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Each student comes to school, not only with unique academic needs, but also with unique background experiences, culture, language, personality, interests, and attitudes toward learning. Effective teachers recognize that all of these factors affect how students learn in the classroom, and they adjust, or differentiate, their instruction to meet students' needs.

Getting Started

Tomlinson and Imbeau (2010) describe differentiation as creating a balance between academic content and students' individual needs. They suggest that this balance is achieved by modifying four specific elements related to curriculum:

These curriculum-related factors are based on student need in three areas:

The goal of differentiated instruction is to create learning opportunities that make allowances for differences in how individual students learn in order to ensure equal access to important academic content. Content may be modified for students who need additional practice with essential elements before moving on; however, the expectation is that modifications in other areas will ultimately allow all students to master the same key content.

Thus, "differentiated instruction is not the same as individualized instruction. Every student is not learning something different; they are all learning the same thing, but in different ways. And every student does not need to be taught individually; differentiating instruction is a matter of presenting the same task in different ways and at different levels, so that all students can approach it in their own ways" (Trujo, 2004).

It is important to recognize that differentiated instruction is an approach to teaching, not simply a collection of strategies or activities. Effective differentiation requires ongoing evaluation of students' needs and conscious attention to designing instructional activities and assessment to meet those needs. It is true that teachers must have an extensive repertoire of research-based instructional strategies at hand, but they must also be able to "think outside the box" to ensure that each student's needs are met. As Tomlinson and Imbeau (2010) point out, the teacher's role in the differentiated classroom is to continually ask him/herself, "What does this student need at this moment in order to be able to progress with this key content, and what do I need to do to make that happen?" (p. 14).

Content Process Product Affect Readiness Interest Learning Profile

Differentiating Instruction for ELLs

With the recent emphasis on standards-based instruction, there has been much discussion about what constitutes appropriate content, instruction, and assessment for English language learners. As educators have grappled with this issue, it has become clear that educational parity can only be achieved if ELLs have an opportunity to learn the same rigorous academic content as native English speakers. The best way to achieve that goal is through differentiated instruction that takes into account ELLs' English language proficiency, as well as the many other factors that can impact learning (Fairbairn Jones-Vo, 2010).

Differentiated instruction, by definition, is instruction that is designed to support individual students' learning in a classroom of students with varied backgrounds and needs. For this reason, the same general principles that apply to differentiated instruction for native English speakers also apply to ELLs.

Teachers are successful at differentiating instruction for ELLs when they:

For information on differentiating instruction in the reading classroom, see Differentiated Reading Instruction , a Reading Rockets webcast featuring Carol Ann Tomlinson, Michael Pressley, and Louise Spear-Swerling.

ELLs call attention to the incredible diversity that is characteristic of American schools in the 21st century. Today, most U.S. classrooms include students with a wide variety of academic needs, cultural backgrounds, learning styles, and languages. Differentiated instruction offers teachers an effective method of addressing the needs of this diverse population in a way that gives all students equal access to learning.

Get to know as much as possible about each student

Homecoming  In the fifth and last part of the New York Times’ “Invisible Child” series, Chanel and her children move into an apartment in Harlem, NY. (Ruth Fremson / The New York Times / Redux)


The study found that readers of the narrative story felt a higher degree of compassion and empathy for Martinez, feelings that extended to undocumented immigrants as a group. Overall, the narrative readers altered their attitudes and opinions positively and were more interested in seeking additional information about the living conditions of undocumented immigrants, or even taking action to help.

Academics explain this narrative effect via something called “transportation theory,” and while you may not be familiar with the term, you know the feeling:

“This was among the most moving stories I have ever read. When Chanel [Dasani’s mother] clasped her hands together in prayer in her new Harlem apartment, I wanted to burst in and celebrate with her and her kids,” wrote Nathaniel from West Orange, NJ, in an online comment on “Invisible Child.”

That’s transportation. Being so engaged in a story that it feels as if you inhabit that space and time, and feeling so connected to the characters that their joys and sorrows spark a physical reaction in you.

It’s a phenomenon that parallels what Ickes showed with his stranger experiment. Scientific findings suggest that when we read and experience characters in a story, the brain processes our understanding of those unknown others similarly to how we understand real, physical others.

Familiar faces  Chanel says she and Dasani are still approached by supportive strangers on the street who recognize them from the “Invisible Child” series. (Ruth Fremson / The New York Times / Redux)

Familiar faces

The more transported you feel, the more likely you’ll be to change your opinions and beliefs about the real world, psychologists Melanie C. Green and Timothy C. Brock write in The Role of Transportation in the Persuasiveness of Public Narratives . That feeling can be so strong that it leads to altered behavior, such as giving a $100 bill to a family of strangers. David Comer Kidd and Emanuele Castano even suggest that reading narratives make us more empathetic overall, because stories force us to engage in intense perspective taking.

What we intuitively believe to be true about how journalistic stories can spark empathy among readers is backed up by science. The question we’re left with is whether that spark may diminish as our culture turns toward digital.


A middle-aged woman reaches for the handle of her front door. Her body is rigid and her limbs protrude at sharp angles. Her wheelchair lurches and stops in strange syncopation. Her movements are typical of someone with cerebral palsy.

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You can see this in the example below, which shows a small section of code from one of the Met Office Hadley Centre models. The code contains commands such as “IF”, “THEN” and “DO”. When the model is run, it is first translated (automatically) into machine code that the computer understands.

A section of code from HadGEM2-ES (as used for CMIP5) in Fortran programming language. The code is from within the plant physiology section that starts to look at how the different vegetation types absorb light and moisture. Credit: Dr Chris Jones, Met Office Hadley Centre

There are now many other programming languages available to climate scientists, such as , Python , , Matlab and . However, the last four of these are applications that are themselves written in a more fundamental language (such as Fortran) and, therefore, are relatively slow to run. Fortran and C are generally used today for running a global model quickly on a supercomputer.

Spatial resolution

Throughout the code in a climate model are equations that govern the underlying physics of the climate system, from how sea ice forms and melts on Arctic waters to the exchange of gases and moisture between the land surface and the air above it.

The figure below shows how more and more climate processes have been incorporated into global models over the decades, from the mid-1970s through to the fourth assessment report (“AR4”) of the Intergovernmental Panel of Climate Change (IPCC), published in 2007.

Illustration of the processes added to global climate models over the decades, from the mid-1970s, through the first four IPCC assessment reports: first (“FAR”) published in 1990, second (“SAR”) in 1995, third (“TAR”) in 2001 and fourth (“AR4”) in 2007. (Note, there is also a fifth report, which was completed in 2014). Source: IPCC AR4, Fig 1.2

So, how does a model go about calculating all these equations?

Because of the complexity of the climate system and limitation of computing power, a model cannot possibly calculate all of these processes for every cubic metre of the climate system. Instead, a climate model divides up the Earth into a series of boxes or “grid cells”. A global model can have dozens of layers across the height and depth of the atmosphere and oceans.

The image below shows a 3D representation of what this looks like. The model then calculates the state of the climate system in each cell – factoring in temperature, air pressure, humidity and wind speed.

Illustration of grid cells used by climate models and the climatic processes that the model will calculate for each cell (bottom corner). Source: NOAA GFDL

For processes that happen on scales that are smaller than the grid cell, such as convection, the model uses “parameterisations” to fill in these gaps. These are essentially approximations that simplify each process and allow them to be included in the model. (Parameterisation is covered in the question on model tuning below .)

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