the quick-reference guide to the ngsswillard the nsta quick-reference guide to the ngss k–12...

Willa

rdThe

NSTA

Quic

k-Refe

renc

e G

uide

to the

NG

SS K–12

Sampson et al.Lab Investigations for Grades 9–12

K–12

The Quick-Reference Guide to the

NGSS

Edited by Ted WillardCopyright © 2015 NSTA. All rights reserved. For more information, go to www.nsta.org/permissions.

TO PURCHASE THIS BOOK, please visit www.nsta.org/store/product_detail.aspx?id=10.2505/9781941316108.

K–12


NGSS

Copyright © 2015 NSTA. All rights reserved. For more information, go to www.nsta.org/permissions. TO PURCHASE THIS BOOK, please visit www.nsta.org/store/product_detail.aspx?id=10.2505/9781941316108.

Arlington, Virginia


K–12


NGSS

Arlington, Virginia

Edited by Ted Willard


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Art And design Will Thomas Jr., Director Cover design by Gina Toole Saunders

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Copyright © 2015 by the National Science Teachers Association.All rights reserved. Printed in the United States of America.18 17 16 15 4 3 2 1

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Library of Congress Cataloging-in-Publication DataWillard, Ted. The NSTA quick-reference guide to the NGSS, K–12 / edited by Ted Willard. pages cm ISBN 978-1-941316-10-8—ISBN 978-1-941316-90-0 (electronic) 1. Science—Study and teaching (Elementary)—Standards—United States. 2. Science—Study and teaching (Secondary)—Standards--United States. I. National Science Teachers Association. II. Title. LB1585.3.W595 2014 507.1—dc23 2014033833Cataloging-in-Publication Data for the e-book are also available from the Library of Congress.

This book contains excerpts from National Research Council (NRC). 2012. A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press. Reprinted with permission.

The Next Generation Science Standards (“NGSS”) were developed by twenty-six states, in collaboration with the National Research Council, the National Science Teachers Association, and the American Association for the Advancement of Science in a process managed by Achieve, Inc. The NGSS are copyright © 2013 Achieve, Inc. All rights reserved.


CONTENTS

Introduction .................................................................................... ix

Acknowledgments ......................................................................... xi

Chapter 1: Basics of NGSS ............................................................ 1• Three Dimensions of the Next Generation Science Standards (NGSS) ............................ 2

• Science and Engineering Practices .................................................................................. 4

1. Asking Questions and Defining Problems..................................................................... 4

2. Developing and Using Models ...................................................................................... 6

3. Planning and Carrying Out Investigations .................................................................... 9

4. Analyzing and Interpreting Data ................................................................................. 11

5. Using Mathematics and Computational Thinking ....................................................... 13

6. Constructing Explanations and Designing Solutions .................................................. 16

7. Engaging in Argument From Evidence ....................................................................... 20

8. Obtaining, Evaluating, and Communicating Information ............................................ 23

• Crosscutting Concepts .................................................................................................... 26

1. Patterns ....................................................................................................................... 26

2. Cause and Effect: Mechanism and Prediction ............................................................ 28

3. Scale, Proportion, and Quantity .................................................................................. 30

4. Systems and System Models ...................................................................................... 32

5. Energy and Matter: Flows, Cycles, and Conservation ................................................ 35

6. Structure and Function ................................................................................................ 37

7. Stability and Change ................................................................................................... 39

• A Look at the Next Generation Science Standards ......................................................... 42

• Inside the NGSS Box ....................................................................................................... 43

• NGSS Organized by Topic .............................................................................................. 44

• NGSS Organized by Disciplinary Core Ideas ................................................................. 45

• Commonalities Among the Practices in Science, Mathematics, and English Language Arts (ELA) .......................................................................................... 46

Chapter 2: K–12 Progressions ..................................................... 49• Science and Engineering Practices ................................................................................ 50

• Crosscutting Concepts .................................................................................................... 58


• Disciplinary Core Ideas in Physical Science ................................................................... 61

• Disciplinary Core Ideas in Life Science ........................................................................... 68

• Disciplinary Core Ideas in Earth and Space Science ..................................................... 75

• Disciplinary Core Ideas in Engineering Design ............................................................... 80

• Connections to the Nature of Science ............................................................................. 82

• Connections to Engineering, Technology, and Applications of Science ......................... 85

Chapter 3: Focus on Grades K–2 ................................................ 87• Science and Engineering Practices ................................................................................ 88

• Crosscutting Concepts and Connections to Engineering, Technology, and Applications of Science ................................................................................................... 90

• Connections to the Nature of Science ............................................................................. 91

• Performance Expectations and Disciplinary Core Ideas for Kindergarten .......................... 92

• Performance Expectations and Disciplinary Core Ideas for Grade 1 ............................. 94

• Performance Expectations and Disciplinary Core Ideas for Grade 2 ............................. 96

• Performance Expectations and Disciplinary Core Ideas for Engineering Design in Grades K–2 ...................................................................................................................... 98

Chapter 4: Focus on Grades 3–5 ................................................. 99• Science and Engineering Practices .............................................................................. 100

• Crosscutting Concepts and Connections to Engineering, Technology, and Applications of Science ................................................................................................ 102

• Connections to the Nature of Science ........................................................................... 103

• Performance Expectations and Disciplinary Core Ideas for Grade 3 ........................... 104



• Performance Expectations and Disciplinary Core Ideas for Engineering Design in Grades 3–5 .................................................................................................................... 114

Chapter 5: Focus on Middle School ........................................... 115• Science and Engineering Practices .............................................................................. 116

• Crosscutting Concepts and Connections to Engineering, Technology, and Applications of Science ................................................................................................. 119


CONTENTS


• Performance Expectations and Disciplinary Core Ideas for Physical Science ............. 122

• Performance Expectations and Disciplinary Core Ideas for Life Science .................... 128

• Performance Expectations and Disciplinary Core Ideas for Earth and Space Science .............................................................................................. 133

• Performance Expectations and Disciplinary Core Ideas for Engineering Design ........ 138

Chapter 6: Focus on High School ............................................. 139• Science and Engineering Practices .............................................................................. 140

• Crosscutting Concepts and Connections to Engineering, Technology, and Applications of Science ................................................................................................. 143


• Performance Expectations and Disciplinary Core Ideas for Physical Science ............. 147

• Performance Expectations and Disciplinary Core Ideas for Life Science .................... 154

• Performance Expectations and Disciplinary Core Ideas for Earth and Space Science .............................................................................................. 161

• Performance Expectations and Disciplinary Core Ideas for Engineering Design ........ 167


ixThe NSTA Quick-Reference Guide to the NGSS, K–12

INTRODUCTION

Since the release of the first draft of the Next Generation Science Standards (NGSS), NSTA has been at the forefront in promoting the standards and helping science educators become familiar with and learn to navigate this exciting but complex document. When the final version was released and states began adopting the standards, NSTA started to develop resources that would assist educators with their implementation, including web seminars, virtual conferences, sessions and forums at conferences, books, and the NGSS@NSTA Hub (http://ngss.nsta.org)—a digital destination focus-ing on all things NGSS.

