a law that required parents to teach their children to read and understand the bible and the laws of the colony. In 1647, the “Old Deluder Satan” act was passed. This law stipulated that towns of a certain size had to employ teachers. This law provides an insight into how important religion was at this time in American history. The example of Massachusetts was followed by most of the other New England colonies.

Middle Colony Schools

The Middle Colonies, which included New York, New Jersey, and other colonies, were never able to adopt a single system of education as was found in the New England Colonies. In comparison to the New England Colonies, there was much more religious and political variety in the Middle Colonies. As such, this independence of thought and action was seen in the establishment of schools. In the Middle Colonies, one will find independent and parochial schools to match the many different flavors of ethnicities and religions. Schools were usually locally controlled.

Southern Colonies

The Southern Colonies had the most independent spirit in term of education than the New England or Middle Colonies. In the South, the education of children was left to the family with almost no state interference. For poor children, the most education they could hope for was training in some sort of vocation, which did not include learning to read and write. For children of affluence, such as the plantation owners, the best education was provided through private tutors. For slaves, it was actually illegal to even learn to read and write. The political system of the South prevented the growth of a school system since a widely available education was seen as a threat to the established order.

Conclusion

As one moves from North to South, one will see that the control over schools gradually decreases. From the religiously inspired laws of the North to the independent spirit of the South, the control over education varied greatly. This can be seen in who goes to school. In the North, everyone was “required” to go to. school. In the South, some were “required” to not go to school.

The curriculum of all areas consisted of reading, writing, and arithmetic. Learning emphasized memorizing and conformity. These values would endure for almost the first 200 years of American history.

Introduction to Vectors in R: Part II

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In a previous post, we took our first look at vectors and their use in R. In this post, we will build on this knowledge by learning more ways to use and analyze vectors.

Functions for Analyzing Vectors

An important function for analyzing vectors is the str() function. This function allows you to look at the structure or characteristics of any object, including vectors. Objects are the pieces of data you create and or manipulate in R.

We are going to analyze the structure of the variable ‘points.of.James’, which contains a vector. Below is the code for this followed by an explanation.

  • > points.of.James > str(points.of.James)
 num [1:6] 12 15 30 25 23 32

Here is what we did

  1. We created the variable ‘points.of.James’
  2. We assigned the values 12, 15,30, 25, 23, 32 to the variable ‘points.of.James’ Using a vector
  3. We used the str() function to analyze the variable ‘points.of.James’
  4. The output told us the following
    1. num means that the vector is numeric
    2. 1:6 shares two pieces of information. The 1 tells us how many dimensions the example, which is one. The six tells us how many values or indices the vector has, which is six. In other words there are six numbers in the variable. Remember the word ‘indices’, which refers to the location of a value within a vector, as it will be important in the future.

Let’s say you are curious to know how many indices a vector has. To figure this out you use the length()  function is demonstrated below.

  • > points.of.James > length(points.of.James)
[1] 6

You can probably see that the variable ‘points.of.James’ has six values within it.

Another useful function for vectors is the ability to combine them. In the example below, we will combine the variables ‘points.of.James’ and ‘points.of.Kevin’.

  • > points.of.James > points.of.Kevin 
> all.points > all.points  
[1] 12 15 30 25 23 32 20 19 25 30 31 22

Here is what happen

  1. We made the variables ‘point.of.James’ and ‘points.of.Kevin’ and inputted the values
  2. We created a new variable called ‘all.points’ and assigned the variables ‘points.of.James’ and ‘points.of.Kevin’ to it.
    1. NOTE: Assigning a variable to another variable means assigning the values of the first variable to the second one. In other words, the values of ‘points.of.James’ and the values of ‘points.of.Kevin’ are now stored in the variable ‘all.points’
  3. We then read the ‘all.points’ variable and it showed us all of the values within it. Which the same values found with ‘points.of.James’ and ‘points.of.Kevin’.

One last useful tool when using vectors is the ability to extract values from a vector. This can be done by using the brackets or [ ]. Extracting values means taking a handful of values from a vector and looking at them alone. Below is an example.

