K Hefty Final ID Project

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EdTech 503: ID Project | Page 0

Lets PlayBall!!Excel, Baseball & Statistics

Final ID Project

i "e#ty

EdTech 503: Instr$ctional Design

Boise %tate &ni'ersity

($g$st )* +0)3

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Table of Contents

Reflection Paper 3

Part 1 Topic 5

1a. Goal Statement 5

1b. Audience Description 5

1c. Rationale 5

Part 2 Analysis Report 7

2a. Description of eed 7

2a1. eeds Assessment Sur!ey 7

2a2. eeds Assessment Results "

2b. Description of #earnin$ %onte&t 12

2b1. #earnin$ %onte&t Description 12

2b2. Transfer %onte&t Description 12

2c. Description of #earners 13

2d. Tas' Analysis (lo)c*art 1+

Part 3 Plannin$ 15

3a. #ist of ,nstructional -becti!es 15

3b. -becti!es /atri& Table 17

3c. AR%S Table 20

Part + ,nstructor Guide 22

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+a. ,nstructor Guide 22

Part 5 #earner %ontent 2

5a. #earnin$ /aterials 2

5b. Assessment /aterials 27

5c. Tec*nolo$y Tool Rationale 2"

Part (ormati!e !aluation Plan 2

a. &pert Re!ie) Plan 2

b. -ne4to4-ne Re!ie) Plan 2

c. Small Group !aluation Plan 30

d. (ield Trial Plan 31

Part 7 (ormati!e !aluation Report 32

7a. !aluation Sur!ey 32

7b. Report of &pert Re!ie) 33

7c. Desi$ners Response to Re!ie) 3+

Part " Standards Grid 35

". A%T Standards Grid 35

Appendices +3

Appendi& A6 Rubrics +3

Appendi& 6 /icrosoft &cel &amples ++

Appendi& %6 ,nnin$ %*ec'list +5

Appendi& D6 References +

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Reflective Synthesis Paper

a'in$ re8uires $reat attention to detail. (irst9 t*e ba'er must decide on )*at t*ey

)ant to ba'e9 t*en t*ey *a!e to decide )*ic* recipe t*ey )ould li'e to use. T*erecan be multiple options for t*e same product but t*e ba'er *as to loo' at t*e needs

of )*o *e is ba'in$ for. Suppose t*e ba'er )ants to ma'e blueberry muffins for

c*ildren. ,s anyone aller$ic to nuts or e$$s or $luten: T*e ans)ers to t*ese

8uestions )ill determine )*ic* recipe t*e ba'er uses. After decidin$ on )*ic*

recipe to use9 t*e ba'er )ill carefully assemble all t*eir in$redients. a'in$

re8uires precise attention to detail. T*e in$redients must be assembled in t*e

correct order )it* t*e correct amount and for a precise amount of time. A $ood

ba'er follo)s t*e steps of analy;e9 de!elop9 and e!aluate. (ailin$ to do any of

t*ese steps could be disastrous.

Details9 its all about t*e details< T*at is *o) , )ould summari;e ,nstructional

Desi$n. An ,nstructional Desi$ner is similar to a ba'er= bot* must follo)s t*e

steps of analy;e9 desi$n9 and e!aluate. A $ood desi$ner starts )it* an analysis of

)*at s*e )ants to accomplis* > a $oal. T*e desi$ner in education )ill consult

)it* subect matter e&perts or different departments to de!elop t*e ultimate end

$oal. A $ood desi$ner must also start )it* e!aluatin$ prere8uisite s'ills and any

restrictions before c*oosin$ t*e best ?recipe@ to follo). ust as a ba'er assembles

*er in$redients before be$innin$9 t*e ,nstructional Desi$ner prepares *er learnin$

obecti!es. Bit* precise attention to detail9 t*e ,nstructional Desi$ner carefullyand met*odically be$ins to assemble *er proect. -r$ani;ation is often t*e 'ey to

success in bot* ,nstructional Desi$n and ba'in$.

!aluation is t*e 'ey to bot* ba'in$ and ,nstructional Desi$n. -nce t*e muffins

*a!e ba'ed for t*e set amount of time9 t*e ba'er must c*ec' t*em. Did t*ey ba'e

lon$ enou$*: Did t*ey ba'e for too lon$: Co) do t*ey taste: Bas t*e end result

)*at )as e&pected or *oped for: A desi$ner *as to as' *erself similar 8uestions.

ltimately9 is t*e end result )*at )as *oped for: ,f not9 bot* t*e ba'er and t*e

,nstructional Desi$ner must loo' bac' and find t*e error. (or ba'in$9 if all t*e

steps )ere follo)ed correctly t*ere are ot*er factors suc* as ele!ation9 in$redient!ariations or brands of o!en t*at can effect ba'in$ time. A $ood desi$ner must also

ma'e sure t*at enou$* time )as allo)ed for *er product to *a!e fully de!eloped.

Personally9 , *a!e been astounded by t*e ,nstructional Desi$n process. As ,

de!eloped my o)n ,nstructional Desi$n proect9 , *a!e made a $reat many

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mista'es. /y ori$inal proect )as too broad in bot* scope and desi$n9 especially

for a t)o to t*ree *our proect. , needed to scale it )ay bac' and $et more focused.

