Part
5
Educational
s
oftware and
e

Learning
s
ystems
Recently
e

learning has become one of the most important forms of educ
a
tion. E

learning systems include learning content, but also the infrastru
c
ture that allows content to be created, stored, accessed, a
nd delivered and
the learning process to be managed. The architecture of these e

learning
systems is a crucial aspect. Architectures define structures that connect a
system as an e

learning software and information system with its instru
c
tional and educati
onal context. In this part we present some papers that
deal with the principles and components of the architectures of e

learning
systems
and how they operated in terms of the instructional and content
aspects involved. The papers include u
sing mathematics
for data traffic
modeling within an e

learning platform
, d
eveloping statistics learning at a
distance using formal discussions
, d
istance teaching in the course of tec
h
nology
in Senior High School
, electronic exams for the 21st century, e
f
fects of Orff mus
ic teaching method on creative think
ing abilities,
kno
w
ledge application toward
preparing engineering high school teachers
,
c
ollaborative on

line network and culture exchange project
, a
study of the
project on mobile device in education
, t
he solutions of m
obile technology
for primary school
, a
study of verifying
knowledge reuse
path using
s
tru
c
tural equation modeling
, a
w
eb

based system for
distance learning
of pr
o
gramming
, new software for the study of the classic surfaces from diff
e
r
ential geometry, d
ata
requirements for d
etecting student learning style and
o
ntology

based feedback e

learning system for mobile computing
.
Chapter 1
Using mathematics for data traffic modeling
within an e

learning platform
Marian Cristian Mih
ă
escu
Software Engineering Depart
ment, University of Craiova, Romania
Abstract.
E

Learning data traffic characterization and modeling
may bring impo
r
tant knowledge about characteristics of traffic. It is
considered that without measurement it is impossible to build reali
s
tic traffic model
s. We propose an analysis architecture employed for
characterization and modeling using data mining techniques and
mathematical models. The main problem is that usually real data
traffic has to be measured in real time, saved and later analyzed. The
propos
ed architecture uses data from application level. In this way
the data logging process becomes a much easier task with practica
l
ly almost the same outcomes.
Keyword
s
.
D
ata traffic, mathematical modeling, data mining
1.1
Introduction
Tesys e

Learnin
g platfo
rm was developed and de
ployed. It has a built
in
mechanism for monitoring ac
tions performed by users and data traffic
transferred during usage. In this paper we study the possibility of modeling
data traffic using data mining techniques and mathematics bas
ed on pe
r
formed actions. This would have great benefits regarding the overhead
within the platform. Introduction presents in short Tesys e

Learning pla
t
form.
In second section there are pre
sented the employed methods: a
c
tions
and data moni
toring, the proce
ss of clustering users. The clustering pro
c
ess
groups similar
users based on a specific simi
larity function. At cluster le
v
368
Marian
Cristian Mihăescu
el, self

similarity of data traffic is then examined. This is a
c
complished by
esti
mating Hurst parameter.
In third section of the pa
per there is presented the proposed architecture
and the analysis process. In short, the architecture and employed process
will try to estimate the data traffic self

similarity within a cluster of users.
In fourth section there are presented obtained resul
ts. Finally, the concl
u
sions and future works are presented.
The platform has built in capability of monitoring and recording user’s
activity at application level. The activity represents valuable data since it is
the raw data for our machine learning and
modeling process. User’s s
e
quence of sessions makes up his activity. A session starts when the student
logs in and finishes when the student logs out. Under these circumstances,
a sequence of actions makes up a session.
1.2
Methods and materials
There are
many different ways for representing patterns that can be di
s
covered by mac
h
ine learning. From all of them we choose clustering,
which is the process of grouping a set of physical or abstract objects into
classes of similar objects [1]. Basically, for our
pla
t
form we create clusters
of users based on their activity.
As a product of clustering process, associations between different a
c
tions on the platform can easily be inferred from the logged data. In ge
n
e
r
al, the activities that are present in the same p
rofile tend to be found t
o
gether in the same session. The actions making up a profile tend to co

occur to form a large item set [8].
There are many clustering methods in the literature: partitioning m
e
t
h
ods such as [9], hierarchical methods, density

based
methods such as [10],
grid

based methods or model

based methods.
Hierarchical clustering alg
o
rithms like the Single

Link method [
4
] or OPTICS [
7
] compute a r
e
prese
n
tation of the possible hierarch
i
cal clustering structure of the database in the
form of a de
ndrogram or a reachability plot from which clusters at various
resolutions can be extracted, as has been shown in [
11
].
From all of these
we chose to have a closer look on partitioning methods.
The EM algorithm [2] takes into consideration that we know nei
ther of
these things: not the distribution that each training instance came from, nor
the param
e
ters μ, σ or the probability. So, we adopt the procedure used for
the k

means clustering algorithm and iterate. Start with initial guess for the
five parameters
, use them to calculate the cluster probabilities for each i
n
stance, use these probabilities to estimate the param
e
ters, and repeat. This
Using mathematics for data traffic modeling within an e

learning platform
369
is called the EM algorithm for “expectation

maximization”. The first step,
the calculation of cluster probabilities (w
hich are the “expected” class va
l
ues) is “expectation”; the second, calculation of the distribution parameters
is “maximization” of the likelihood of the distributions given the data [8].
The EM algorithm is implemented in Weka package[12] and needs the
in
put data to be in a custom fo
r
mat called
arff
.
Self

similarity and long

range dependence of data traffic are discussed
in detail in [13, 14 and 15]. A process is considered to be self

similar if
Hurst p
a
rameter satisfies the condition:
(1.1)
where the equality is in the sense of finite

dimensional distributions.
Parameter H can take any value between 1/2 and 1 and the higher the
value the higher the degree of self

similarity. For smooth Poisson traffic
the value is H=0.5. There are
four methods are used to test for self

similarity. These four methods are all heuristic graphical methods, they
provide no confidence intervals and they may be biased for some values of
H. The r
e
scaled adjusted range plot (R/S plot), the Variance

Time plo
t and
the Periodogram plot, and also the theory behind these methods, are d
e
scribed in detail by Beran [13] and Taqqu et al. [16]. Molnar et al. [17] d
e
scribes the index of dispersion for counts method and also discuss how the
estimation of the Hurst param
eter can depend on estimation technique,
sample size, time scale and other factors.
1.3
Proposed analysis process
The analysis process starts from logged data about actions and data tra
f
fic
and comes up with an estimation of Hurst parameter. This estimatio
n of
self

similarity represents important knowledge in characterizing and mo
d
eling data traffic. In Figure 1.1 it is presented the employed analysis pr
o
cess.
Fig.
1.1
.
Analysis process
370
Marian Cristian Mihăescu
The analysis process starts by creating clusters of users based on th
eir
activity. For this there is used only the data regarding performed actions.
Once the clusters of users are obtained, the data traffic transferred within
each cluster is taken into consideration by H P
a
rameter Estimator module.
This module will produce
three plots: R/S plot, Variance