Along the way, NSTA learned that even the simplest of resources, such as a one-page cheat sheet, can be extremely useful. Many of those tools are collected in this volume, including

• a two-page cheat sheet that describes the practices, core ideas, and crosscut-ting concepts that make up the three dimensions described in A Framework for K–12 Science Education;

• an “Inside the NGSS Box” graphic that explains all of the individual sections of text that appear on a page of the NGSS;

• a Venn diagram comparing the practices in NGSS and Common Core State Standards in English language arts and mathematics; and

• matrixes showing how the NGSS are organized by topic and by disciplinary core idea.

We’ve also produced tables to describe the various parts of the standards. For example, the performance expectations describe what every student should know and be able to do by the end of a particular grade or grade span. These expectations are designed to assess the material in the foundation box, which includes

• science and engineering practices;

• disciplinary core ideas;

• crosscutting concepts;

• connections to engineering, technology, and applications of science; and

• connections to nature of science.

While summative assessments are required to focus on a particular combination of these components, curriculum developers and classroom teachers have the free-dom to mix and match these components in a wide variety of ways. In fact, to learn any particular disciplinary core idea or crosscutting concept, students will need to engage in multiple practices in a well-thought-out sequence of learning experiences. The matrixes we developed and include in this book will help educators in their


x National Science Teachers Association

INTRODUCTION

planning. There are two different sets of matrixes. The first set shows the K–12 pro-gression of each of the components in the foundation box (e.g., practices, core ideas, or connection to nature of science). These matrixes will help you understand how what students are expected to know and do in each grade span builds on what they have learned in earlier grades and prepares them for what they are expected to learn in later grades.

The second set of matrixes combines all the materials for a particular grade level together. For example, one of the matrixes focuses only on the science and engineer-ing practices that students need to master in grades K–2.

The materials in this book should be a useful companion to the NGSS. The educa-tors we have shared them with have found it helpful to photocopy particular pages for participants to use in a workshop or for colleagues to use during planning time.


xiThe NSTA Quick-Reference Guide to the NGSS, K–12

ACKNOWLEDGMENTS

Production of A Framework for K–12 Science Education and the Next Generation Science Standards involved the work and contributions of thousands of educators, and I thank them for their efforts. Almost every word in this publication is drawn directly from those two documents, but any errors that appear here are mine. In addition I want to thank those educators involved in developing the documents that preceded NGSS, including the Atlas of Science Literacy, National Science Education Standards, Benchmarks for Science Literacy, and Science for All Americans. Finally, I thank the many educators working to make the vision of science literacy for all a reality for their students. This book is for you.


CHAPTER 2K–12 Progressions


50 National Science Teachers Association

Chapter 2Sc

ienc

e a

nd E

ngin

eerin

g P

ract

ices

: Ask

ing

Que

stio

ns a

nd D

efin

ing

Pro

ble

ms

A p

ract

ice

of s

cien

ce is

to a

sk a

nd r

efine

que

stio

ns th

at le

ad to

des

crip

tions

and

exp

lana

tions

of h

ow th

e na

tura

l and

des

igne

d w

orld

wor

ks a

nd w

hich

can

be

empi

rical

ly te

sted

. Eng

inee

ring

ques

tions

cla

rify

prob

lem

s to

det

erm

ine

crite

ria fo

r su

cces

sful

sol

utio

ns a

nd id

entif

y co

nstr

aint

s to

sol

ve p

robl

ems

abou

t the

de

sign

ed w

orld

. Bot

h sc

ient

ists

and

eng

inee

rs a

lso

ask

ques

tions

to c

larif

y id

eas.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Ask

ing

ques

tions

and

defi

ning

pro

blem

s in

K–2

bui

lds

on p

rior

expe

rienc

es a

nd

prog

ress

es to

sim

ple

desc

riptiv

e qu

estio

ns

that

can

be

test

ed.

Ask

ing

ques

tions

and

defi

ning

pro

blem

s in

3–5

bui

lds

on K

–2 e

xper

ienc

es a

nd

prog

ress

es to

spe

cify

ing

qual

itativ

e re

latio

nshi

ps.

Ask

ing

ques

tions

and

defi

ning

pro

blem

s in

6–8

bui

lds

on K

–5 e

xper

ienc

es a

nd

prog

ress

es to

spe

cify

ing

rela

tions

hips

be

twee

n va

riabl

es, c

larif

ying

arg

umen

ts

and

mak

ing

mod

els.

Ask

ing

ques

tions

and

defi

ning

pro

blem

s in

9–1

2 bu

ilds

on K

–8 e

xper

ienc

es a

nd

prog

ress

es to

form

ulat

ing,

refi

ning

, and

ev

alua

ting

empi

rical

ly te

stab

le q

uest

ions

an

d de

sign

pro

blem

s us

ing

mod

els

and

sim

ulat

ions

.

• A

sk q

uest

ions

bas

ed o

n ob

serv

atio

ns to

fin

d m

ore

info

rmat

ion

abou

t the

nat

ural

an

d/or

des

igne

d w

orld

(s).

• A

sk q

uest

ions

abo

ut w

hat w

ould

hap

pen

if a

varia

ble

is c

hang

ed.

• A

sk q

uest

ions

that

aris

e fr

om c

aref

ul

obse

rvat

ion

of p

heno

men

a, m

odel

s, o

r un

expe

cted

res

ults

, to

clar

ify a

nd/o

r se

ek

addi

tiona

l inf

orm

atio

n.

• A

sk q

uest

ions

to id

entif

y an

d/or

cla

rify

evid

ence

and

/or

the

prem

ise(

s) o

f an

argu

men

t.

• A

sk q

uest

ions

to d

eter

min

e re

latio

nshi

ps

betw

een

inde

pend

ent a

nd d

epen

dent

va

riabl

es a

nd r

elat

ions

hips

in m

odel

s.

• A

sk q

uest

ions

to c

larif

y an

d/or

refi

ne a

m

odel

, an

expl

anat

ion,

or

an e

ngin

eerin

g pr

oble

m.

• A

sk q

uest

ions

that

aris

e fr

om c

aref

ul

obse

rvat

ion

of p

heno

men

a, o

r un

expe

cted

res

ults

, to

clar

ify a

nd/o

r se

ek

addi

tiona

l inf

orm

atio

n.

• A

sk q

uest

ions

that

aris

e fr

om e

xam

inin

g m

odel

s or

a th

eory

, to

clar

ify a

nd/o

r se

ek

addi

tiona

l inf

orm

atio

n an

d re

latio

nshi

ps.

• A

sk q

uest

ions

to d

eter

min

e re

latio

nshi

ps, i

nclu

ding

qua

ntita

tive

rela

tions

hips

, bet

wee

n in

depe

nden

t and

de

pend

ent v

aria

bles

.

• A

sk q

uest

ions

to c

larif

y an

d re

fine

a m

odel

, an

expl

anat

ion,

or

an e

ngin

eerin

g pr

oble

m.

• A

sk a

nd/o

r id

entif

y qu

estio

ns th

at c

an b

e an

swer

ed b

y an

inve

stig

atio

n.

• Id

entif

y sc

ient

ific

(tes

tabl

e) a

nd n

on-

scie

ntifi

c (n

on-t

esta

ble)

que

stio

ns.

