A Programmed Instruction Tutor for Java: Evidence …emurian/cv/IRMA2004.pdfProgrammed instruction...
Transcript of A Programmed Instruction Tutor for Java: Evidence …emurian/cv/IRMA2004.pdfProgrammed instruction...
1A P
rogr
amm
ed In
stru
ctio
n Tu
tor f
or J
ava:
Ev
iden
ce o
f Rul
e-G
over
ned
Lear
ning
Hen
ry H
. Em
uria
nH
enry
H. E
mur
ian
Info
rmat
ion
Syst
ems
Dep
artm
ent
Info
rmat
ion
Syst
ems
Dep
artm
ent
Col
lege
of E
ngin
eerin
g an
d In
form
atio
n Te
chno
logy
Col
lege
of E
ngin
eerin
g an
d In
form
atio
n Te
chno
logy
UM
BC
UM
BC
Bal
timor
e, M
aryl
and
2125
0B
altim
ore,
Mar
ylan
d 21
250
emur
ian@
umbc
.edu
emur
ian@
umbc
.edu
h ttp
://na
sa1.
ifsm
.um
bc.e
du/le
arnJ
ava/
tuto
rLin
ks/T
utor
Link
s.ht
ml
ttp://
nasa
1.ifs
m.u
mbc
.edu
/lear
nJav
a/tu
torL
inks
/Tut
orLi
nks.
htm
l
2
Janu
ary
1, 2
003
Whe
n E
very
body
Kne
w a
Poe
t
To th
e Ed
itor:
Re
"A L
ost E
loqu
ence
," b
y C
arol
Mus
ke-D
ukes
(Op-
Ed, D
ec. 2
9):
The
balk
ing
of st
uden
ts to
put
ting
vers
e to
hea
rt by
rote
mem
oriz
atio
n is
not
lim
ited
to p
oetry
. The
re is
alm
ost a
ped
agog
ical
mal
aise
that
dec
ries r
ote
lear
ning
in d
isci
plin
es li
ke sc
ienc
e, m
athe
mat
ics a
nd e
ngin
eerin
g. A
nd
criti
cal a
naly
sis a
nd sc
hola
rshi
p ar
e be
ing
repl
aced
by
sear
chin
g th
e W
eb.
Ther
e is
a g
row
ing
cont
empt
for t
he h
ard
wor
k of
ach
ievi
ng m
aste
ry.
But
the
beau
ty o
f a p
oem
, onc
e le
arne
d, is
not
in th
e re
cita
tion
of w
ords
. Th
e po
em, c
omm
itted
to m
emor
y, b
ecom
es a
veh
icle
of c
omm
unio
n fo
r the
se
lf an
d th
e so
ul. R
ote
lear
ning
of t
he to
ols o
f tho
ught
has
sim
ilar b
enef
its in
al
l fie
lds.
HEN
RY
H. E
MU
RIA
NB
altim
ore,
Dec
. 29,
200
2
3Ove
rvie
w
1.O
bjec
tives
of t
rain
ing
2.C
halle
nges
of t
each
ing
com
pute
r pr
ogra
mm
ing
to IS
maj
ors
3.A
pro
gram
med
inst
ruct
ion
tuto
ring
syst
em–
From
rote
mem
oriz
atio
n to
“mea
ning
ful
lear
ning
”4.
Evi
denc
e of
effe
ctiv
enes
s5.
Evi
denc
e of
stu
dent
s’ac
cept
ance
and
ap
prec
iatio
n of
this
trai
ning
app
roac
h
4Obj
ectiv
es: N
ear
…
and
Far
1.im
port
java
.app
let.A
pple
t;2.
impo
rt ja
va.a
wt.L
abel
;3.
publ
ic c
lass
MyP
rogr
am e
xten
ds A
pple
t {4.
Labe
l myL
abel
;5.
publ
ic v
oid
init(
) {6.
myL
abel
=new
Lab
el(“
This
is m
y fir
st p
rogr
am.”)
;7.
add(
myL
abel
);8.
myL
abel
.set
Vis
ible
(true
);9.
}10
.}
5Cha
lleng
es1.
IS s
tude
nts
do n
otlik
e to
writ
e co
mpu
ter p
rogr
ams.