  • > all.points
 [1] 12 15 30 25 23 32 20 19 25 30 31 22
> all.points[10]
[1] 30

In the example above we were looking at our ‘all.points’ variable. I typed all.points[10] into R and this told R to do the following

  • Extract the tenth value from the ‘all.points’ variable

R then replies by telling me the tenth value of the ‘all.points’ variable is 30.

Off course, you can extract multiple values as seen below

  • > all.points
 [1] 12 15 30 25 23 32 20 19 25 30 31 22
> all.points[c(2, 4, 6, 8)]
[1] 15 25 32 19

In this example, we extracted the second, fourth, sixth, and 8th value from the variable ‘all.points’

This is still just an introduction into how vectors can be used in R.

Doing Simple Math in R

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As a mathematical program, R is able to calculate a wide range of mathematical functions. This post will cover some basic operations involving variables and vectors.

We will start with an example, imagine that two basketball players, James and Kevin, have decided to donate money to a local charity forever point they score in a game. James agrees to give $150.00 per point and Kevin agrees to give $130.00 per point. This promise is for the last six games they have played. You want to know the following

  1. How much money did they give combined for each game?

Below is the code for this scenario.

  • > points.of.James <- c(12, 15, 30, 25, 23, 32)
> points.of.Kevin <- c(20, 19, 25, 30, 31, 22)
> James.money <- points.of.James * 150
> Kevin.money <- points.of.Kevin * 130
> James.money + Kevin.money
[1] 4400 4720 7750 7650 7480 7660

Here is what we did

  1. We need to know how many points James and Kevin scored in each game. Each of these values were set to their own variable “points.of.James” and “points.of.Kevin” respectively. This information was provided for you.
  2. We then needed to figure out how much money James gave away by multiplying his points by the 150.00 per point he promised. This value was assigned to the variable “James.money” We also did this for Kevin but he only promised to pay 130.00 per point. Kevin’s amount was set to the variable “Kevin.money”
  3. Finally, we combined the amount James and Kevin promised by adding together the variables “James.money” and “Kevin.money”

Rounding

Numbers can also be rounded in R. Going back to our previous example. Let’s say we want to know how much James and Kevin gave round to the nearest thousand. Below is the code

> round(James.money + Kevin.money, digits = -3)
[1] 4000 5000 8000 8000 7000 8000

Here is what happened

  1. We used the “round” function to round the number.
  2. The variables “James.money” and “Kevin.money were put inside the parenthesis with the operator + put between them so that R adds them together.
  3. Since we are rounding, we told are that we want to round to the nearest thousand by adding the argument “digits” with an equal sign followed by negative three. The negative tells R to round by looking to the left three place from the decimal. If we wanted to round to the nearest thousandth we would have used positive 3.

This was just an introduction to some of the basic mathematical functions of R

Basics of Vectors Functions in R

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Vectors were discussed in a previous post. In short, vectors are a collection of information. Functions serve the purpose of performing several operations one after another. Now, will combine these two concepts into what are known as vectorized functions. Vectorized functions are functions that perform several operations on a vector. In other words, you can put several numbers into a vector and then have a function do something to all the numbers at the same time.

Here is an example

You have been asked to keep score during basketball season. Below are the number of points per game for a player name James for his first six games. The information is inputted in R as follows

  • > points.of.James <- c(24, 8, 8, 12, 18, 9)
> points.of.James
[1] 24 8 8 12 18 9

What we have done so far is create the variable “points.of.James” and assigned the vector of 24, 8, 8, 12, 18, 9. The points represent the number of points he scored in the six games.

The first function we are going to use is the sum() function. This function will add up how many points James scored in the six games. Below is the code for it.

  • > sum(points.of.James)
[1] 79

Here is what we did

  1. We told R we want the sum of the values in the variable “points.of.James”
  2. The values in the variable “points.of.James” are the vector 24, 8, 8, 12, 18, 9.
  3. R adds the values up and produces the answer 79

Another more complicated example has to do with how functions can combine vectors. For example, let’s say a list of people in the English department and you want to add after their name that they all have the position of lecturer. To do this you need to use the paste() function which will combine the information. Below is an example of how to do this using R.