• A

sk q

uest

ions

that

can

be

inve

stig

ated

an

d pr

edic

t rea

sona

ble

outc

omes

bas

ed

on p

atte

rns

such

as

caus

e-an

d-ef

fect

re

latio

nshi

ps.

• A

sk q

uest

ions

that

requ

ire s

uffic

ient

and

ap

prop

riate

em

piric

al e

vide

nce

to a

nsw

er.

• A

sk q

uest

ions

that

can

be

inve

stig

ated

w

ithin

the

scop

e of

the

clas

sroo

m,

outd

oor

envi

ronm

ent,

and

mus

eum

s an

d ot

her

publ

ic fa

cilit

ies

with

ava

ilabl

e re

sour

ces

and,

whe

n ap

prop

riate

, fra

me

a hy

poth

esis

bas

ed o

n ob

serv

atio

ns a

nd

scie

ntifi

c pr

inci

ples

.

• E

valu

ate

a qu

estio

n to

det

erm

ine

if it

is

test

able

and

rel

evan

t.

• A

sk q

uest

ions

that

can

be

inve

stig

ated

w

ithin

the

scop

e of

the

scho

ol la

bora

tory

, re

sear

ch fa

cilit

ies,

or

field

(e.

g., o

utdo

or

envi

ronm

ent)

with

ava

ilabl

e re

sour

ces

and,

whe

n ap

prop

riate

, fra

me

a hy

poth

esis

bas

ed o

n a

mod

el o

r th

eory

.

• N

/A•

N/A

• A

sk q

uest

ions

that

cha

lleng

e th

e pr

emis

e(s)

of a

n ar

gum

ent o

r th

e in

terp

reta

tion

of a

dat

a se

t.

• A

sk a

nd/o

r ev

alua

te q

uest

ions

that

ch

alle

nge

the

prem

ise(

s) o

f an

argu

men

t, th

e in

terp

reta

tion

of a

dat

a se

t, or

the

suita

bilit

y of

the

desi

gn.

• D

efine

a s

impl

e pr

oble

m th

at c

an b

e so

lved

thro

ugh

the

deve

lopm

ent o

f a

new

or

impr

oved

obj

ect o

r to

ol.

• U

se p

rior

know

ledg

e to

des

crib

e pr

oble

ms

that

can

be

solv

ed.

• D

efine

a s

impl

e de

sign

pro

blem

that

can

be

sol

ved

thro

ugh

the

deve

lopm

ent o

f an

obj

ect,

tool

, pro

cess

, or

syst

em a

nd

incl

udes

sev

eral

crit

eria

for

succ

ess

and

cons

trai

nts

on m

ater

ials

, tim

e, o

r co

st.

• D

efine

a d

esig

n pr

oble

m th

at c

an b

e so

lved

thro

ugh

the

deve

lopm

ent o

f an

obje

ct, t

ool,

proc

ess,

or

syst

em a

nd

incl

udes

mul

tiple

crit

eria

and

con

stra

ints

, in

clud

ing

scie

ntifi

c kn

owle

dge

that

may

lim

it po

ssib

le s

olut

ions

.

• D

efine

a d

esig

n pr

oble

m th

at in

volv

es

the

deve

lopm

ent o

f a p

roce

ss o

r sy

stem

w

ith in

tera

ctin

g co

mpo

nent

s an

d cr

iteria

and

con

stra

ints

that

may

incl

ude

soci

al, t

echn

ical

, and

/or

envi

ronm

enta

l co

nsid

erat

ions

.

N/A

= N

ot a

pplic

able

for

this

gra

de r

ange


51The NSTA Quick-Reference Guide to the NGSS, K–12

K–12 Progressions

Scie

nce

and

Eng

inee

ring

Pra

ctic

es: D

evel

opin

g a

nd U

sing

Mod

els

A p

ract

ice

of b

oth

scie

nce

and

engi

neer

ing

is to

use

and

con

stru

ct m

odel

s as

hel

pful

tool

s fo

r re

pres

entin

g id

eas

and

expl

anat

ions

. The

se to

ols

incl

ude

diag

ram

s,

draw

ings

, phy

sica

l rep

licas

, mat

hem

atic

al r

epre

sent

atio

ns, a

nalo

gies

, and

com

pute

r si

mul

atio

ns. M

odel

ing

tool

s ar

e us

ed to

dev

elop

que

stio

ns, p

redi

ctio

ns, a

nd

expl

anat

ions

; ana

lyze

and

iden

tify

flaw

s in

sys

tem

s; a

nd c

omm

unic

ate

idea

s. M

odel

s ar

e us

ed to

bui

ld a

nd r

evis

e sc

ient

ific

expl

anat

ions

and

pro

pose

d en

gine

ered

sy

stem

s. M

easu

rem

ents

and

obs

erva

tions

are

use

d to

rev

ise

mod

els

and

desi

gns.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Mod

elin

g in

K–2

bui

lds

on p

rior

expe

rienc

es

and

prog

ress

es to

incl

ude

usin

g an

d de

velo

ping

mod

els

(e.g

., di

agra

m, d

raw

ing,

ph

ysic

al r

eplic

a, d

iora

ma,

dra

mat

izat

ion,

or

stor

yboa

rd)

that

rep

rese

nt c

oncr

ete

even

ts

or d

esig

n so

lutio

ns.

Mod

elin

g in

3–5

bui

lds

on K

–2 e

xper

ienc

es

and

prog

ress

es to

bui

ldin

g an

d re

visi

ng

sim

ple

mod

els

and

usin

g m

odel

s to

re

pres

ent e

vent

s an

d de

sign

sol

utio

ns.

Mod

elin

g in

6–8

bui

lds

on K

–5 e

xper

ienc

es

and

prog

ress

es to

dev

elop

ing,

usi

ng, a

nd

revi

sing

mod

els

to d

escr

ibe,

test

, and

pr

edic

t mor

e ab

stra

ct p

heno

men

a an

d de

sign

sys

tem

s.

Mod

elin

g in

9–1

2 bu

ilds

on K

–8

expe

rienc

es a

nd p

rogr

esse

s to

usi

ng,

synt

hesi

zing

, and

dev

elop

ing

mod

els

to

pred

ict a

nd s

how

rel

atio

nshi

ps a

mon

g va

riabl

es b

etw

een

syst

ems

and

thei

r co

mpo

nent

s in

the

natu

ral a

nd d

esig

ned

wor

ld(s

).

• D

istin

guis

h be

twee

n a

mod

el a

nd th

e ac

tual

obj

ect,

proc

ess,

and

/or

even

ts th

e m

odel

rep

rese

nts.

• C

ompa

re m

odel

s to

iden

tify

com

mon

fe

atur

es a

nd d

iffer

ence

s.

• Id

entif

y lim

itatio

ns o

f mod

els .

•

Eva

luat

e lim

itatio

ns o

f a m

odel

for

a pr

opos

ed o

bjec

t or

tool

. •

Eva

luat

e m

erits

and

lim

itatio

ns o

f tw

o di

ffere

nt m

odel

s of

the

sam

e pr

opos

ed

tool

, pro

cess

, mec

hani

sm, o

r sy

stem

in

orde

r to

sel

ect o

r re

vise

a m

odel

that

be

st fi

ts th

e ev

iden

ce o

r de

sign

crit

eria

.