Time plot and the
Period
o
gram plot.
1.4 Results
The EM algorithm is implemented in Weka package[19] and needs the i
n
put data to be in a custom format called
arff
. Under these circumstances
we have developed an offline Java a
pplication that queries the platform’s
database and crates the input data file called
activity.arff
. This process is
automated and is driven by a properties file in which there is specified
what data will lay in activity.arff file.
Running the EM algorithm
created three clusters. The procedure clu
s
tered 91 instances (34%) in cluster 0, 42 instances (16%) in cluster 1 and
135 instances (50%) in cluster 3. The final step is to check how well the
model fits the data by computing the likelihood of a set of test
data given
the model.
Weka measures goodness

of

fit by the logarithm of the likel
i
h
ood, or log

likelihood: and the larger this quantity, the better the model
fits the data. Instead of using a single test set, it is also possible to compute
a cross validat
ion estimate of the log

likelihood. For our instances the va
l
ue of the log

likelihood is

2.61092 which represent a promising result in
the sense that instances (in our case students) may be classified in three
di
s
joint clusters based on their activity.
Th
e clustering process produced the following r
e
sults:
Table
1.1
.
Distribution of users in clusters
Cluster
No. of users
0
91 (34%)
1
42 (16%)
2
135 (50%)
For obtained clusters a study of self similarity of tra
f
fic was performed.
More precisely, self

sim
ilarity was studied for cluster 0 formed of 91 st
u
dents (34%).
In 6 month of functioning on the platform there were executed over
10,000 actions of different types: course downloads, messaging, self tests,
exams. For comput
a
tions a packet was considered to
have 1,000 bytes.
Using mathematics for data traffic modeling within an e

learning platform
371
For estimation of Hurst parameter there was chosen a 3 hours interval,
between 18:00 and 21:00 which is considered to be a heavy traffic period.
This may be o
b
served from the general traffic statistics presented in
Figure
1.
2
.
Fig.
1.
2
.
General data traffic on e

Learning platform
The interval from 18:00 to 21:00 was chosen for close analysis. The R/S
plot estimated H parameter to a value of 0.89. The time

variance plot
showed a slope of

0.320 which means a value of H of 1+slope/2=0.84
.
The IDC (Index of Dispersion for Counts) shows an H parameter of 0.88.
In Periodogram plot there may be observed a value of H = 0.85. These
m
e
thods do not obtain exactly the same values but values are over 0.5
which is a good indication of traffic’s self

similarity.
Fig. 1.3.
H parameter
–
R/S plot
Fig. 1.4.
H parameter
–
V

T plot
The self

similarity of byte traffic presents similar values for H param
e
ter. The number of bytes tran
s
ferred in each bin were computed and results
presented in
Table
1.2.
In this table there are presented estimations of H
parameter for different dimensions of time bin.
372
Marian Cristian Mihăescu
Having in mind that non

stationary traffic may be easily taken as self

similar stationary traffic there were also examined smaller intervals of time
bins. H
p
a
rameter was estimated for each of the 6 intervals of 30 minutes
between 18:00 and 21:00. The results are pr
e
sented in Figure 1.7.
Fig. 1.5.
H parameter
–
P plot
Fig. 1.6.
H parameter
–
IDC plot
Table
1.2
.
Hurst parameter estimates for 18:00

21:00 time
interval
Bin size
Packets
Bytes
H
R/S
H
VT
H
P
H
R/S
H
VT
H
P
2h
0.85
0.84
0.87
0.83
0.86
0.88
4h
0.82
0.78
0.85
0.81
0.86
0.88
6h
0.84
0.83
0.89
0.77
0.79
0.85
Fig. 1.7.
Hurst parameter from 18:00 to
21:00
Fig. 1.8.
Hurst parameter for each hour
of
traffic monito
r
ing
In this way, there was estimated H parameter for three hours from a
complete interval of 24 hours. Estimation of H parameter for other inte
r
vals is presented in
Figure
1.
8.
Estimations were accomplished for packet data traffic and a tim
e inte
r
val of 15 minutes. All three methods show high values between 15:00 and
20:00. Because this time interval corresponds to moments when the pla
t
Using mathematics for data traffi
c modeling within an e

learning platform
373
form was intensely used confirms the researches of Leland et. al. [18] that
expressed the idea that when ne
twork load is high than the degree of self

similarity is increased.
The fact that traffic is found to be self

similar does not change its b
e
h
a
v
ior but it changes the knowledge about real traffic and also the way in
which traffic is modeled. It has lead man
y [19] to abandon the Poisson

based modeling of network traffic for all but user session arrivals. Real
traffic, well described as self

similar, has a “burst within burst” structure
that cannot be described with the traditional Poisson

based traffic mode
l
i
ng.
1.5
Conclusions
Data analysis is done using EM clustering algorithm implemented by W
e
ka system and mathematical traffic modeling.
Mathematical modeling estimates the self

similarity of data traffic. This
is accomplished by heuristic graphical methods:
R/S plot, variance

time
plot, IDC plot, periodogram plot. The analysis is performed rigorously for
a three hours interval, from 18:00 to 21:00 but also for the whole day.
All the analysis follows a proposed analysis process that has as input d
a
ta regarding
executed actions and transferred bytes within the platform and
has as output estimates of the Hurst parameter.
Values found for Hurst parameter are very promising. All calculations
showed values above 0.7 and many times above 0.8 which indicate a good
le
vel of self

similarity.
References
1.
Jiawei Han, Micheline Kamber (2001) Data mining
–
concepts and tech

niques. Morgan Kaufmann Publishers
2.
Agrawal
R, Srikant R (1994) Fast algorithms for mining association rules. In:
Proc. of the 20th VLDB Conference,
Santiago, Chile,
pp 487

499
3.
MacQueen J (1967) Some methods for classification and analysis of mul

tivariate observations. In: 5th Berkeley Symp. Math. Statist. Prob., pp 281

297
4.
Ester M, Kriegel H

P, Sander J, Xu X (1996) A density

based algorithm f
or
discovering clusters in large spatial databases with noise. In: Proc. KDD’96,
Portland, OR, pp 226

231
5.
Sibson R (1973) SLINK: An optimally efficient algorithm for the single

link
clu
s
ter method. The Computer Journal 16(1):30

34
374
Marian Cristian Mihăescu
6.
Ankerst M, Breuing
M, Kriegel H

P, Sander J (1999) OPTICS: Ordering
points to identify the clustering structure. In: SIGMOD’99, pp 49

60
7.
Sander J, Qin X, Lu Z, Niu N, Kovarsky A (2003) Automated extraction of
clusters from hierarchical clustering representations. In: PAKD
D’03
8.
Witten
Ian H, Eibe Frank (2000) Data mining
–
practical machine learning
tools and techniques with Java implementations. Morgan Kaufmann Publis
h
ers
9.
Nasraoui O, Joshi A, Krishnapuram R (1999) Relational clustering based on a
new robust estimator
with application to web mining. In: Proc. Intl. Conf.
North American Fuzzy Info. Proc. Society (NAFIPS 99), New York
10.
Agrawal
R, Srikant
R (1994) Fast algorithms for mining association rules. In:
Proc. of the 20th VLDB Conference, Santiago, Chile,
pp 48
7

499
11.
Mobasher B, Jain N, Han E

H, Srivastava J (
1996
)
Web mining: Pattern di
s
covery from World Wide Web transactions. Technical Report 96

050, Unive
r
sity of Minnesota
12.
Holmes G, Donkin A, Witten IH (1994) Weka: a machine learning wor
k
bench. In: Pro
ceedings of the Second Australian and New Zealand Confe
r
ence
on I
n
telligent Information Systems, Brisbane, Australia, pp 357

361
13.
Beran
J (1994) Statistics for Long

Memory Processes. Chapman & Hall, New
York
14.
Willinger
W, Taqqu
MS, Sherman
R, Wilson
D (1995) Self

similarity through
high

variability: statistical analysis of Ethernet LAN traffic at the source level.
In: Proceedings of SIGCOMM ‘95, pp 100

113
15.
Willinger
W, Paxson
V, Taqqu
MS (1998) Self

similarity and heavy tails:
structural modeling
of network traffic. In: Adler
R, Feldman
R, Taqqu
MS
(eds) A practical guide to heavy tails: statistical techniques and a
p
plications,
Birkhauser, Boston
16.
Taqqu
MS, Teverovsky
V (1998) On estimating the intensity of long

range
dependence in finite and in
finite variance time series. In: Adler
R, Feldman
R,
Taqqu
MS (eds) A practical guide to heavy tails: statistical techniques and a
p
plications, Birkhauser, Boston
17.
Molnar
S, Vidacs
A, Nilsson
A (1997) Bottlenecks on the way towards fra
c
tal
characterizati
on of network traffic: Estimation and interpretation of the hurst
parameter. In: International Conference on the Performance and Management
of Communication Network, Tsukuba, Japan
18.
Leland
WE, Taqqu
MS, Willinger
W, Wilson
DV (1994) On the self

similar
nature of Ethernet traffic (Extended version). IEEE/ACM Transactions on
Networking 2(1):1