2.IS
stu
dent
s ha
ve m
inim
al c
ours
ewor
kin
com
pute
r pr
ogra
mm
ing
and
prog
ram
min
g la
ngua
ges.
3.IS
stu
dent
s ne
eda
fund
amen
tal m
aste
ry
prog
ram
min
g.4.
IS s
tude
nts
are
ofte
n de
mor
aliz
edby
taki
ng c
ours
es
with
com
pute
r sci
ence
maj
ors
that
are
taug
ht b
y co
mpu
ter s
cien
ce fa
culty
. 5.
How
can
we
help
IS s
tude
nts
to a
chie
ve th
e ob
ject
ives
?
6Und
erly
ing
Ass
istiv
e Pr
inci
ples
1.R
ote
Mem
oriz
atio
n is
Goo
d–
Fund
amen
tal t
o m
eani
ngfu
l lea
rnin
g●
Nea
r and
far t
rans
fer
–C
onst
ruct
ivis
m c
omes
late
r (m
uch,
muc
h la
ter..
.).2.
Dis
cipl
ined
Stu
dy B
ehav
ior i
s G
ood
–It
is e
ssen
tial t
o m
aste
ry.
–M
ost s
tude
nts
don'
t kno
w h
ow to
stu
dy. T
hey
don'
t kn
ow h
ow to
mon
itor t
heir
acqu
isiti
on o
f co
mpe
tenc
e.3.
Rep
etiti
on a
nd O
verle
arni
ng a
re G
ood
–C
ontri
bute
to re
tent
ion
4.Fe
elin
g G
ood
Abo
ut Y
ours
elf i
s G
ood
–S
usta
ins
hard
wor
k an
d en
cour
ages
futu
re
lear
ning
.
7Prog
ram
med
Inst
ruct
ion
1.A
set
of s
truct
ured
inte
ract
ions
betw
een
a le
arne
r an
d a
tuto
r.2.
Occ
asio
ns d
isci
plin
ed s
tudy
beh
avio
r tha
t is
focu
sed
on th
e in
divi
dual
lear
ner.
3.M
anag
es th
e m
omen
t-by-
mom
ent i
nter
actio
nsbe
twee
n a
lear
ner a
nd a
tuto
r.4.
Ste
p-w
ise
prog
ress
ion
from
ele
men
tary
kn
owle
dge
units
(lea
rn u
nits
) or f
acts
to th
e ac
hiev
emen
t of a
com
plex
repe
rtoire
(mea
ning
ful
lear
ning
) tha
t is
the
outc
ome
of c
ompl
etin
g th
is
inst
ruct
iona
l sys
tem
.
8Ass
umpt
ion
Pow
er F
unct
ion
Prac
tice
Tria
ls
Errors
9Mar
ketin
g C
halle
nges
An
entre
nche
d an
d cu
ltura
l elit
ism
exi
sts
with
in a
cade
me
that
vie
ws
com
pete
ncy-
base
d ed
ucat
ion
with
sus
pici
on. I
f al
l stu
dent
s ac
hiev
e an
“A,”
som
ethi
ng is
con
side
red
to
be w
rong
. My
view
is th
at m
y jo
b is
to o
verc
ome
indi
vidu
al d
iffer
ence
s, n
ot ju
st to
doc
umen
t the
m.
Wha
t I h
ave
to d
eal w
ith fr
om c
ompu
ter s
cien
tists
,pr
ofes
sion
al J
ava
inst
ruct
ors,
cog
nitiv
e ps
ycho
logi
sts,
…
and
just
abo
ut e
very
body
els
e, to
incl
ude
my
colle
ague
s:
10Mar
ketin
g C
halle
nges
Rag
e
11Mar
ketin
g C
halle
nges
Rag
e
Con
tem
pt
12Mar
ketin
g C
halle
nges
Rag
e
Con
tem
pt
Rid
icul
e
13My
Res
pons
e?
14I don
’t ca
re.
15Sym
bol F
amili
arity
16Sym
bol I
dent
ifica
tion
17Show
Item
18Expl
ain
Item
19Exam
ple
of a
Rul
e
Insi
de th
e A
pple
t cla
ss, t
he in
it() m
etho
d ha
s no
stat
emen
ts in
it.