• D

esig

n a

test

of a

mod

el to

asc

erta

in it

s re

liabi

lity.

• D

evel

op a

nd/o

r us

e a

mod

el to

rep

rese

nt

amou

nts,

rel

atio

nshi

ps, r

elat

ive

scal

es

(big

ger,

smal

ler)

, and

/or

patte

rns

in th

e na

tura

l and

des

igne

d w

orld

(s).

• C

olla

bora

tivel

y de

velo

p an

d/or

rev

ise

a m

odel

bas

ed o

n ev

iden

ce th

at s

how

s th

e re

latio

nshi

ps a

mon

g va

riabl

es fo

r fr

eque

nt a

nd r

egul

ar o

ccur

ring

even

ts.

• D

evel

op a

mod

el u

sing

an

anal

ogy,

ex

ampl

e, o

r ab

stra

ct r

epre

sent

atio

n to

de

scrib

e a

scie

ntifi

c pr

inci

ple

or d

esig

n so

lutio

n.

• D

evel

op a

nd/o

r us

e m

odel

s to

des

crib

e an

d/or

pre

dict

phe

nom

ena.

• D

evel

op o

r m

odify

a m

odel

—ba

sed

on

evid

ence

—to

mat

ch w

hat h

appe

ns if

a

varia

ble

or c

ompo

nent

of a

sys

tem

is

chan

ged.

• U

se a

nd/o

r de

v elo

p a

mod

el o

f sim

ple

syst

ems

with

unc

erta

in a

nd le

ss

pred

icta

ble

fact

ors.

• D

evel

op a

nd/o

r re

vise

a m

odel

to s

how

th

e re

latio

nshi

ps a

mon

g va

riabl

es,

incl

udin

g th

ose

that

are

not

obs

erva

ble

but p

redi

ct o

bser

vabl

e ph

enom

ena.

• D

evel

op a

nd/o

r us

e a

mod

el to

pre

dict

an

d/or

des

crib

e ph

enom

ena.

• D

evel

op a

mod

el to

des

crib

e un

obse

rvab

le m

echa

nism

s.

• D

evel

op, r

evis

e, a

nd/o

r us

e a

mod

el

base

d on

evi

denc

e to

illu

stra

te a

nd/

or p

redi

ct th

e re

latio

nshi

ps b

etw

een

syst

ems

or b

etw

een

com

pone

nts

of a

sy

stem

.

• D

evel

op a

nd/o

r us

e m

ultip

le ty

pes

of

mod

els

to p

rovi

de m

echa

nist

ic a

ccou

nts

and/

or p

redi

ct p

heno

men

a, a

nd m

ove

flexi

bly

betw

een

mod

el ty

pes

base

d on

m

erits

and

lim

itatio

ns.

• D

evel

op a

sim

ple

mod

el b

ased

on

evid

ence

to r

epre

sent

a p

ropo

sed

obje

ct

or to

ol.

• D

evel

op a

dia

gram

or

sim

ple

phys

ical

pr

otot

ype

to c

onve

y a

prop

osed

obj

ect,

tool

, or

proc

ess.

• U

se a

mod

el to

test

cau

se-a

nd-e

ffect

re

latio

nshi

ps o

r in

tera

ctio

ns c

once

rnin

g th

e fu

nctio

ning

of a

nat

ural

or

desi

gned

sy

stem

.

• D

evel

op a

nd/o

r us

e a

mod

el to

gen

erat

e da

ta to

test

idea

s ab

out p

heno

men

a in

na

tura

l or

desi

gned

sys

tem

s, in

clud

ing

thos

e re

pres

entin

g in

puts

and

out

puts

, an

d th

ose

at u

nobs

erva

ble

scal

es.

• D

evel

op a

com

plex

mod

el th

at a

llow

s fo

r m

anip

ulat

ion

and

test

ing

of a

pro

pose

d pr

oces

s or

sys

tem

.

• D

evel

op a

nd/o

r us

e a

mod

el (

incl

udin

g m

athe

mat

ical

and

com

puta

tiona

l) to

ge

nera

te d

ata

to s

uppo

rt e

xpla

natio

ns,

pred

ict p

heno

men

a, a

naly

ze s

yste

ms,

an

d/or

sol

ve p

robl

ems.



Chapter 2Sc

ienc

e a

nd E

ngin

eerin

g P

ract

ices

: Pla

nnin

g a

nd C

arry

ing

Out

Inve

stig

atio

ns

Sci

entis

ts a

nd e

ngin

eers

pla

n an

d ca

rry

out i

nves

tigat

ions

in th

e fie

ld o

r la

bora

tory

, wor

king

col

labo

rativ

ely

as w

ell a

s in

divi

dual

ly. T

heir

inve

stig

atio

ns a

re s

yste

mat

ic

and

requ

ire c

larif

ying

wha

t cou

nts

as d

ata

and

iden

tifyi

ng v

aria

bles

or

para

met

ers.

Eng

inee

ring

inve

stig

atio

ns id

entif

y th

e ef

fect

iven

ess,

effi

cien

cy, a

nd d

urab

ility

of

desi

gns

unde

r di

ffere

nt c

ondi

tions

.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Pla

nnin

g an

d ca

rryi

ng o

ut in

vest

igat

ions

to

ans

wer

que

stio

ns o

r te

st s

olut

ions

to

pro

blem

s in

K–2

bui

lds

on p

rior

expe

rienc

es a

nd p

rogr

esse

s to

sim

ple

inve

stig

atio

ns, b

ased

on

fair

test

s, w

hich

pr

ovid

e da

ta to

sup

port

exp

lana

tions

or

desi

gn s

olut

ions

.

Pla

nnin

g an

d ca

rryi

ng o

ut in

vest

igat

ions

to

ans

wer

que

stio

ns o

r te

st s

olut

ions

to

prob

lem

s in

3–5

bui

lds

on K

–2 e

xper

ienc

es

and

prog

ress

es to

incl

ude

inve

stig

atio

ns

that

con

trol

var

iabl

es a

nd p

rovi

de e

vide

nce

to s

uppo

rt e

xpla

natio

ns o

r de

sign

sol

utio

ns.

Pla

nnin

g an

d ca

rryi

ng o

ut in

vest

igat

ions

in

6–8

bui

lds

on K

–5 e

xper

ienc

es a

nd

prog

ress

es to

incl

ude

inve

stig

atio

ns

that

use

mul

tiple

var

iabl

es a

nd p

rovi

de

evid

ence

to s

uppo

rt e

xpla

natio

ns o

r so

lutio

ns.

Pla

nnin

g an

d ca

rryi

ng o

ut in

vest

igat

ions

in

9–1

2 bu

ilds

on K

–8 e

xper

ienc

es a

nd

prog

ress

es to

incl

ude

inve

stig

atio

ns th

at

prov

ide

evid

ence

for

and

test

con

cept

ual,

mat

hem

atic

al, p

hysi

cal,

and

empi

rical

m

odel

s.

• W

ith g

uida

nce,

pla

n an

d co

nduc

t an

inve

stig

atio

n in

col

labo

ratio

n w

ith p

eers

(f

or K

).