15
20.
Paxson
V, Floyd
S (1995) Wide

area traffic: The failure of poisson mode
l
ing.
IEEE/ACM Transactions on Networking 3(3):226

244
Chapter
2
Developing statistics
learning at a distance using
formal discussions
Jamie D. Mills
Department of Educational Studies, University of Alabama
316

A Carmichael Hall, Box 870231, Tuscaloosa, AL 35487

0231, USA
jmills@bamaed.ua.edu
Abstract
.
The purpose of t
his paper is to report some preliminary
empirical results of students enrolled in two graduate

level hybrid
courses. A new design feature, the discussion board, was recently
implemented in one section in order to increase overall interaction
as well as to
better monitor and assess student learning. The preli
m
inary results of this study indicate that students who were more a
c
tively involved with the course materials, discussions, and others in
the class performed better academically than students who were le
ss
involved. The results might support formal asynchronous discu
s
sions as one teaching strategy that might facilitate the learning of
statistics concepts online. There have been no e
m
pirical studies that
focus on how effective asynchronous discussions migh
t be utilized
in an online/hybrid statistics course.
Keywords.
Distance education, Hybrid/online education, Statistics
learning, Teaching and learning, Formal asynchronous discussion,
Design
2.1
Introduction
Today, teaching and learning using technology is
used more than ever b
e
fore in higher education.
According
to the Council for Highe
r Education
Accreditation
, many traditional colleges and universities are now offering
376
Jamie D. Mills
courses and complete degree

programs in a wide variety of disciplines at a
distance
[1
].
This alternative form of course delivery is a fast

growing
trend and has the potential to change and revolutionize teaching and lear
n
ing at every level of education, perhaps forever.
Teaching statistics at a distance is also becoming a popular course of
fe
r
ing [2,
3]; however, there is a lack of empirical results and discussions
about teaching and
learning in this environment.
In particular, many que
s
tions might be of interest: How is
instruction “best” delivered?
What sp
e
cific technologies seem to be hel
pful for learning specific statistics co
n
cepts online?
How does student

to

student interaction and student

to

teacher interaction take place?
Is t
here an optimal course design?
Which
design features (i.e., whiteboard, chat

feature, discussion board) in the
course development system appear to be most effective for students lear
n
ing statistics?
Although there are researchers who are studying how to d
e
liver statistics courses in this new technological environment, there is still
much to learn about how to effe
ctively implement these course
s and what
practices are best.
Another common problem in many online courses is a lack of teacher

to
student interaction, as well as stude
nt

to

student interaction [2].
Students
may feel not only isolated from the teacher, but
also isolated and deprived
of the “normal” social interaction and cognitive learning processes that
take place
in a face

to

face class [4, 5].
Particularly in a statistic course,
where students often feel anxious and insecure about learning with an i
n
stru
ctor face

to

face each week [6], additional online support may be n
e
c
essary and critical
for student academic success.
In an effort to improve interaction in an online course, the discussion
board might provide one avenue in which to accomplish this task.
There is
substantial evidence to indicate that students learn more when they are a
c
tively engaged with the course materials, their classmates, and the instru
c
tor [7
, 8].
Therefore, how can students in statistics courses utilize the di
s
cussion board?
In oth
er disciplines, asynchronous discussion g
roups have
been very valuable.
The following authors [9] found that asynchronous
discussions in their psychology and educational sciences courses r
e
flected
high phases in knowledge construction while others have rep
orted that di
s
cussion boards can provide an interactive venue where students can reflect,
evaluate, solve pro
blems, and exchange ideas [10].
Is it possible to effe
c
tively discuss statistics concepts online through the use of asynchronous
discussions and as
sist students through different levels of learning?
Developing statistics learning at a distance using formal discussions
377
2.
2
The hybrid course
2.2.1
Course modules
The W
ebCT course management system was
used to deliver the hybrid
cour
ses. Although many students were
familiar with WebCT or have us
ed
it in other courses, the
re was
an animated talking head on the front page of
the site to welcome students to the course, provide brief announcemen
ts
and course logistics, and
refer students to begin the course with the trai
n
ing video module, which illustrates how to use relevant
modules in the
course (i.e., how to submit assignments, how to read the calendar, how to
use the discussion board). The training videos also explain
ed
all of the
links in the course (i.e., where assignments are posted, where the lecture
and SPSS movies are
located, examples of how files should “look” before
submitting). The streaming videos on the site require a high

speed internet
connection, which is available in the labs on campus, if working off

campus is not feasible for stude
nts. Technical difficultie
s were
handled
through the university help desk.
An interactive Smart board, smart pen, projector, and a computer were
used for the recording of all course materials. The Smart pen was used to
write text and formulas as well as drawings that were all captu
red as
streaming audio and video clips. The clips were segmented into smaller
chunks of related concepts/topics to allow for greater flexibility in viewing
the videos and to encourage step

by

step learning. Figure
2.
1 provides an
example of a typical video
the students might see in each chapter.
Fig.
2.1
.
Students watch streaming videos about the normal distribution.
378
Jamie D. Mills
2.2.2
Collaborative Learning
For both the spring and fall 2005 courses, discussion board problems
(without solution
s) and practice problems (with solutions) were posted for
specific topics in their respective discussion areas. The discussion board
problems, which consisted of 5

6 questions to answer for each problem,
were context

related research problems which require
d students to apply
the concepts they were learning to a specific research scenario. The pra
c
tice problems had similar objectives but were not as comprehensive (i.e.,
one question). The discussion board problems were reserved for specific
students involved
in group work while the practice problems could be di
s
cussed by all students. During the spring semester, the discussion board
and practice problems were available to all students for discussions but
postings were not required. The students enrolled in th
e fall semester,
however, were required to make contributions and participate in group
work. Although students in both courses had access to the same discussion
board and practice problems, the major difference was the requirement to
participate in posting
solutions to discussion board and practice problems.
The students in the fall course received a post grade for their contrib
u
tions, which were weighted at 35% of their final course grade. A contrib
u
tion could be 1) a question about a practice or discussio
n board problem,
2)
a
discussion of a solution to a practice problem, or 3) some other co
n
tent

related contribution over the specific topic of interest. Students were
generally given full credit if they posted by the deadline. Therefore, acc
u
racy was gener
ally not considered. As long as the student demonstrated a
concerted effort to make a contribution, full credit was granted. Students in
the spring course posted their questions to the discussion board and pra
c
tice problems, which were answered primarily b
y the instructor.
There were 5 topics considered for the discussion board problems: no
r
mal distribution, hypothesis testing using z and t, independent samples t

test, dependent samples t

tes
t, and correlation/regression.
An example of a
discussion board as
signment, the independent samples t

test, required a
group to analyze the data using SPSS, address assumptions, set up the null
and alternative hypotheses, interpret confidence intervals within the co
n
text of the example, make a decision about the null hyp
othesis, and make a
final conclusion within the c
ontext of the example. For this
topic of inte
r
est for this study (independent and dependent samples), the group in the
required

post course were responsible for posting the solutions and SPSS
files for the r
est of the class while other students (not in the group) and the
instructor could ask questions either related to the discussion board a
s
signment or participate in another eligible manner (i.e., discuss practice
problem, ask question, or make some other
co
ntent

related contribution).
Developing statistics learning at a distance using formal discussions
379
Approximately 52 optional postings were observed for students e
n
rolled
in the spring course compared to 268 required postings for the fall st
u
dents. A typical posting from each course that discussed differences in
one