  • >  faculty.names <- c("Darrin Thomas", "John Williams", "Sarah Smith")
>  Rank <- "Lecturer"
>  Rank <- ", Lecturer"
>  paste(faculty.name, Rank, sep = "")
[1] "Darrin Thomas, Lecturer" "John Williams, Lecturer" "Sarah Smith, Lecturer"

Here is what we did.

  1. We created a variable called “faculty.names” and we assigned a vector to it that contain the following names
    1. Darrin Thomas
    2. John Williams
    3. Sarah Smith
  2. When then created a variable called “Rank” and assigned the value “, Lecturer”. We put the comma in front of the word Lecturer so that when we combine the faculty.names and rank variables we have a comma after the name of the faculty person and before their rank, which is lecturer.
  3. Next, we combined all of the names of the faculty members with the rank of “Lecturer” using the paste() function.
    1. NOTE: In the paste function we included the argument sep = “” This prevents a space appearing after the name of the person when the comma is inserted. Below is an example of what we do not want
      1. Darrin Thomas , Faculty   (Remeber we do not want this space. That’s why we use the sep = “” argument because it removes the space in front of the comma)
  4. After setting up the paste function we get the “faculty.names” and “Rank” combined which shows the names of the faculty members with their corresponding rank.

Let’s pretend you are dealing with the same problem but now the faculty members have different rankings. Here is the code for how to do this.

  • > faculty.names <- c("Darrin Thomas", "John Williams", "Sarah Smith")  
> faculty.ranks <- c(", Lecturer", ", Senior Lecturer", ", Principal Lecturer")
> paste(faculty.names, faculty.ranks, sep="")
[1] "Darrin Thomas, Lecturer" "John Williams, Senior Lecturer"  
[3]"Sarah Smith, Principal Lecturer"

All that is new is that we created a variable called “faculty.ranks”, which included the new rankings. Then we used the paste function to combine the names with ranks. As you can see, each person can their corresponding rank. The first name got the first rank, the second name got the second rank, etc. If there is a difference between the number of names and ranks R will recycle values to complete the function

Sourcing Script in R

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Sourcing a script has to do with having R running several commands either one after another with stopping. In order to do this R Studio, you need to type your code into Source Editor which is normally at the top left-hand side of the program. We are going to go through an example in which R will ask a question and you will answer it.

In the source editor, type the following information. At the end of each line, press enter to move to the next line. I will explain the meaning of the text after the example

h <- "Welcome to R"
yourname <- readline("What is your name?")
print(paste(h, yourname))

After typing this information, you should click on the source button near the top middle of the computer screen. If this is done correctly you should see the following in the console.

> source('~/.active-rstudio-document')
What is your name?

Type your name in response to the question and press enter. Then you should see the following.

> source('~/.active-rstudio-document')
What is your name?Darrin
[1] "Welcome to R Darrin"

So what exactly did we do?

  1. We made a variable named h and assigned the object “Welcome to R”
  2. Next, we made a variable called yourname and assigned the function readline to read the phrase “What is your name?”. The readline function literally reads text.
  3. We then told R to print a combination (using the paste function) of the h variable first (Welcome to R) and the yourname variable second (What is your name?)
  4. Next, we clicked the source script button
  5. In the console, we were asked the question “What is your name?”
  6. We responded with our name.
  7. R takes your name and combines it with the h variable to create the following phrase
  8. “Welcome to R Darrin”

Several steps of code were run at once through using the script editor. This same process could have been done in the console but using the script editor saves time. Many aspects of programming focus on saving time and improving efficiency. There are always several ways to do something but the goal is always to find the method that takes the least amount of effort and improves the readability of the code.

Making and Using Variables in R

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Variables are used in R to store information for computational purposes. It seems that there is almost no limit to what can be stored in a variable. To make a variable, you need to know the following information.

  • The name you want to give the variable
  • The information you want to store in the variable

Here is an example,

You want to make a variable that will store the following test score: 80, 81, 82, 83, 84, 85. You want to call the variable test_scores. Here is what we know

  • The name of the variable is test_scores
  • It will contain the values of 80, 85, 90, 95, 100

Here is how this would look in R.