• P

lan

and

cond

uct a

n in

vest

igat

ion

colla

bora

tivel

y to

pro

duce

dat

a to

ser

ve

as th

e ba

sis

for

evid

ence

to a

nsw

er a

qu

estio

n.

• P

lan

and

cond

uct a

n in

vest

igat

ion

colla

bora

tivel

y to

pro

duce

dat

a to

ser

ve

as th

e ba

sis

for

evid

ence

, usi

ng fa

ir te

sts

in w

hich

var

iabl

es a

re c

ontr

olle

d an

d th

e nu

mbe

r of

tria

ls c

onsi

dere

d.

• P

lan

an in

vest

igat

ion

indi

vidu

ally

and

co

llabo

rativ

ely,

and

in th

e de

sign

iden

tify

inde

pend

ent a

nd d

epen

dent

var

iabl

es

and

cont

rols

, wha

t too

ls a

re n

eede

d to

do

the

gath

erin

g, h

ow m

easu

rem

ents

w

ill b

e re

cord

ed, a

nd h

ow m

any

data

are

ne

eded

to s

uppo

rt a

cla

im.

• C

ondu

ct a

n in

vest

igat

ion

and/

or e

valu

ate

and/

or r

evis

e th

e ex

perim

enta

l des

ign

to p

rodu

ce d

ata

to s

erve

as

the

basi

s fo

r ev

iden

ce th

at m

eet t

he g

oals

of t

he

inve

stig

atio

n.

• P

lan

an in

vest

igat

ion

or te

st a

des

ign

indi

vidu

ally

and

col

labo

rativ

ely

to p

rodu

ce

data

to s

erve

as

the

basi

s fo

r ev

iden

ce

as p

art o

f bui

ldin

g an

d re

visi

ng m

odel

s,

supp

ortin

g ex

plan

atio

ns fo

r ph

enom

ena,

or

test

ing

solu

tions

to p

robl

ems.

Con

side

r po

ssib

le v

aria

bles

or

effe

cts

and

eval

uate

th

e co

nfou

ndin

g in

vest

igat

ion’

s de

sign

to

ensu

re v

aria

bles

are

con

trolle

d.

• P

lan

and

cond

uct a

n in

vest

igat

ion

indi

vidu

ally

and

col

labo

rativ

ely

to

prod

uce

data

to s

erve

as

the

basi

s fo

r ev

iden

ce, a

nd in

the

desi

gn d

ecid

e on

type

s, h

ow m

uch,

and

acc

urac

y of

dat

a ne

eded

to p

rodu

ce r

elia

ble

mea

sure

men

ts a

nd c

onsi

der

limita

tions

on

the

prec

isio

n of

the

data

(e.

g.,

num

ber

of tr

ials

, cos

t, ris

k, ti

me)

; refi

ne

the

desi

gn a

ccor

ding

ly.

• P

lan

and

cond

uct a

n in

vest

igat

ion

or

test

a d

esig

n so

lutio

n in

a s

afe

and

ethi

cal m

anne

r in

clud

ing

cons

ider

atio

ns

of e

nviro

nmen

tal,

soci

al, a

nd p

erso

nal

impa

cts.

• E

valu

ate

diffe

rent

way

s of

obs

ervi

ng

and/

or m

easu

ring

a ph

enom

enon

to

dete

rmin

e w

hich

way

can

ans

wer

a

ques

tion.

• E

valu

ate

appr

opria

te m

etho

ds a

nd/o

r to

ols

for

colle

ctin

g da

ta.

• E

valu

ate

the

accu

racy

of v

ario

us

met

hods

for

colle

ctin

g da

ta.

• S

elec

t app

ropr

iate

tool

s to

col

lect

, re

cord

, ana

lyze

, and

eva

luat

e da

ta.

• M

ake

obse

rvat

ions

(fir

stha

nd o

r fr

om

med

ia)

and/

or m

easu

rem

ents

to

colle

ct d

ata

that

can

be

used

to m

ake

com

paris

ons.

• M

ake

obse

rvat

ions

(fir

stha

nd o

r fr

om

med

ia)

and/

or m

easu

rem

ents

of a

pr

opos

ed o

bjec

t or

tool

or

solu

tion

to

dete

rmin

e if

it so

lves

a p

robl

em o

r m

eets

a

goal

.

• M

ake

pred

ictio

ns b

ased

on

prio

r ex

perie

nces

.

• M

ake

obse

rvat

ions

and

/or

mea

sure

men

ts

to p

rodu

ce d

ata

to s

erve

as

the

basi

s fo

r ev

iden

ce fo

r an

exp

lana

tion

of a

ph

enom

enon

or

test

a d

esig

n so

lutio

n.

• M

ake

pred

ictio

ns a

bout

wha

t wou

ld

happ

en if

a v

aria

ble

chan

ges.

• Te

st t

wo

diffe

rent

mod

els

of th

e sa

me

prop

osed

obj

ect,

tool

, or

proc

ess

to

dete

rmin

e w

hich

bet

ter

mee

ts c

riter

ia fo

r su

cces

s.

• C

olle

ct a

nd p

rodu

ce d

ata

to s

erve

as

the

basi

s fo

r ev

iden

ce to

ans

wer

sci

entifi

c qu

estio

ns o

r te

st d

esig

n so

lutio

ns u

nder

a

rang

e of

con

ditio

ns.

• C

olle

ct d

ata

abou

t the

per

form

ance

of a

pr

opos

ed o

bjec

t, to

ol, p

roce

ss, o

r sy

stem

un

der

a ra

nge

of c

ondi

tions

.

• M

ake

dire

ctio

nal h

ypot

hese

s th

at

spec

ify w

hat h

appe

ns to

a d

epen

dent

va

riabl

e w

hen

an in

depe

nden

t var

iabl

e is

m

anip

ulat

ed.

• M

anip

ulat

e va

riabl

es a

nd c

olle

ct d

ata

abou

t a c

ompl

ex m

odel

of a

pro

pose

d pr

oces

s or

sys

tem

to id

entif

y fa

ilure

po

ints

or

impr

ove

perf

orm

ance

rel

ativ

e to

cr

iteria

for

succ

ess

or o

ther

var

iabl

es.