Whe
n th
e pr
ogra
mm
er u
ses t
he in
it() m
etho
d in
a p
rogr
am
and
adds
stat
emen
ts to
it, t
hat i
s cal
led
over
ridi
ng th
e in
it()
met
hod.
Tha
t is a
n im
port
ant r
ule
to k
now
.
The
gen
eral
form
of t
he in
it() m
etho
d is
as f
ollo
ws:
publ
ic v
oid
init(
) {a
line
of J
ava
code
;a
line
of J
ava
code
;}
20Test
Item
21Ente
r Ite
m
22Row
Fam
iliar
ity
23Row
Iden
tific
atio
n
24Row
Lea
rnin
g (T
wo
pass
es)
25Text
Win
dow
26Text
Err
or
27Subj
ects
●G
roup
1–
Sum
mer
200
3●
Gra
duat
e C
ours
e●
N =
12
●8
fem
ales
, 4 m
ales
●G
roup
2–
Sprin
g 20
04●
Und
ergr
adua
te
Cou
rse
●N
= 12
●2
fem
ales
, 10
mal
es
28Bac
kgro
und:
Age
29Bac
kgro
und:
Cou
rses
30Bac
kgro
und:
Jav
a Ex
perie
nce
31Proc
edur
e
●Fi
rst S
essi
on: 3
Hou
rs–
Pre
-Tut
orQ
uest
ionn
aire
s●
Bac
kgro
und
●SS
E●
Rul
es te
st: 5
or 1
0 m
ultip
le-c
hoic
e qu
estio
ns●
Rul
es e
ffica
cy–
Run
the
Tuto
r–
Pos
t-Tut
orQ
uest
ionn
aire
s●
SSE
●R
ules
test
●R
ules
effi
cacy
●Se
cond
Ses
sion
: 3 H
ours
–Le
ctur
e–
Run
the
App
let
–P
ost-L
ectu
reQ
uest
ionn
aire
s●
SSE
●R
ules
test
●R
ules
effi
cacy
32Softw
are
Self-
Effic
acy
Ass
essm
ent
21 It
ems
How
con
fiden
t are
you
that
you
can
use
the
follo
win
g sy
mbo
l now
to w
rite
a Ja
va a
pple
t?
impo
rt
Not
at a
ll co
nfid
ent
1
2 3
4
5
6 7
8
9
10
Tota
lly c
onfid
ent
33Rul
es A
sses
smen
t5
or 1
0 Ite
ms
Whi
ch o
ne o
f the
bel
ow li
nes
decl
ares
myJ
Fram
e as
a p
oten
tial i
nsta
nce
of th
e JF
ram
e cl
ass?
1.m
yJFr
ame
exte
nds
JFra
me.
2.JF
ram
e m
yJFr
ame:
3.m
yJFr
ame
JFra
me;
4.JF
ram
em
yJFr
ame;
How
con
fiden
t are
you
that
you
sel
ecte
d th
e co
rrec
t ans
wer
?N
ot a
t all
conf
iden
t. 1
2
3
4
5
6
7
8
9
1
0 T
otal
ly c
onfid
ent.
34Cor
rect
Rul
e Te
st A
nsw
ers
35Con
fiden
ce in
Rul
e A
nsw
ers
36Softw
are
Self-
Effic
acy
37Prog
ram
Err
ors
38Eval
uatio
ns o
f the
Tut
or
39Con
clus
ions
1.P
rogr
amm
ed in
stru
ctio
n is
an
effe
ctiv
e to
ol
in te
chno
logy
edu
catio
n.2.
It m
eets
the
need
s of
the
indi
vidu
al le
arne
r.3.
The
inst
ruct
iona
l des
ign
can
prom
ote
mea
ning
ful l
earn
ing
and
self-
conf
iden
ce.
4.Th
e tu
torin
g sy
stem
is w
ell-r
ecei
ved
by
novi
tiate
lear
ners
.5.
The
com
pete
ncy
atta
ined
set
s th
e oc
casi
on
for a
dvan
ced
lear
ning
with
ent
husi
asm
.6.
Stu
dent
s lik
e th
e tu
tor,
and
so d
o I.
40Than
k yo
u!
Que
stio
ns?