  • > test_score <- 80:85

There are a few things to explain

  1. the <- sign means “assigned to” in other words, the variable name on the left of the <- sign is being assigned to the values 80:85.
  2. The colon sign stands for a sequence. We wanted all whole numbers from 80 to 85 and the colon sign provides this information.
  3. Both the <- and : are known as operators in R. Operators symbols you place between numbers in order to make a calculation.

Know, type test_scores into the R console and press enter. You should see the following.

  • > test_scores
[1] 80 81 82 83 84 85

R now shows you everything that is stored in the variable. We can also created other variables and perform calculations through the use of variables. For example, let’s say that you want to add 5 points of extra credit to the scores of the test. To do this let’s make a variable called extra_credit and add test_scores to extra_credit

  • > extra_credit <- 5
> test_scores + extra_credit
[1] 85 86 87 88 89 90

As you can see, R took the values of test_scores and add extra_credit to each value in test_scores.  This is much faster than entering each value separately to calculate it. We can also make a new variable for the new scores and we can call it revised_test_scores Let’s try

  • > revised_test_scores <- test_scores + extra_credit
> revised_test_scores
[1] 85 86 87 88 89 90

Variables can also be used for text. The only difference is that you must put quotes around the words. Otherwise, the computer will think the words are numbers, which does not make sense. Below is an example,

  • > h <- "Howdy"
> h
[1] "Howdy"

Lastly, variables can be used to store vectors. This is very useful in saving a lot of time in performing calculations. We will now  make the variable student_names and assigned a vector to it containing the names of students.

  • > student_names <- c("David", "Edward", "John")
> student_names
[1] "David" Edward" "John"

This is only the beginning of some of the amazing features of R.

Introduction to Vectors in R

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A key component of R is the use of vectors. Vectors a single piece of information that contents a collection of information. This probably sounds extremely confusing so an example will be provided.

Think of an organization such as a school. We will call the school Asia International School. Asia International school consist of an administrator named Dr. T, teachers named Mr. Bob and Mrs . Smith, and students named Sam, David, and Mary. In this example, the school is consider a vector or a single piece of information (Asia International School). The administrators, teachers, and students are the collection of information within the large piece of information that is the school.

If we wanted to write this example using the R programming language it would look something like the following…

  • > Asia International School(Dr. T, Mr. Bob, Mrs. Smith, Sam, David, Mary)
[1] Dr. T Mr. Bob Mrs. Smith Sam David Mary

To be fair this is not exactly how it is done this is strictly an imperfect illustration of a very abstract concept.

A Real Example

In order to make a vector in R you need to use c() function. A function is a piece of code that does something to the information that is within its parentheses. For example, let’s make a vector that contains the numbers 1, 2, 3, 4, 5.

  • > c(1,2,3,4,5)
[1] 1 2 3 4 5

To get the second line you need you press enter

The c means combine. The information inside the parentheses is the information that is being combined into one vector. Going back to our school example, Dr. T, Mr. Bob, Mrs. Smith, Sam, David, and Mary were being combined into the school Asia International. In a vector, all information inside the parentheses is known as arguments.

Conclusion

This is just some of the most basic ideas about vectors. There is a great deal more to explore about the use of vectors which is considered one of the most powerful features of R. The challenge of learning R is with the abstract nature of programming. You have to think of things you want to do in terms of a code that the computer can understand. This is very confusing for most people.

Using R

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The R program can be used only to process many different mathematical functions. However, many people choose to use some sort of editing tool while using R. The editing tool provides a place for developing and save codes and functions.

There are many different editing tools available. For Windows, a popular choice is RGui. For Mac, R.app is a common choice. The choice that is quickly becoming the standard for R Users is RStudio. RStudio works on all software platforms. This provides a consistent interface for people despite whatever operating system they are using. Below are some additional benefits of RStudio.

  • Brackets are automatically setup when developing code
  • Different parts of a code have a corresponding color. This helps in reading the code.
  • Code completion. Saves time

Coding in RStudio

The first thing people often do when learning to code is create the message “Hello World.” To do this in RStudio requires the following

  1. In RStudio make sure the cursor is blinking in the console section (The console is normally in the lower left hand part of the window)
  2. Type the following and press enter
    1. print (“Hello World!”)
  3. After pressing enter you should see the following
    1. [1] “Hello World!”