K–12 ProgressionsSc

ienc

e a

nd E

ngin

eerin

g P

ract

ices

: Ana

lyzin

g a

nd In

terp

retin

g D

ata

S

cien

tific

inve

stig

atio

ns p

rodu

ce d

ata

that

mus

t be

anal

yzed

to d

eriv

e m

eani

ng. B

ecau

se d

ata

patte

rns

and

tren

ds a

re n

ot a

lway

s ob

viou

s, s

cien

tists

use

a r

ange

of

tool

s—in

clud

ing

tabu

latio

n, g

raph

ical

inte

rpre

tatio

n, v

isua

lizat

ion,

and

sta

tistic

al a

naly

sis—

to id

entif

y th

e si

gnifi

cant

feat

ures

and

pat

tern

s in

the

data

. Sci

entis

ts

iden

tify

sour

ces

of e

rror

in th

e in

vest

igat

ions

and

cal

cula

te th

e de

gree

of c

erta

inty

in th

e re

sults

. Mod

ern

tech

nolo

gy m

akes

the

colle

ctio

n of

larg

e da

ta s

ets

muc

h ea

sier

, pro

vidi

ng s

econ

dary

sou

rces

for

anal

ysis

. Eng

inee

ring

inve

stig

atio

ns in

clud

e an

alys

is o

f dat

a co

llect

ed in

the

test

s of

des

igns

. Thi

s al

low

s co

mpa

rison

of

diff

eren

t sol

utio

ns a

nd d

eter

min

es h

ow w

ell e

ach

mee

ts s

peci

fic d

esig

n cr

iteria

—th

at is

, whi

ch d

esig

n be

st s

olve

s th

e pr

oble

m w

ithin

giv

en c

onst

rain

ts. L

ike

scie

ntis

ts, e

ngin

eers

req

uire

a r

ange

of t

ools

to id

entif

y pa

ttern

s w

ithin

dat

a an

d in

terp

ret t

he r

esul

ts. A

dvan

ces

in s

cien

ce m

ake

anal

ysis

of p

ropo

sed

solu

tions

m

ore

effic

ient

and

effe

ctiv

e.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Ana

lyzi

ng d

ata

in K

–2 b

uild

s on

prio

r ex

perie

nces

and

pro

gres

ses

to c

olle

ctin

g,

reco

rdin

g, a

nd s

harin

g ob

serv

atio

ns.

Ana

lyzi

ng d

ata

in 3

–5 b

uild

s on

K–2

ex

perie

nces

and

pro

gres

ses

to in

trod

ucin

g qu

antit

ativ

e ap

proa

ches

to c

olle

ctin

g da

ta

and

cond

uctin

g m

ultip

le tr

ials

of q

ualit

ativ

e ob

serv

atio

ns. W

hen

poss

ible

and

feas

ible

, di

gita

l too

ls s

houl

d be

use

d.

Ana

lyzi

ng d

ata

in 6

–8 b

uild

s on

K–5

ex

perie

nces

and

pro

gres

ses

to e

xten

ding

qu

antit

ativ

e an

alys

is to

inve

stig

atio

ns,

dist

ingu

ishi

ng b

etw

een

corr

elat

ion

and

caus

atio

n, a

nd b

asic

sta

tistic

al te

chni

ques

of

dat

a an

d er

ror

anal

ysis

.

Ana

lyzi

ng d

ata

in 9

–12

build

s on

K–8

ex

perie

nces

and

pro

gres

ses

to in

trod

ucin

g m

ore

deta

iled

stat

istic

al a

naly

sis,

the

com

paris

on o

f dat

a se

ts fo

r co

nsis

tenc

y,

and

the

use

of m

odel

s to

gen

erat

e an

d an

alyz

e da

ta.

• R

ecor

d in

form

atio

n (o

bser

vatio

ns,

thou

ghts

, and

idea

s).

• U

se o

bser

v atio

ns (

first

hand

or

from

m

edia

) to

des

crib

e pa

ttern

s an

d/or

re

latio

nshi

ps in

the

natu

ral a

nd d

esig

ned

wor

ld in

ord

er to

ans

wer

sci

entifi

c qu

estio

ns a

nd s

olve

pro

blem

s.

• C

ompa

re p

redi

ctio

ns (

base

d on

pr

ior

expe

rienc

es)

to w

hat o

ccur

red

(obs

erva

ble

even

ts).

• R

epr e

sent

dat

a in

tabl

es a

nd/o

r va

rious

gra

phic

al d

ispl

ays

(bar

gra

phs,

pi

ctog

raph

s, a

nd/o

r pi

e ch

arts

) to

rev

eal

patte

rns

that

indi

cate

rel

atio

nshi

ps.

• C

onst

r uct

, ana

lyze

, and

/or

inte

rpre

t gr

aphi

cal d

ispl

ays

of d

ata

and/

or la

rge

data

set

s to

iden

tify

linea

r an

d no

nlin

ear

rela

tions

hips

.

• U

se g

r aph

ical

dis

play

s (e

.g.,

map

s,

char

ts, g

raph

s, a

nd/o

r ta

bles

) of

larg

e da

ta s

ets

to id

entif

y te

mpo

ral a

nd s

patia

l re

latio

nshi

ps.

• D

istin

guis

h be

twee

n ca

usal

and

co

rrel

atio

nal r

elat

ions

hips

in d

ata.

• A

naly

ze a

nd in

terp

ret d

ata

to p

rovi

de

evid

ence

for

phen

omen

a.

• A

naly

z e d

ata

usin

g to

ols,

tech

nolo

gies

, an

d/or

mod

els

(e.g

., co

mpu

tatio

nal,

mat

hem

atic

al)

in o

rder

to m

ake

valid

and

re

liabl

e sc

ient

ific

clai

ms

or d

eter

min

e an

op

timal

des

ign

solu

tion.

• N

/A•

Ana

lyze

and

inte

rpre

t dat

a to

mak

e se

nse

of p

heno

men

a, u

sing

logi

cal

reas

onin

g, m

athe

mat

ics,

and

/or

com

puta

tion.

• A

pply

con

cept

s of

sta

tistic

s an

d pr

obab

ility

(in

clud

ing

mea

n, m

edia

n,

mod

e, a

nd v

aria

bilit

y) to

ana

lyze

and

ch

arac

teriz

e da

ta, u

sing

dig

ital t

ools

w

hen

feas

ible

.

• A

pply

con

cept

s of

sta

tistic

s an

d pr

obab

ility

(in

clud

ing

dete

rmin

ing

func

tion

fits

to d

ata,

slo

pe, i

nter

cept

, an

d co

rrel

atio

n co

effic

ient

for

linea

r fit

s)

to s

cien

tific

and

engi

neer

ing

ques

tions

an

d pr

oble

ms,

usi

ng d

igita

l too

ls w

hen

feas

ible

.

• N

/A•

N/A

• C

onsi

der

limita

tions

of d

ata

anal

ysis

(e

.g.,

mea

sure

men

t err

or)

and/

or s

eek

to im

prov

e pr

ecis

ion

and

accu

racy

of

data

with

bet

ter

tech

nolo

gica

l too

ls a

nd

met

hods

(e.

g., m

ultip

le tr

ials

).

• C

onsi

der

limita

tions

of d

ata

anal

ysis

(e

.g.,

mea

sure

men

t err

or, s

ampl

e se

lect

ion)

whe

n an

alyz

ing

and

inte

rpre

ting

data

.

• N

/A•

Com

pare

and

con

tras

t dat

a co

llect

ed

by d

iffer

ent g

roup

s in

ord

er to

dis

cuss

si

mila

ritie

s an

d di

ffere

nces

in th

eir

findi

ngs.

• A

naly

ze a

nd in

terp

ret d

ata

to d

eter

min

e si

mila

ritie

s an

d di

ffere

nces

in fi

ndin

gs.

• C

ompa

re a

nd c

ontr

ast v

ario

us ty

pes

of d

ata

sets

(e.

g., s

elf-

gene

rate

d,

arch

ival

) to

exa

min

e co

nsis

tenc

y of

m

easu

rem

ents

and

obs

erva

tions

.

• A

naly

ze d

ata

from

test

s of

an

obje

ct o

r to

ol to

det

erm

ine

if it

wor

ks a

s in

tend

ed.