-nce , simplified t*e o!erall scope , )as able to focus on t*e details. , also needed

to re!ise my $oals multiple times. T*e more detailed , $ot9 t*e more focused t*e

proect became. , learned a lot about ,nstructional Desi$n t*rou$* t*is process9especially t*rou$* my errors.

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Part 1 Topic: Microsoft Excel, Statistics and Baseball

1a. earnin! "oal

After t)o to t*ree *ours of researc* on t*e ,nternet and direct instruction9 students)ould utili;e &cel spreads*eets to be able to create and interpret a statistical

correlation proect relatin$ )innin$ to t*e offensi!e statistics of t*e Seattle

/ariners o!er a defined period of time.

1b. #$dience %escription

T*e learners of t*is ,nstructional Desi$n proect )ill be 7 t*$rade students in t*e

ad!anced al$ebra course )*o *a!e pre!iously ta'en t*e ad!anced pre4al$ebra

course. ,nstruction )ill ta'e place in t*e computer lab meetin$ on t*ree

consecuti!e Bednesdays9 durin$ desi$nated ?computer@ time9 for an *our eac*time.

1c. Rationale

, *a!e a 7t*$rade son )*o lo!es mat* and sports. -!er 20 years a$o , tau$*t

middle sc*ool mat* and remember doin$ a ?baseball proect@ t*at t*e students ust

lo!ed. ut 20 years a$o )e did !irtually e!eryt*in$ by *and. T*e students *ad to

brin$ in p*ysical ne)spapers and loo' at t*e bo& scores to find t*e data to calculate

battin$ a!era$es for t*e Seattle /ariners. T*e students )ould record all t*is

information on )or's*eets and9 by *and9 calculate all t*e data and $enerate !isual

representations. , )ondered *o) different it )ould be to update t*is proect )it*

tec*nolo$y. , 8uic'ly reali;ed t*at t*e computations and $rap*s could be done

)it* /icrosoft &cel and t*e students could spend more time analy;in$ t*e

compilation of data and loo'in$ for correlations. /y sons sc*ool pro!ided t*e

perfect opportunity to try t*is out.

(or t*is proect9 bot* supplanti!e and $enerati!e strate$ies )ill be utili;ed. T*e

o!erall strate$y of t*is instruction )ill be a blend of appro&imately 20E

supplanti!e and "0E $enerati!e instruction. T*e instruction )ill be primarilyprocedural9 )*ic* )ill re8uire a more structured approac* to learnin$ and

instruction. Researc* and data analysis )ill *a!e a more $enerati!e instructional

approac*. T*ere )ill be opportunities for creati!ity for t*e students $rap*ical

representations and final product. (or many of t*e steps it )ill be critical t*at t*ey

complete eac* step in a precise manner in order to correctly calculate t*e data.

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Procedural )ill be t*e maor instructional strate$y. #earners )ill be follo)in$ a

specific set of steps needed in order to be able to create t*eir $rap*s usin$

/icrosoft &cel. T*e learner )ill reac* a decision points alon$ t*e )ay and )ill

need to c*oose t*e correct branc* of t*e procedure to follo). At t*ese decisionpoints9 t*e instructor )ill utili;e Problem Sol!in$ ,nstruction. T*ere )ill be a fe)

places )*ere t*e learner can complete steps out of order )it*out impactin$ t*e

final outcome. ,n order for learners to reac* t*e instructional $oal9 t*ey )ill need to

do more t*an recall t*e steps of t*e procedure t*ey )ill need to apply t*e

procedures to create somet*in$ ne).

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P#RT &: #nalysis Report&a. %escription of 'eed

&a1. 'eeds #ssess(ent S$rvey

T*e follo)in$ is a list of 8uestions presented by Sur!ey /on'ey at6*ttps6FF))).sur!eymon'ey.comFsF37HI . , sent out t*is sur!ey to all 2 of my

sons classmates. 2+ out of 2 responded.

T*ese 8uestions )ere de!eloped to analy;e t*e s'ill set of t*e students )*o )ill be

participatin$ in t*e proect. T*is sur!ey co!ers t*e students familiarity )it*

tec*nolo$y9 baseball and statistics.

Baseball Statistics ) Excel S$rvey *$estions

1. Ca!e you e!er used /icrosoft &cel before:

2. Co) comfortable are you )it* /icrosoft &cel:

3. Do you 'no) *o) to do researc* on t*e ,nternet:

+. Co) comfortable are you )it* doin$ researc* on t*e internet:

5. Are you familiar )it* aseball:

. Do you 'no) *o) to calculate a battin$ a!era$e:

7. Do you 'no) *o) to read a ?bo& score@ for baseball:

". B*ic* of t*e follo)in$ $rap*s are you familiar )it*: %ircle or Pie Grap*9

ar Grap*9 #ine Grap*9 Scatterplots

. Do you understand *o) to calculate t*e mean of data:

10. Do you understand *o)