Congrats, you have just developed and implemented your first R Script

Doing Some Math

R can be used for performing math calculations as well. Consider the following example

  1. Type the following into the console and press enter
    1. 1 + 3 + 5 + 7
  2. You should get the following output
    1. [1] 16

Have some fun playing with the print as well as calculating various math problems as well.

The History and Characteristics of R

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R is a programming language and software environment that is used for the development of graphic data products and the computation of many forms of mathematics. The history of R goes back about 20-30 years ago. This post will look at the history of R as well as the Characteristics of this software.

The History

Ross Ihaka and Robert Gentleman are the developers of R. R is actually based on an older programming language known as S which goes back to the 1970″s. Ihaka and Gentleman develop their own programming language while working together in New Zealand. With the release of R in the early 1990’s, several people joined the project to help to improve it. By 1995, the software had become “open-sourced” which means that anyone can use and modify it for themselves without cost. By 2000, the first version of R (1.0) was released to the public.

Characteristics of R

In many peoples opinion, the best feature of R is the price. Being free, R is by far one of the best softwares for statistical analysis is price is the most important criterion. SPSS and SAS are also great and user-friendlier, however, their price is completely outlandish for most individual researchers. R removes this problem completely

R also has an active community around it that supports its development. For example, people are able to develop packages that provide assistance in running various task in the R software. Naturally, most packages are free as well. The focus on community has enabled R to be run on almost any operating system as well, such as Windows, OSX, or Linux.

R also allows people to make graphs and data products. The graphs are actually very well made. The drawback is understanding the coding necessary to develop these various products. This is discussed more below.

One major drawback that affects the typical computer user is learning the programming language of R. This can be challenging for those who are not techie or able to think abstractly in computer codes. I have never seen a satisfactory way to get around this problem but to crack open a book and practice, practice, practice. With time, the code will start to make sense but it is not a five-minute process for someone who has not studied programming.

Conclusion

Despite the challenges of learning computer programming R is becoming the software of choice for many. The benefits far outweigh the problems for many individuals. Personally, I am looking forward to continuing to develop skills in understanding this dynamic software.

Evaluation Models Part III: Eisner’s Criticism Model

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In the last few posts, we have looked at evaluation models that come from a scientific approach. In this post, we look at evaluation from a humanistic perspective.

Curriculum evaluation models that come from a humanistic approach see evaluation as more qualitative than scientific models. Humanistic evaluators believe that the process of assessing curriculum is too complex and messy to rely so heavily on quantitative methods. There should be fewer numbers and more written descriptions  The purpose is not to answer questions completely but to add to the conversation by exposing additional questions.

One example of an evaluation model that adheres to the principles of humanistic approaches is Eisner’s Criticism Model.

Eisner’s Criticism Model

Eisner’s Criticism Model is used for evaluating a new curriculum and not necessarily to examine a curriculum that is established. This model has four steps as follows

  1. Describe
  2. Interpret
  3. Evaluate
  4. Identify themes

The steps are mainly self-explanatory. First, evaluators describe the setting and curriculum of the study. Second,  the evaluators explain the reasons for the new curriculum by explaining the need. Third, The evaluators present their understanding of the value of the new curriculum. Fourth, various themes are identified within the curriculum that are meaningful.

This four-step process involves qualitative activities for data collection. Evaluators might participate in classes, observe classes or other activities, analyze student work,  use video, photos, interviews, of teacher and students in action. The goal is to notate what is happening but also what may not be happening in the data. This involves written narrative much more the numerical summaries.

In order to complete such an evaluation, an individual needs to have an expert knowledge in education. Eisner refers to such individuals as connoisseurs. These are people who know what to look for as well as how to value and appreciate what is happening.

Conclusion

Although not nearly as common as scientific models such as Stufflebeam or Stake humanistic models such as Eisner’s Criticism model has its place in education.  For smaller schools or smaller programs, this model is an excellent way to go beyond just numbers and to describe the effectiveness of a curriculum with words. However, for larger schools or programs it is challenging to use such an approach when there is so much happening that needs to be summarized succinctly.