• A

naly

ze d

ata

to r

efine

a p

robl

em

stat

emen

t or

the

desi

gn o

f a p

ropo

sed

obje

ct, t

ool,

or p

roce

ss.

• U

se d

ata

to e

valu

ate

and

refin

e de

sign

so

lutio

ns.

• A

naly

ze d

ata

to d

efine

an

optim

al

oper

atio

nal r

ange

for

a pr

opos

ed o

bjec

t, to

ol, p

roce

ss, o

r sy

stem

that

bes

t mee

ts

crite

ria fo

r su

cces

s.

• E

valu

ate

the

impa

ct o

f new

dat

a on

a

wor

king

exp

lana

tion

and/

or m

odel

of a

pr

opos

ed p

roce

ss o

r sy

stem

.

• A

naly

ze d

ata

to id

entif

y de

sign

feat

ures

or

cha

ract

eris

tics

of th

e co

mpo

nent

s of

a

prop

osed

pro

cess

or

syst

em to

opt

imiz

e it

rela

tive

to c

riter

ia fo

r su

cces

s.

N/A

= N

ot a

pplic

able

for

this

gra

de r

ange



Chapter 2

Scie

nce

and

Eng

ine

erin

g P

rac

tice

s: U

sing

Ma

the

ma

tics

and

C

om

put

atio

nal T

hink

ing

In

bot

h sc

ienc

e an

d en

gine

erin

g, m

athe

mat

ics

and

com

puta

tion

are

fund

amen

tal t

ools

for

repr

esen

ting

phys

ical

var

iabl

es a

nd th

eir

rela

tions

hips

. The

y ar

e us

ed fo

r a

rang

e of

task

s su

ch a

s co

nstr

uctin

g si

mul

atio

ns; s

olvi

ng e

quat

ions

exa

ctly

or

appr

oxim

atel

y; a

nd r

ecog

nizi

ng, e

xpre

ssin

g, a

nd a

pply

ing

quan

titat

ive

rela

tions

hips

. M

athe

mat

ical

and

com

puta

tiona

l app

roac

hes

enab

le s

cien

tists

and

eng

inee

rs to

pre

dict

the

beha

vior

of s

yste

ms

and

test

the

valid

ity o

f suc

h pr

edic

tions

.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Mat

hem

atic

al a

nd c

ompu

tatio

nal

thin

king

in K

–2 b

uild

s on

prio

r ex

perie

nce

and

prog

ress

es to

re

cogn

izin

g th

at m

athe

mat

ics

can

be u

sed

to d

escr

ibe

the

natu

ral a

nd

desi

gned

wor

ld(s

).

Mat

hem

atic

al a

nd c

ompu

tatio

nal

thin

king

in 3

–5 b

uild

s on

K–2

ex

perie

nces

and

pro

gres

ses

to

exte

ndin

g qu

antit

ativ

e m

easu

rem

ents

to

a v

arie

ty o

f phy

sica

l pro

pert

ies

and

usin

g co

mpu

tatio

n an

d m

athe

mat

ics

to

anal

yze

data

and

com

pare

alte

rnat

ive

desi

gn s

olut

ions

.

Mat

hem

atic

al a

nd c

ompu

tatio

nal

thin

king

in 6

–8 b

uild

s on

K–5

ex

perie

nces

and

pro

gres

ses

to

iden

tifyi

ng p

atte

rns

in la

rge

data

se

ts a

nd u

sing

mat

hem

atic

al

conc

epts

to s

uppo

rt e

xpla

natio

ns

and

argu

men

ts.

Mat

hem

atic

al a

nd c

ompu

tatio

nal t

hink

ing

in 9

–12

build

s on

K–8

exp

erie

nces

and

pro

gres

ses

to u

sing

alg

ebra

ic

thin

king

and

ana

lysi

s, a

ran

ge o

f lin

ear

and

nonl

inea

r fu

nctio

ns in

clud

ing

trig

onom

etric

func

tions

, exp

onen

tials

an

d lo

garit

hms,

and

com

puta

tiona

l too

ls fo

r st

atis

tical

an

alys

is to

ana

lyze

, rep

rese

nt, a

nd m

odel

dat

a. S

impl

e co

mpu

tatio

nal s

imul

atio

ns a

re c

reat

ed a

nd u

sed

base

d on

m

athe

mat

ical

mod

els

of b

asic

ass

umpt

ions

.

• N

/A•

N/A

• D

ecid

e w

hen

to u

se q

ualit

ativ

e vs

. qua

ntita

tive

data

. •

Dec

ide

if qu

alita

tive

or q

uant

itativ

e da

ta a

re b

est t

o de

term

ine

whe

ther

a p

ropo

sed

obje

ct o

r to

ol m

eets

cr

iteria

for

succ

ess.

• U

se c

ount

ing

and

num

bers

to id

entif

y an

d de

scrib

e pa

ttern

s in

the

natu

ral

and

desi

gned

wor

ld(s

).

• O

rgan

ize

sim

ple

data

set

s to

rev

eal

patte

rns

that

sug

gest

rel

atio

nshi

ps.

• U

se d

igita

l too

ls (

e.g.

, co

mpu

ters

) to

ana

lyze

ver

y la

rge

data

set

s fo

r pa

ttern

s an

d tr

ends

.

• C

reat

e an

d/or

rev

ise

a co

mpu

tatio

nal m

odel

or

sim

ulat

ion

of a

phe

nom

enon

, des

igne

d de

vice

, pro

cess

, or

sys

tem

.

• D

escr

ibe,

mea

sure

, and

/or

com

pare

qu

antit

ativ

e at

trib

utes

of d

iffer

ent

obje

cts

and

disp

lay

the

data

usi

ng

sim

ple

grap

hs.

• D

escr

ibe,

mea

sure

, est

imat

e, a

nd/

or g

raph

qua

ntiti

es s

uch

as a

rea,

vo

lum

e, w

eigh

t, an

d tim

e to

add

ress

sc

ient

ific

and

engi

neer

ing

ques

tions

an

d pr

oble

ms.

• U

se m

athe

mat

ical

re

pres

enta

tions

to d

escr

ibe

and/

or s

uppo

rt s

cien

tific

conc

lusi

ons

and

desi

gn s

olut

ions

.

• U

se m

athe

mat

ical

, com

puta

tiona

l, an

d/or

alg

orith

mic

re

pres

enta

tions

of p

heno

men

a or

des

ign

solu

tions

to

desc

ribe

and/

or s

uppo

rt c

laim

s an

d/or

exp

lana

tions

.

• U

se q

uant

itativ

e da

ta to

com

pare

tw

o al

tern

ativ

e so

lutio

ns to

a p

robl

em.

• C

reat

e an

d/or

use

gra

phs

and/

or c

hart

s ge

nera

ted

from

sim

ple

algo

rithm

s to

com

pare

alte

rnat

ive

solu

tions

to a

n en

gine

erin

g pr

oble

m.

• C

reat

e al

gorit

hms

(a s

erie

s of

ord

ered

ste

ps)

to s

olve

a

prob

lem

.