and two

tail tests are presented for each course below:
(Spring 2005)
“I know that we talked about 2

tailed tests in our notes but how will the
question be worded to let us know that we need to do a 2

tail test? Are
there any key words or phrases to look for in th
e problem?”
(Fall 2005)
“I came up with the hypothesis as a two tail test because there was no
difference between the groups. When there is no difference between the
groups, then you use the two tail test which is Ho: Mu1=Mu2 and Hi: Mu1
is not equal to Mu
2. I got this from your lecture notes on Hypotheses of i
n
te
r
ests, so I hope I interpreted it correctly.”
Other contributions for independent/dependent samples and other topics
were similar for each course, in terms of the type, quality, and content of
ques
tions asked as well as any other comments posted. The majority of the
postings were comments and questions related to practice problems, SPSS,
verification of assumptions, and questions regarding what language to use
regarding: the decision about the null
hypothesis, statistical ev
i
dence, and
the interpretation of the confidence interval. The major diffe
r
ence between
the two courses, in terms of the postings, was the dispropo
r
tionate number
of contributions made (i.e., required vs. optional postings).
2.
3
M
ethod
2.
3.1
Sample
The study was conducted during the spring (n=22) and fall of 2005 (n=14)
at a large research university in the south. The same instructor, course d
e
sign, content, textbooks/software, discussion board assignments and pra
c
tice problems, c
omputer assignments, optional lectures, and tests were
used for both sections. One difference was the way in which the discu
s
sion
boards were utilized. Students in the fall course were required to partic
i
pate and received a post grade for their contributio
n while the spring st
u
dents were allowed to work on the discussion board assignments, pra
c
tice
problems, and post questions/comments as an option.
All graduate students in the College of Education were required to take
this introductory statistics course a
nd
all
students enrolled in both se
c
tions
380
Jamie
D. Mills
elected to take this course online (there was an optional face

to

face course
also available). The course is normally offered in the evenings (i.e., 6

9
pm) to primarily full

time working graduate students making p
rogres
s
ing
toward the Doctor of Philosophy degree. Approximately 82% (n=18) of
the students indicated that their GPA was between 3.5
–
4.0 for the spring
course while 85% indicated the same average range for students enrolled
in the fall course. As indicat
ed previously, there was no random assig
n
ment of students to either section; the students elected to enroll in the
o
n
line section of this course during the spring and fall semesters of 2005.
2.
3.2
Student performance and evaluation
In both courses, there w
ere 3 computer assignments, a midterm examin
a
tion (in

class), and a final examination (in

class). Assignment 1 was d
e
signed to introduce students to SPSS; therefore, this assignment only r
e
quired that students input data in
to
SPSS and generate the output.
Assignment 2 required students to have an understanding of basic descri
p
tive statistics, through computing hand calculations and using SPSS to i
n
terpret graphs and measures of central tendency and variability. Assig
n
ment 3 presented two research scenarios:
one for independent samples and
one for dependent samples. This assignment required students to input data
and generate the output, answer questions related to assumptions, write
null and alternative hypotheses, and read, report, and understand related
st
atistics from the SPSS output. Both Assignments 2 and 3 followed the
postings, comments, and questions of the related discussion board assig
n
ments and practice problems for both courses. Therefore, these two a
s
signments were two of the dependent measures f
or this study. Other v
a
ri
a
bles of interest for this study included a midterm, which covered topics
from the introductory material up through the normal distribution. This test
included objective (i.e., true/false, multiple choice) and short answer (i.e.,
h
and calculations) problems, as well as excerpts from the SPSS ou
t
put, in
which the students were required to demonstrate their ability to r
e
port and
interpret the statistics. Finally, the final examination, directly r
e
lated to the
overall objectives for th
e course, was a series of research scenarios related
to the following tests: one

sample z

test, one

sample t

test, independent
samples t

test, dependent samples t

test, correlation, r
e
gression, and chi

square. For each scenario, the students were required
to address the a
s
sumptions, set up the null and alternative hypotheses, find the calculated
statistic by hand, make a decision about the null hypothesis, and make a f
i
nal conclusion within the context of the research scenario. This was also an
Developing statistics learning at a distance using formal discussions
381
in

class tes
t (done by hand
–
no SPSS) and followed a comprehensive r
e
view of each test in the discussion area as well.
Student performance was measured by the following variables: Assig
n
ment 2, 3, and the final examination grade, where scores for all
mea
s
ures
were a
ssigned from 0

100. The midterm grade was used as a covar
i
ate for
both groups. The end

of

semester student evaluations, 1

to

5 Likert

scaled
items (1=strongly disagree and 5=strongly agree) that measured student a
t
titudes toward the course and the instruct
or, were also consi
d
ered
.
ANCOVA was used to determine if differences existed between the two
classes for all variables of interest. The assumptions were investigated for
all measures. Although the normality assumption was violated for A
s
signments 2, 3, an
d the final examination, according to the Wilk’s statistic
(p < .001), the F

statistic is generally robust with regard to
this violation
[11]
. When examining the distributions, all three were negatively skewed.
In addition, the homogeneity of variance test
s, examined at the .10 level of
significance was tenable for all three measures.
Using the student’s midterm score as the covariate, the results r
e
vealed a
statistically significant difference between average adjusted scores for
A
s
signments 2 [
s
=85.2, s
s
=2.5;
f
=94.1, s
f
=2.0;
F(1, 33)=7.3, p<.011; η
2
= .182] and 3 [
s
=66.1, s
s
=6.4;
f
=94.2, s
f
=8.1;
F(1, 33)=7.1, p<.012; η
2
= .177] but not for the final examination [
s
=85.1, s
s
=3.3;
f
=88.1,
s
f
=4.2;
F(1, 33)=.307, p>.05; η
2
=
.009]. Both statistically significant results
represent large e
ffects, according to [12]
. It appears that the students in the
fall course performed better statistically on Assignments 2 and 3 than the
students enrolled in t
he spring course. In addition, although student att
i
tudes in the fall course were generally higher than st
u
dent attitudes in the
spring course, student attitudes were positive in both courses and revealed
no statistically significant differences between se
c
tions
(attitude results are
presented in full paper).
2.
4
Summary and concluding remarks
The preliminary results indicate that the students enrolled in the required
asynchronous discussion section performed better than students not i
n
volved in formalized
discussions. Specifically, students who were r
e
quired
to participate in required discussion board assignments and discussion
s
performed better statistically than students who were not required to pa
r
ti
c
ipate. In addition, these students performed better on
the final examin
a
tion, but not better statistically. Because there have been no empirical st
u
d
ies that focus on how effective asynchronous discussions might be utilized
382
Jamie D. Mills
in an online statistics course, this study presents some evidence that ind
i
cates that
discussions might offer one way to facilitate students learn
ing
statistics concepts online.
Although students in both courses were exposed to the same content and
course materials, the students in the fall course contributed more to the
discussion area, du
e to the fact that their postings were required. This i
n
creased involvement alone is potentially why the results may have di
f
fered. The required contributions were implemented as a way to ensure
that everyone stayed involved with the course materials and t
o determine
whether these kinds of contributions might make a difference in student
performance. In terms of the content of the contributions, there appeared to
be no differences in the type and quality of contributions made between
the two sections, despi
te the fact that the students in the fall course also r
e
ceived a grade for their posts. This could be due to the fact that the fall
students were not evaluated based on a “right”
or “wrong” solution, since
all
of the solutions were provided for both course
s. Thus, requiring partic
i
pation appeared to influence attitudes and academic performance in this
study. The lack of randomization and the fact that the sections were not
taught simultaneously in the same semester are also limitations of this
study.
The re
sults of this study not only lends support to previous work regar
d
ing the notion that students benefit academically when they are actively
involved with others and engaged with the course materials
[7