Evaluation Models Part II: Stufflebeam’s Model

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In the last post, we began a discussion on evaluation models of the scientific variety. In this post, we conclude are look at scientific evaluation models by examining Stufflebeam’s CIPP model. Daniel Stufflebeam is a famous educator and evaluator of curriculum.

Stufflebeam’s model is called the CIPP model which stands for

  • Context
  • Input
  • Process
  • Product

Context

Context is about studying the context in which the curriculum is used. The purpose is to assess what is happening and examine why needs of the stakeholders may not be met. Common activities at this stage include the following.

  • Physical environment
  • Philosophical foundations of the curriculum
  • Determine stakeholders
  • Background of the context

Of course, there are other activities that could take place but these just provide some examples

Input

Input is about determining whether there are adequate resources to conduct an evaluation. Evaluators look at the goals and strategies of the evaluation. If there are concerns, other approaches may be suggested in order to evaluate the curriculum.

In many ways, this is when the methodology of the evaluation is checked rigorously. Common questions assessed at this level include the following

  • Do goals and objectives align?
  • Are teaching strategies suitable?

Process

Whereas context and process take place before implementation,  process evaluation takes place during implementation. At this stage, evaluators examine if the plan is actually happening in the classroom. Evaluators look for defects in the implementation or use of the curriculum. Whatever problems are identified, strategies are developed to address them.

Normally, process happens at a pilot stage before a larger implementation. For example, the algebra teachers might experiment with a new teaching approach before using it in the entire math department at a school. In many ways, process evaluation is formative evaluation.

Product

The product stage involves collecting data to make a decision about the curriculum. Information is gathered to see how well the curriculum is meeting objectives. From this decision are made. The product stage is really a type of summative evaluation.

Conclusion

Stufflebeam’s model involves analyzing the context and the appropriateness of the curriculum to this context. From there, a formative and summative evaluation is used to assess the impact of the curriculum among the stakeholders. This practical model is useful for educators who are seeking ways to examine the various programs under their care.

Evaluation Models Part I: Stake’s Congruence-Contingency Model

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Evaluation models are used in curriculum as a process for assessing the appropriateness of a curriculum for a context. As with approaches to curriculum evaluation, evaluation models can be divided into scientific and humanistic models. For the next few post, we will look at scientific models of curriculum evaluation. Our first example is Robert Stake’s Congruence-Contingency Model.

Congruence-Contingency Model

Stake’s model of curriculum evaluation is more than just an evaluation process. Stake’s model also looks at the development of the curriculum. When using this model, it is necessary to compare the developed curriculum with what actually happens in the classroom.

There are six key terms, broken down into two groups of three, that we need to know in order to understand Stake’s model and they are as follows.

Development Stage

  • Potential prerequisites
  • Potential Curriculum
  • Potential results

Evaluation Stage

  • Prerequisites applied in context
  • Evaluation of operational curriculum
  • Actual results

Prerequisites

The prerequisites is another way of saying “before” or the state of the context before the intervention of teaching. This includes student’s attitude, motivation, prior academic performance, teacher characteristics, and more. In the development stage, the teachers need to identify what are some potential prerequisites that may impact learning. In the evaluation stage, the evaluators determine what prerequisites actually impact the curriculum. In other words, there is a comparison of what was anticipated and what actually was the case in terms of the prerequisites.

Potential & Operational Curriculum

Potential curriculum is the “dream” curriculum that is developed. It includes everything that the teachers want to do. The Operational curriculum is what was actually used. There is normally a discrepancy between the two as it is difficult to cover all of the material and use all of the activities. The evaluation will examine the difference between these two aspects of curriculum as another criterion for assessing the quality of the curriculum.

Potential vs. Actual Results

Potential results are what the teachers hope to see as a result of the use of the curriculum. Actual results are the real performance of the students. The difference between the potential or desired results and actual results is another indicator of the quality of the curriculum in Stake’s model.

Conclusion

Stake’s Model provides evaluators with an opportunity to compare the desired outcome with the actual outcome. The benefit of this is that it is the curriculum developers that set the criteria of evaluation. All the evaluators do is determine if the curriculum performed in a manner that is consistent with the ideas of the developers.