• A

pply

mat

hem

atic

al c

once

pts

and/

or p

roce

sses

(su

ch a

s ra

tio,

rate

, per

cent

, bas

ic o

pera

tions

, an

d si

mpl

e al

gebr

a) to

sci

entifi

c an

d en

gine

erin

g qu

estio

ns a

nd

prob

lem

s.

• U

se d

igita

l too

ls a

nd/o

r m

athe

mat

ical

con

cept

s an

d ar

gum

ents

to te

st a

nd c

ompa

re

prop

osed

sol

utio

ns to

an

engi

neer

ing

desi

gn p

robl

em.

• A

pply

tech

niqu

es o

f alg

ebra

and

func

tions

to r

epre

sent

an

d so

lve

scie

ntifi

c an

d en

gine

erin

g pr

oble

ms.

• U

se s

impl

e lim

it ca

ses

to te

st m

athe

mat

ical

ex

pres

sion

s, c

ompu

ter

prog

ram

s, a

lgor

ithm

s, o

r si

mul

atio

ns o

f a p

roce

ss o

r sy

stem

to s

ee if

a m

odel

“m

akes

sen

se” b

y co

mpa

ring

the

outc

omes

with

wha

t is

know

n ab

out t

he r

eal w

orld

.

• A

pply

rat

ios,

rat

es, p

erce

ntag

es, a

nd u

nit c

onve

rsio

ns

in th

e co

ntex

t of c

ompl

icat

ed m

easu

rem

ent p

robl

ems

invo

lvin

g qu

antit

ies

with

der

ived

or

com

poun

d un

its

(e.g

., m

g/m

L, k

g/m

3 , a

cre-

feet

).

N/A

= N

ot a

pplic

able

for

this

gra

de r

ange



K–12 ProgressionsSc

ienc

e a

nd E

ngin

ee

ring

Pra

ctic

es:

Co

nstru

ctin

g E

xpla

natio

ns a

nd

De

signi

ng S

olu

tions

T

he e

nd-p

rodu

cts

of s

cien

ce a

re e

xpla

natio

ns a

nd th

e en

d-pr

oduc

ts o

f eng

inee

ring

are

solu

tions

. The

goa

l of s

cien

ce is

the

cons

truc

tion

of th

eorie

s th

at p

rovi

de

expl

anat

ory

acco

unts

of t

he w

orld

. A th

eory

bec

omes

acc

epte

d w

hen

it ha

s m

ultip

le li

nes

of e

mpi

rical

evi

denc

e an

d gr

eate

r ex

plan

ator

y po

wer

of p

heno

men

a th

an

prev

ious

theo

ries.

The

goa

l of e

ngin

eerin

g de

sign

is to

find

a s

yste

mat

ic s

olut

ion

to p

robl

ems

that

is b

ased

on

scie

ntifi

c kn

owle

dge

and

mod

els

of th

e m

ater

ial

wor

ld. E

ach

prop

osed

sol

utio

n re

sults

from

a p

roce

ss o

f bal

anci

ng c

ompe

ting

crite

ria o

f des

ired

func

tions

, tec

hnic

al fe

asib

ility

, cos

t, sa

fety

, aes

thet

ics,

and

co

mpl

ianc

e w

ith le

gal r

equi

rem

ents

. The

opt

imal

cho

ice

depe

nds

on h

ow w

ell t

he p

ropo

sed

solu

tions

mee

t crit

eria

and

con

stra

ints

.

K–2

Co

nd

ense

d P

ract

ices

3–

5 C

on

den

sed

Pra

ctic

es

6–8

Co

nd

ense

d P

ract

ices

9–

12 C

on

den

sed

Pra

ctic

es

Con

stru

ctin

g ex

plan

atio

ns a

nd d

esig

ning

so

lutio

ns in

K–2

bui

lds

on p

rior

expe

rienc

es

and

prog

ress

es to

the

use

of e

vide

nce

and

idea

s in

con

stru

ctin

g ev

iden

ce-b

ased

ac

coun

ts o

f nat

ural

phe

nom

ena

and

desi

gnin

g so

lutio

ns.

Con

stru

ctin

g ex

plan

atio

ns a

nd d

esig

ning

so

lutio

ns in

3–5

bui

lds

on K

–2 e

xper

ienc

es

and

prog

ress

es to

the

use

of e

vide

nce

in c

onst

ruct

ing

expl

anat

ions

that

spe

cify

va

riabl

es th

at d

escr

ibe

and

pred

ict

phen

omen

a an

d in

des

igni

ng m

ultip

le

solu

tions

to d

esig

n pr

oble

ms.

Con

stru

ctin

g ex

plan

atio

ns a

nd d

esig

ning

so

lutio

ns in

6–8

bui

lds

on K

–5 e

xper

ienc

es

and

prog

ress

es to

incl

ude

cons

truc

ting

expl

anat

ions

and

des

igni

ng s

olut

ions

su

ppor

ted

by m

ultip

le s

ourc

es o

f evi

denc

e co

nsis

tent

with

sci

entifi

c id

eas,

prin

cipl

es,

and

theo

ries.

Con

stru

ctin

g ex

plan

atio

ns a

nd d

esig

ning

so

lutio

ns in

9–1

2 bu

ilds

on K

–8 e

xper

ienc

es

and

prog

ress

es to

exp

lana

tions

and

de

sign

s th

at a

re s

uppo

rted

by

mul

tiple

and

in

depe

nden

t stu

dent

-gen

erat

ed s

ourc

es o

f ev

iden

ce c

onsi

sten

t with

sci

entifi

c id

eas,

pr

inci

ples

, and

theo

ries.

• U

se in

for m

atio

n fr

om o

bser

vatio

ns

(firs

than

d an

d fr

om m

edia

) to

con

stru

ct

an e

vide

nce-

base

d ac

coun

t for

nat

ural

ph

enom

ena.

• C

onst

r uct

an

expl

anat

ion

of o

bser

ved

rela

tions

hips

(e.

g., t

he d

istr

ibut

ion

of

plan

ts in

the

back

yard

).

• C

onst

r uct

an

expl

anat

ion

that

incl

udes

qu

alita

tive

or q

uant

itativ

e re

latio

nshi

ps

betw

een

varia

bles

that

pre

dict

and

/or

desc

ribe

phen

omen

a.

• C

onst

ruct

an

expl

anat

ion

usin

g m

odel

s or

rep

rese

ntat

ions

.

• M

ake

a qu

antit

ativ

e an

d/or

qua

litat

ive

clai

m r

egar

ding

the

rela

tions

hip

betw

een

depe

nden

t and

inde

pend

ent v

aria

bles

.

• N

/A•

Use

evi

denc

e (e

.g.,

mea

sure

men

ts,

obse

rvat

ions

, pat

tern

s) to

con

stru

ct

or s

uppo

rt a

n ex

plan

atio

n or

des

ign

a so

lutio

n to

a p

robl

em.

• C

onst

ruct

a s

cien

tific

expl

anat

ion

base

d on

val

id a

nd r

elia

ble

evid

ence

obt

aine

d fr

om s

ourc

es (

incl

udin

g th

e st

uden

ts’

own

expe

rimen

ts)

and

the

assu

mpt

ion

that

theo

rie

the quick-reference guide to the ngsswillard the nsta quick-reference guide to the ngss k–12...

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