8]
, but it a
l
so extends this notion to the online cours
e; it further corroborates the pr
e
vious research that claims asynchronous discussions can assist st
u
dents in
progressing through different levels of learning online
[9

10]
; and the r
e
sults might also be extended to learning in an online statistics course.
However, additional descriptive and empirical studies and results will be
needed in order to further advance our knowledge and understanding of
these teaching and learning practices online. Although there are many
more questions than answers at this point
about teaching statistics online, it
is hoped that our results and experiences might contribute to the scarce r
e
search literature in this area, as well as encourage further pedagogical d
i
a
logue and empirical results about how to effectively and successfull
y d
e
liver these kinds of courses online. We hope our study begins this much
needed exchange.
References
1.
Accreditation and assuring quality in distance learning
.
(2002) Council for
Higher Education Accredit
a
tion, CHEA Monograph Series, 1, 5
Developing statistics learning at a distance using formal discussions
383
2.
Utts J, So
mmer B, Acredolo C, Maher M, Matthews H (2003) A study co
m
paring traditional and hybrid internet

based instruction in introductory stati
s
tics classes. Journal of Statistics Education 11(3), http://www.amstat.org/ pu
b
lic
a
tions/jse/v11n3/utts.html
3.
Ward B
(2004) The best of both worlds: A hybrid statistics course. Journal of
Statistics Education 12(3), http://www.amstat.org/publications/jse/v12n3/
ward.html
4
Arnold N, Ducate L (2006) Future foreign language teachers’ social and co
g
nitive collaboration in a
n online environment. Language, Learning, and Tec
h
nology 10(1), http://llt.msu.edu/vol10num1/arnoldducate/d
e
fault.html
5
Pawan F, Paulus T, Yalcin S, Chang C (2003) Online learning: patterns of e
n
gagement and interaction among in

service teachers. Language
, Lear
n
ing, and
Technology 7(3), http://llt.msu.edu/vol7num3/pawan/
6
Gal I, Ginsburg L, Schau C (1997) In: Gal I,
Garfield
J (
e
ds) The Asses
s
ment
Challenge in Statistics Education, IOS Press, pp 37

51
7
Mills J, Johnson E (2004) An
e
valuation of ActivStat
s
®
For SPSS
®
for Teac
h
ing and Learning. The American Statistician 58(3):254

258
8
Moore D (1997) New pedagogy and new content: The case of statistics
.
Inte
r
national Statistical Review 65(2):123

137
9
Schellens T, Valcke M (2005) Collaborative learning in a
synchronous discu
s
sion groups: What about the impact on cognitive processing? Computers in
Human Behavior 21:957

975
10
DeWert M, Babinski L, Jones B (2003) Safe
p
assages: Providing online su
p
port for beginning teachers. Journal of Teacher Education 54(4)
:311

320
11
Keppel G, Wickens T (2004) Design and
a
nalysis:
a
r
esearcher’s
h
andbook
.
Pearson/Prentice Hall
12.
Cohen J (1977)
Statistical power analysis for the behavioral sciences (Rev.
ed.). Academic Press
Chapter
3
D
istance teaching in the course of
technology
in
Senior High School
S
hi

J
er
L
ou
,
T
zai

H
ung
H
uang
,
Chia

Hung Yen
,
Mei

Huang
Huang
,
Y
i

H
ui
L
ui
*
Institute
of
Technological
and
Vocational Education National Pingtung
University of Science and
Technology
, T
aiwan
lou@mail.npust.ed
u.tw
Abstract.
In the
general outline of
T
echnology course of senior
high school
in Taiwan
,
several
topics are included. These curricula
emphasize inspiring the students’ creative design and producing
ability at the same time. However, in the face of teach
ing so many
different categories, the scientific and technological teacher may lay
particular stress on or attach undue importance to one thing but n
e
g
lect the other category to some extent because of being limited to
his own specialty. In order to improve
the problems mentioned
above, this research adopts the distance

education way in coordin
a
tion, combining teachers of different specialty and applying teac
h
ing equipment which the general high school has now at present.
A
f
ter teaching through this way in p
ractic
e
, the researchers inte
r
view the teacher in coordination and conduct the questionnaire i
n
vestigation to students. Results of this research are as follow:
(1)
Students give positive opinions on the whole feeling about the di
s
tance team teaching for th
e class of science and technology. (2)
Teachers a
p
prove students’ performance in the distance team teac
h
ing of the class of science and technology. (3) Present science and
technology classrooms lack of equipment, students and teachers i
n
dicated that insuff
icient equipment and space should be improved.
(4) Teachers and students hold positive and affirmative aspects of
the actuation of distance team teaching.
Keywords.
Distance education, Team Teaching
*
386
Shi

Jer
Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
3.
1
Introduction
In the 2006's general outline of l
iving
technology course of senior high
school
in Taiwan
,
the following topics are included: Technology of Co
m
munication, Construction, Production, Trans
po
r
ta
tion, Power and Energy,
Biology. These
curriculums
emphasize on inspiring the students
’ creative
design
and
producing
ability at the same time. However, in the face of
teaching so many different categories, the scientific and technological
teacher may lay particular stress on or attach undue importance to one
thing but neglect the other category to some exte
nt because of being l
i
m
ited to his own specialty.
The
researchers also face
those
problems
in the real
teaching
filed.
In
order to improve the problems mentioned above,
the
research
er
s
adopt the
distance

education
way in coordination, combin
e
teachers of d
ifferent sp
e
cialty and appl
y
teaching equipment which the general high school
s
own
at
present
to develop a concrete plan for being the basis of
transnational
team
teaching.
Based on the motives stated above, the purposes of this study include:
1.
To provide
the teaching example of distan
ce
team teaching for
teac
h
ers
.
2.
To
promote
teachers to use basic computer equipment at school for
conducting distance education.
3.
To understand students’ learning effectiveness via distance team
teaching.
4.
To provide suggestions
for conducting different team teaching.
3.
2
Literature
r
eview
3.
2.1
Team teaching
In Taiwan,
the general outline of 1
st
to
9
th
grades
curriculum alignments,
the content of that indicates the conducting of learning domains should
contain the spirit of inte
gration; teachers and governors should refer the
character of the class to conduct team teaching (Department for Education,
2006). Therefore, team teaching becomes a cynosure in education during
recent years.
According to the team teaching
, it
means teache
rs conduct a teaching
performance
in coordination. In order to
understand
the meaning
of team
Distance teaching in the course of technology in Senior High School
387
teaching objectively,
the
detailed descriptions of team teaching are as fo
l
lows.
Shaplin defines that team teaching is a kind of teaching styles, two or
more tea
chers work together to teach all or parts of courses [1].
Li
indicates that team teaching means two or more teachers
should
o
p
erate and integrate their own specialties to teach students in one or
several
domains [2].
Buckley
identifies
team teaching means
a group of teachers aim at d
e
signing a curriculum, schedule, and lesson plan in
coordination
. Then these
teachers have to teach students, to evaluate the result, to share their aspects
and to discuss things together [3].
Jeng
defines the team teaching mea
ns two or more teacher to form into a
teaching team, and they are
responsible for
teaching a group of students.
Teachers have to plan, to teach, to assess students, to evaluate instruction
in one or several domains [4].
Chen indicates that team
teaching
me
ans two or more teachers, or su
b
ject
teachers to
form into a teaching team. These teachers have to operate
their specialties to design lesson plan, to help students via different teac
h
ing way, to assess students
’
learning and activities in one or several d
o
mains [5].
Above
definitions
of team
teaching are
very similar, the researchers sum
up those definitions into a model as Fig
ure 3
.
1:
Fig.
3.1
.
The Model of Team Teaching
Team
teaching is
very suitable for teacher who just has single
specialty
to apply i
n their
teaching
, and it also needs many
specialized
teachers to
conduct their own
specialty
in the teaching process. However, Science and
Technology this kind of subject includes many learning domains, team
teaching is just
appropriate
for science and tec
hnology teachers who ca
n
not be proficient in each specialty.
388
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen,
Mei

Huang Huang, Yi

Hui Lui
According to the developmental history of team teaching, team teaching
is not a new teaching theory. Many schoolteachers have known that before,
but few teachers can conduct this kind of teaching
in the class. The reason
of this situation is imputed to the subjective and objective
viewpoints
in
the school environment [6]. O
n the other hand
, the
difficulties
of team
teaching are as follows [7],
“
It
’
s not easy to arrange co

teaching time
”
,
“
Media te
aching equipment, individual learning facility, classroom, and
learning space are very insufficient
”
,
“
The deficient motivation and
exp
e
rience
of attending
related conference and workshop
”
,
“
Need to spend
more time on discussing with co

teachers
”
,
“
Lack of
teaching
assistant”
,
“
Difficult to look for
commune
helping team teaching
”
,
“
Teaching plan is
always
changeable
owning to the commune and assistant are not easy to
match up the
schedule”
. Thus, this study is to
apply
team teaching to ope
r
ating with distan
ce education in coordination, then to find the
approach
to
solve problems.
3.
2.2
Distance
education
The concept
of distance education means teachers and students they
teach
and learn in a separated space, so teachers and students
should
pass
through man

ma
de
broadcast
media to send
information.
In a word,
it pr
o
vide
s
the interaction platform
for teachers and students [8]. Regarding to
the history of distance education, Moore indicated that
Correspondence
Education is the
foundation
of distance education, an
d this foundation
evolves into several styles such as broadcast, internet, and
videotext
with
the
movement
of media technology [9].The
developments of the current
distance education are
as follows [10],
1.
Instantaneous broadcast system of team teaching: One
main broadcast
room and several distance classrooms are
supplied
for this teaching
system.
Teacher conducts
instruction in the broadcast classroom, and
students have to
attend
the class
in the distance classroom. Teaching
assistant should capture the frame
s and sound to let teacher and
st
u
dents can
communicate
with each other
instantaneously
via High

speed
Internet
transmission.
2.
Virtual classroom teaching system (network teaching): The system is
to
apply
software to designing a
manageable
system and to mak
ing
use of this system to
create
the real classroom context. Network
teaching is not only an
assisted teaching
but also a
substitute
for
teacher
’
s real lecture in the classroom.
3.
Teaching system for instantaneous course taking: Students use co
m
puter and SE
T

TOP BOX of TV to get the teaching materials that
Distance teaching in the course of technology in Senior High School
389
they want to learn, then they can follow the learning speed of one
’
s
own to handle and control the
transmission
process of conducting di
s
tance learning.
Moreover,
Instantaneous
broadcast system of team te
aching provides
vivid and lively class quality, but the shortcoming is this system setting b
e
longs to a kind of
high consumption
so that some general organization
cannot be able afford that;
in addition
, it needs extra professional to m
a
n
i
pulate the teachi
ng
equipment
. Compare general learner with distance
ed
u
cation learner, distance education learner is more active and voluntary
than
traditional
learner.
In summary, most junior and senior high school students fail in
ind
e
pendent
leaning. Therefore, instant
aneous broadcast system of team teac
h
ing is a synchronous distance education. In this system, teacher can guide
students to learn everything instantaneously, and the effectiveness
of that
should be superior to another system. F
or that reason
, this kind of
distance
education is adopted and conducted in this study.
Nevertheless
, some junior and senior high school cannot be able to a
f
ford the pay for professional equipment and specialist.
Therefore
, this
study is to investigate and understand the advantages of
instantaneous
broadcast system of team teaching can be achieved or not by available
teaching staff and equipment.
3.
3
Research
m
ethod
3.
3.1
Research subject
The subjects of this study were 43 students of one class at National Pin
g
tung Girl
’
s Senior High S
chool and 40 students of one class at
Taipei M
u
nicipal Jianguo High School. All together, there are
83
students
present
at
this study. The schedules of Science and Technology class are conducted
at the same time.
3.
3.2
The way of conduct
The study was bas
ed on distance team teaching, and adopted synchronous
method to conduct the class. For that reason, the
researcher
s
have
to look
for
two
t
eachers who live in different counties but they can conduct the
class to their students at the same time.
Designated t
eachers sh
o
uld co
n
390
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
duct these two classes
in 3 weeks (6 hours). The
researchers also
have to
interview teachers and to
carry
out questionnaire survey to understand the
effectiveness of synchronous distance education.
3.
3.3
Research tools
The research tools
of this study are questionnaire and the structural que
s
tions for interview, so the
following
are the descriptions about the content
and the
amendment
of them.
(1)
Questionnaire and the content of the interview question:
According to the literature review a
nd researchers
’
discussion, the
factors of synchronous
distance
team teaching are as follows:
1.
The learning effectiveness of
influence
part to teacher
’
s team teac
h
ing.
2.
The learning effectiveness of influence part to the synchronous di
s
tance v
ideotex
class.
3.
The whole perception of the class of distance team teaching.
After that
, the researchers draw up the items for each question, and the
question totals to 21. Owning to students
’
backgrounds are very clear and
definite, the researchers just list students
’
s
chool name and gender to re
p
resent students
’
file. Finally, the researchers use the factors as the unit and
apply the items of questions as the descriptions to be the structural que
s
tions for teachers to conduct interview with their students.
After finishi
ng the first draft of questionnaire, the researcher requests
adviser to revise some useless
and
unclear questions; then
those
revised
questions are arranged into
“
The
learning
effectiveness
questionnaire
of
distance
team teaching
on the subject of
science
and
technology
in senior
high school
”
and
“
The interview
question
of distance team
t
each
ing
on the
subject of science and technology in senior high school for teachers
”
(2)
The way for editing
questionnaire
The questions for interviewing students
’
learnin
g
effectiveness should
match with the factors of questions and students
’
understanding for voc
a
b
ulary (subjects) as the main principles. 18 questions of
questionnaire
are
based on Likert 5 points scales to proceed, and the descriptions of que
s
tions are
“
ve
ry agree
, agree, average, disagree, very disagree”
these five
levels. Another three questions are opened questions, and the content of
that is based on
investigating
the whole things.
Distance
teaching in the course of technology in Senior High School
391
3.
3.4
Data collection, processing and an
alysis
The subjects of this stu
dy come from Northern and Southern Taiwan. In
order to conduct questionnaire
easily
, the researcher adopts on

line que
s
tionnaire for students to answer the questions on line. The
researcher
would
transform the rough data into SPSS for setting up the file,
and use
SPSS to carry out
statistical
analysis via the
method
of descriptive
stati
s
tics
. The researchers would also interview teachers and record teacher
s’
thought and
feeling
about this teaching to compare with students
’
response
to the questions.
3.
4
Cu
rriculum
d
esign
3.
4.1
The title of the class
Information
Communication Technology
(ICT)

A story
fan

tan
.
3.
4.2
P
urpose
of the curriculum
1.
The
available
teaching equipment at senior high school is the
found
a
tion
to conduct synchronous across

school teachin
g in coordination
for different
specialized
teachers at different school can conduct
teaching in coordination.
2.
Students who locate at different area can study and
work together
with
each
other
via internet and
Videotex
to shorten the distance b
e
tween urba
n and rural, then to achieve the goal of
multiple

learning.
3.
4
.3
Goal of teaching
1.
Students can be able to shoot a film and use the image editing sof
t
ware to
revise
or to capture the frames from the film.
2.
To
carry out
the system model
of
ICT (input
–
pro
cess

output model
).
3.
To apply ICT to teaching environment as well as to achieve the pu
r
pose of
assimilat
ing information into
instruction
.
4.
To
make use of distance
team teaching to
promote
students
’
learning
interest.
392
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
3.
4
.4
Location
and place
The distance tea
m teaching was held in two schools. One school is located
at Southern Taiwan: The scientific and technological classroom at National
Pingtung Girl
’
s Senior High School. The other school is
located
at Nort
h
ern Taiwan: The scientific and technological classr
oom at
Taipei Munic
i
pal Jianguo High School
. It is about 400 km from Pingtung to Taipei.
3.
4
.5
The
plan
of curriculum
3.
4
.
5
.1
Teaching equipment
a. Hardware:
The researchers just list the equipment at one school on Table
3
.
1, but the
researchers also set
up the same equipment at the other school while co
n
ducting the class. Fig
ure
3
.2 illustrates
the structure model for distance
team teaching
.
Table
3.
1
.
the equipmentat at reasearched school
T
i
tle/Name
Function and Use
Qua
n
tity
FTP
S
erver
Teachers can use
original FTP sever in the school or to
use the available computer to set up the FTP server
with Linux/Windows system. Teachers can open the
upload space for students to make film or to save the r
e
lated file before le
c
turing the class.
1
Media
S
erver
Tea
chers can use available computer or professional
server computer with Linux/Window system to conduct
the class. If the
expenses
are not enough, the researc
h
ers can neglect or set it in the FTP sever. This machine
is used for broadcasting the film.
1
Equi
pment for
I
nternet
The main factor of
Videotex
quality is the frequency
channel
, so
optical fiber
line is just the best tool for
that. If the expenses are not
enough
, ADSL, cable, and
school ne
t
work can also be utilized in this teaching.
1
Computer with
Vide
o
tex
The
functions
of Computer and Notebook are to project
the image onto the screen and to connect web cam.
1
Internet
V
ideo
C
a
m
era
To film the version of teachers and students, and to
send the frames to the other side.
At least 2
Mic
ro Phone
To
receipt the
voice of
teachers and students, and to
send the voice to the other side.
1
Projector and
To
show
the version from the other side
1
Distance teaching in the course of technolog
y in Senior High School
393
P
rojective
C
u
r
tain
Speaker
To broadcast the voice from the other side.
1
Students
’
=
C
o
洭
puter
=
cor=
student
s=to=m
~ke=the=filmK=
=
pever~l
=
or=
灲
漭
vided=by=
students
=
aigit~l=
s
ideo
=
䑖
=
cor
=
students=to=m~ke=their=own=
work
K
=
puggestionsW=
=
qhe=as=with=e~rd=discs=~nd=
jemory=ptick
=
~re=the=b
愭
sis=for=students=to=intercept=the=file=~fter=they=
connected=
vi~=~=co
m
puter
K
=
ff=the=users=
u
tilize
=
jini=as=qype

asI= the=users=h~ve=to=
use=fbbbNPVQ= to=intercept=the=fileK
=
qo=~void=using=the=as= recorded=by=asaI= bec~use=the=
process=of=
form~tting
=
is=very=complic~tedK=
=
pever~l=
潲
=
灲
漭
vided=by=
students
=
aigit~l=C~
m
er~=
=
cor
=
students=to=m~ke=their=own=wor
欮
=
puggestionsW
=
qhe=~uto

c~mer~=is=the=
found~tion
=
for=students
=
to=shoot=
motionless=picture=
~nd=movement=pictureK
=
EOF=qhe=
function
=
of=the=
single

lens=reflex=c~mer~
=
is=
扥
琭
ter
I=but=th~t=is=
just
=
for=shooting=motionless=pictureK
=
pever~l=
or=
灲
漭
vided=by=
students
=
fbbbNPVQ=
C
~rd=
~nd=NPVQ= rpB
=
qo=c~pture=some=fr~mes=from=the=filmK= oecent=mC=~nd=
kB=~re=reg~rded=~s=the=
b~sic
=
equipmentX
=
the=new=co
洭
puter=should=be=
~lloc~ted
=
with=the=NPVQ=rpBK
=
pever~l
=
Fig.
3.2
.
The Structure Model for
Distance
Team Teaching
b.
Software:
The
following software
is for using in this teaching, but some
software
can
be downloaded on

line. It depends on teachers
’
need to
adjust the
setting.
394
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
Table
3.2
.
The software for using on this reasearch
Title
/ Name
Function and
d
escription
Memo
Yahoo
M
e
s
s enger
The
s
oft ware for
Videot ex t hat
can be
downloa
d
ing
on t he int ernet.
Choos e one
MSN
The
s
oft ware for
Videot ex t hat
can be
downloa
d
ing
on t he int ernet.
S
kype
The
s
oft ware for
Videot ex t hat
can be
downloa
d
ing
on t he int ernet.
Micros oft
Movie
M
a
ker
The
s oft ware
for making film or image, and t he
co
n
trolling
interface is easy to
utilize
. XP has this sof
t
ware in it, but the function is too simple.
Gold
W
ave
The program for recording and
edit
ing the
files
of
voice message. It can be downloaded from
http://
www.goldwave.com
Shareware
WinAvi
Converter
The program for
transforming
Media file, the
a
vi,
mpg, wmv, mov these files could be transformed
i
n
terchangeably
. That can support the formats of DC
and DV. This program can be downloaded form
http://w
ww.winavi.com
Shareware
Filezilla
FTP software along with Server and Client these two
editions. The function is completed. This program
can be downloaded from
http://filezilla.sourceforge.com
Shareware
3.
4
.
5
.2
C
ontracted
lesson plan
The lesson plan is
developed for three weeks.
I
nput (Week one)
process
(Week Two)
output (Week three)
Table
3.3
.
The lesson plan of this research
Week
Content
Equi
p
ment
M
emo
One
20
Min
20
Min
80
Min
1.
Description of activity:
(1)Students at National Pin
g
tung Girl
’
猠
penior=eigh=pchool=~nd=
q~ipei= j unic
椭
p~l=gi~nguo=eigh=pchool
=
to=choose=the=
loc~tion=~nd=the=m~teri~ls=for=
shoot
K
=
EOF=qhe=
subject=m~tter
=
for=shoot=will=u
瀭
lo~d=to=
the=cqm
=
serverI=~nd=the=works=of=
students=~t=two=schools=would=be=s~ved=
in=document=styleK=
=
⠳
F
=
ptudents=~t=two=schools=h~ve=to=use=
the=present=m~teri~ls=to=m~ke= ~=film= vi~=
the=processing=of=im~geK= qhe=length=of=
=
aC=or=as
=
=
=
cqm=perver
=
=
=
=
mCL
=
kot
e
扯潫
=
qhe=softw~re=
of=film= edi
t
ingK
=
qhe=
specific
愭
tion=for=picture=
楳
=
NMOQxTSUI=
~nd=the=film= file=
should=be=wmvK
=
=
ptudents=c~n=
freely=~dd=
~side
Ⱐ
捡
p
tion
Ⱐ
~nd=b~ckground=
m
u
sic=for=their=
filmK= =
=
Distance teaching in the course of technology in Senior High School
395
the film is 2min. The
subject
matter and
the scrip of the film should be made by
students.
2.
Start to collect data
Two
100
Min
Film editing. Upload the works on
the i
n
ternet
.
The format of
film should be
wmv.
Three
100
Min
Share moment. Students at these two
schools have to present their works on the
stage
.
PC.
Web
Cam,
Communic
a
tive
software,
M
i
cro phone
After broa
d
cas
t
ing the film,
students have to
present their
thoughts or to
discuss
on

line
.
3.
5
Findings and
d
iscussion
3.
5.1
The questionnaire for learning
effectiveness
After processing the data, the result of the closed questions are as follows:
(1) The impact part on learning
effectiveness
of teachers
’
team
t
eaching
According to the
average (
M>4, SD<1) and mode value, most students
give positive opinions on the atmosphere in the class, the
content
of class,
and different teachers
’
lecturing. Not a few students give
agree (
4) to the
content of the class and the
support
of making works. Most students give
very agree (5) to the atmo
s
phere in the class, and interesting feeling in the
class
.
Just one or two students give
negative
opinions, the
percentage
of
that is 1~2%. The
question of this class is
more interestin
g than
general
class;
none of students give negative opinions.
(2) The impact part on leaning
effectiveness
of distance
V
ideotex
synchronous
class
In this part, students agree with the way of distance
V
ideotex
synchronous
class. Because students can encou
rage and interact with the other group
students via internet,
their
opinions on learning motivation, works, and the
atmosphere in the classroom, present positive result (M>4,SD<1).
Hence
,
most
students
give
agree (
4) and very
agree (
5) to this section. Abo
ut the
negative opinions on this se
ction
, only 1~3 students express disagree, and
396
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
the
percentage
of that is around 1~3%. The
question of “the way of di
s
tance education makes
me understand another students
’
thinking to the
same class
”
, nobody expresses nega
tive opinion. It
obv
i
ously
dictate
s
this
class
inspires students
’
ability via distance

v
ideotex
synchr
o
nous
class.
(3) The entire experience on the distance team teaching program
The results of students
’
questionnaire indicate that students do learn a lot
from this class and like this class (M>4, SD<1), and most students express
agree (
4) and very
agree (
5). The negative opinion on this section only 1~4
students give disagree, the
percentage
of that is situated at around 1~4%.
Nobody gives negative opinions
on the question
of “
Overall, I like this
class
”
. It indicates that all students like the way of this class.
(4) The opened questions for the whole perception of the class of
distance team teaching
In the process of learning program, what do you think tha
t you learn
greatly from it?
Students
’
answers in this section are indicated four kinds of responses in
the following.
a.
Interact with students
at other
school.
(
68.67%)
b.
Learn the technique for film editing.
(
45.78%)
c.
Like and understand science
and technolo
gy
(
8.43%)
What do you think the biggest difficulty you faced in the process of
learning program?
Students
’
answers in this section are indicated four kinds of responses in
the following.
a.
Ability for shoot and making film is insufficient.
(
43.37%)
b.
The
insu
fficient
and low quality equipment.
(
31.33%)
c.
The class time
is
insufficient
.
(
30.12%)
d.
The conflict in the group.
(
4.8%)
In this learning process, what do you think that can be improved fu
r
ther? (Facility, program, etc)
Students
’
answers in this section a
re indicated four kinds of responses in
the following.
a.
Equipment.
(
74.7%)
b.
Technique
and ability for shoot and editing.
(
9.6%)
c.
The class time is too insufficient.
(
14.56%)
d.
The content of the class.
(
7.2%)
e.
Pretty good.
(
6.02%)
Distance teaching in the course of technology in Senior High School
397
3.
5.2
Interview
participant te
achers
The interviewees are three teachers in this distance
education
, and the r
e
searchers use A, B, C to represent them.
(1) The impact on learning
effectiveness
of
teachers’
team teaching:
Teac
her A:
About creating the learning atmosphere, lesson plan an
d pre
p
aration are very important for the class except for the class management.
However, the lesson plan
should
rely on teachers in the group of team
teaching to arrange,
then
to make the process of the class more
smooth
and
successful.
In the process of t
his class, the teachers at National Pingtung Girl
’
s S
e
n
ior High School and
Taipei Municipal Jianguo High School
have to draw
up the lesson plan, to correct the shortcomings of the lesson plan by three
times
distance discussions. A
fter that
, teachers
dig up
common consensus
and then conduct it in the real teaching process. C
onsequently
, students
would have an
authority
to follow, and
would
not occur
disarticulation
b
e
tween teachers and students.
Finally, I know students like this class from students
’
attitu
de
and r
e
sponse in the class, they do involve in this class. Meanwhile, they can also
finish the task on time. Thus, I think this distance team teaching rises
up
students’
learning effe
c
tiveness.
Teacher B:
Students are curious about new things, and also i
nterested in
the looks of teachers or female classmates. Therefore, can this teaching
method be able to help students understand the content or not? I hold
ne
u
ter
opinion on it.
Besides
,
owning
to the l
imitation
in
bandwidth
and
equipment, both sides can n
ot have
international
atmosphere in the class.
Thus, if we had more
high

quality
DV and
bandwidth
, that will improve
students’
ability in
making
their works.
(2) The impact on learning
effectiveness
of
distance Videotex
synchronous
class
T
eac
her A:
I neve
r teach this kind of class, but this class is very good for
students. It is very good for promoting students
’
learning
motivation
. St
u
dents like to interact with students at other
school
, and they have
conf
i
dence
in their works. Nevertheless, students
ca
n
understand different
thoughts and viewpoints from others, these
thoughts
and viewpoints i
n
spire students
’
competitiveness
and
actuation
. Beside, after
br
ain
storming
students
’
achievement is really good.
398
Shi

Jer Lou, Tzai

Hung Huang, Chia

Hung Yen, Mei

Huang Huang, Yi

Hui Lui
Students
attend
the class with students at other sc
hool, this kind of env
i
ronment makes good atmosphere for class. Maybe this kind of
novelty
class effects students
’
learning motivation and atmosphere; therefore, if
teachers can
innovate
their instruction, students
’
motivation
and atmo
s
phere can be
continu
ed
.
Finally, it is the distance videotex
synchronous
class better than
general
class? I hold
qualified
attitude, because different teaching method makes
different leaning effectiveness. This teaching method is not
superior
to
other teaching methods, but it
is still a good teaching method for students.
Teacher B:
This is a cooperation
of a
school for boys and a school for
girls, and this way drives
boys to present good learning attitude toward the
class
and makes good atmosphere in the class. This kind of c
lass is more
interesting than the atmosphere in general class, and students
’
learning e
f
fectiveness is better than the learning effectiveness in general class.
H
owever
, the class time is too
short;
there is no time for students to
share their feelings and
thoughts in the class. Otherwise students can u
n
derstand
more
and different thoughts from others.
About the cooperation
of a
school for boys and a school for girls
,
boys
are free

will to present good learning motivation in the class, class atmo
s
phere is v
ery good, and this class is more interesting than
others
.
(3) The section of the
complementary
to teachers
’
specialty
Teacher A:
In this class, I think different teachers
’
specialties are
co
m
plementary
to each
other, because
a teacher can not be
an experti
se of ev
e
rything
. Science and technology this subject includes many professional
domains, but a teacher can just be
conversant
with
some certain domains.
In order to make
students
can study in this field completely, team teaching
complement
s this
deficienc
y
.
Besides, team teaching promotes my professional ability.
Teaching be
n
efits teachers as well as students
, the key point of that is the interaction b
e
tween teachers and students. Team teaching can inspire new thinking and
creative teaching method for teac
hers, and it is really good for teachers
’
professional
. Thus, I think I really do learn a lot from this class.
Teacher B:
In the process of team teaching, participant teachers have
chance to
learn from each other's work
, it can also achieve the
function
of
complementary
. B
eforehand
c
ommunication
and
preparation
are very
i
m
portant for
team
teaching;
teachers can dispose of the problems by creative
brainstorming and problem
solving
, and then achieve the
function
of
co
m
plementary
.
Distance teaching in the course of technology in Senior High School
399
I am sure this kind of teachi
ng can promote my professional, and the
thought and attitude of teacher A is much appreciated by me. Teacher A
has sufficient knowledge about
network management;
he helps us directly
and
operates
a new teaching field for us.
(4)
The entire experience on th
e distance team teaching program
Teacher A:
In the teaching process, because of the
bandwidth
is not
enough, the effect of version and voice is
terrible
. This problem
obstruct
s
the chance of
interaction
. Next time, the
transmission
equipment (web
cam, comp
uter,
and speed)
, completed lesson plan, students
’
film editing
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