Analysis and Proposal about the Effect of Time, Types of Subject and Types of Room Factor to the Studentsâ Concentration.
Message from the APIEMS President
Greeting and a warm welcome to the participants of the 15th Asia Paciic Industrial Engineering
and Management Systems Conference. Started in 1998, APIEMS has grown to become the premier
conference for industrial engineering and management systems in the region with participants from
all around the world. The main theme of this year conference: “Sustainable Industrial Systems and
Big Data Management”, is an attempt to address the balance among economic and technical development, social development, and environmental protection in this fast changing world.
I congratulate and thank Prof. Dr. Chi-Hyuck Jun, the conference chair, whose leadership made this
APIEMS 2014 conference possible. We are also grateful for the enthusiastic support of APIEMS
from the KIIE and the Korea research community.
On behave of the Asia Paciic Industrial Engineering and Management Society, I wish you a successful conference with many thoughtful discussions and debates with old and new friends.
Professor Voratas Kachitvichyanukul
APIEMS President, (2013-2014)
Professor of Industrial & Manufacturing Engineering
Dean, School of Engineering and Technology
Asian Institute of Technology, THAILAND
Message from the General Chair
Welcome to APIEMS 2014 in Jeju City, a beautiful island located at the most south of Korea. It is
our great pleasure to organize this conference, which is supported by Korean Institute of Industrial
Engineers (KIIE). APIEMS conferences have rapidly emerged as an important forum for exchange
of ideas and information about latest developments in the ield of industrial engineering and management systems among professionals mostly from Asia-Paciic countries. APIEMS 2014 conference encourages contributors to address the topical theme: Sustainable Industrial Systems and Big
Data Management. Papers will represent the latest academic thinking and successful case examples.
The wider audience will beneit from the knowledge and experience of leading practitioners and
academics in this area.
The conference seeks research contributions from researchers, educators, modelers, software developers, users and practitioners. We hope that you enjoy participating in APIEMS 2014 and staying
in Jeju.
Professor Chi-Hyuck Jun
General Chair, APIEMS 2014
Industrial & Management Engineering
POSTECH, Korea
Conference Committee Members
Conference Committee
• Conference Chair
• Chi-Hyuck Jun (POSTECH, Korea)
• Honorary Chairs
• Hark Hwang (KAIST, Korea)
• Mooyoung Jung (UNIST, Korea)
• Kap Hwan Kim (Pusan National Univ., Korea; President, KIIE)
• Conference Co-Chairs (International Advisory Board)
• Abdul Hakim Halim (InstitutTeknologi Bandung, Indonesia)
• Anthony Shun Fung Chiu (De La Salle University, Philippines)
• Baoding Liu (Tsinghua University, China)
• Bernard Jiang (National Taiwan University of Science and Technology, Taiwan)
• C. J. Liao (National Taiwan University of Science and Technology, Taiwan)
• Che-Fu Chien (National Tsing Hua University, Taiwan)
• Du-Ming Tsai (Yuan Ze University, Taiwan)
• ErhanKozan (Queensland University of Technology, Australia)
• HirokazuKono (Keio University, Japan)
• Jin Peng (Huanggang Normal University, China)
• Jinwoo, Park (Seoul National Univ., Korea)
• Katsuhiko Takahashi ( Hiroshima University, Japan)
• Kazuyoshi Ishii (Kanazawa Institute of Technology, Japan)
• Kin Keung Lai (City University of Hong Kong, Hong Kong)
• Mao Jiun Wang (National Tsing Hua Univeristy, Taiwan)
• Min K. Chung (POSTECH, Korea)
• Mitsuo Gen (Fuzzy Logic Systems Institute, Japan)
• P. L. Chang (Feng Chia Uni)
• Shouyang Wan (Chinese Academy of Sciences, China)
• Tae Eog Lee (KAIST, Korea)
• Takashi Oyabu (Kanazawa Seiryo University, Japan)
• VoratasKachitvichyanukul (Asian Institute of Technology, Thailand)
• Yon-Chun Chou (National Taiwan University, Taiwan)
• Young Hae Lee (Hanyang University, Korea)
• ZahariTaha (Universiti Malaysia Pahang, Malaysia)
Organizing Committee
• Technical Program Chairs
• Il-Kyeong Moon (Seoul National Univ., Korea)
• Byung-In Kim (POSTECH, Korea)
• Publication Chairs
• Jaewook Lee (Seoul National Univ., Korea)
• Hosang Jung (Inha Univ., Korea)
• Publicity Chairs
• Chulung Lee (Korea Univ., Korea)
• Yoo-Suk Hong (Seoul National Univ., Korea)
• Sponsorship Chairs
• Minseok Song (UNIST, Korea)
• Young Jin Kim (Pukyong National Univ., Korea)
• Exhibition Chairs
• Hyunbo Cho (POSTECH, Korea)
• Yonghui Oh (Daejin Univ., Korea)
• Finance Chair
• Dong-Ho Lee (Hanyang Univ., Korea)
• Award Chairs
• Kyung sik Lee (Seoul National Univ., Korea)
• Young Jae Jang (KAIST, Korea)
• Local Arrangement Chair
• Dong-Cheol Lee (Jeju National Univ., Korea)
Conference Sponsors
The Korean Federation of Science
and Technology Societies
DOOSAN
SAS KOREA
Pohang University of Science
and Technology
The Korean Operations Research
and Management Science Society
THE KOREAN OPERATIONS RESEARH
AND MANAGEMENT SCIENCE SOCIETY
Keynote Speech
Keynote Speech I
Research Issues in Future Logistics
Oct 13 (Monday) 11:00-12:00
Room: Ramada-1
Chung– Yee Lee
Hong Kong University of Science and Technology, China
Dr. Chung-Yee Lee is Chair Professor/Cheong Ying Chan Professor of Engineering in the Department of Industrial Engineering & Logistics Management at Hong Kong University of Science and
Technology. He served as Department Head for seven years (2001- 2008). He is also the Founding
and Current Director of Logistics and Supply Chain Management Institute. He is a Fellow of the
Institute of Industrial Engineers in U.S. and also a Fellow of Hong Kong Academy of Engineering
Science. Before joining HKUST in 2001, he was Rockwell Chair Professor in the Department of
Industrial Engineering at Texas A&M University. He worked as a plant manager and also had few
years consulting experience in Taiwan. In the past thirty years he has engaged in more than forty
research projects sponsored by NSF, RGC, ITF, IBM, Motorola, AT&T Paradyne, Harris Semicon
ductor, Northern Telecom, Martin Marietta, Hong Kong Air Cargo Terminal, Hongkong International Terminal, Philips Medical, ...,etc.
His search areas are in logistics and supply chain management, scheduling and inventory management. He has published more than 130 papers in refereed journals. According to an article in Int. J.
Prod. Eco. (2009), which looked at all papers published in the 20 core journals during last 50 years
in the ield of production and operations management, he was ranked No. 6 among all researchers
worldwide in h-index.
He received a BS degree in Electronic Engineering (1972) and a MS degree in Management Sciences (1976) both from National Chiao-Tung University in Taiwan. He also received a MS degree
in Industrial Engineering from Northwestern University (1980) and PhD degree in Operations Research from Yale University (1984).
Keynote Speech
Keynote Speech II
Data-Driven Decision Making in Manufacturing:
Lessons Learned and Future Opportunities
Oct 14 (Tuesday) 11:00-12:00
Room: Ramada-1
Ronald G. Askin
Arizona State University, USA
Ronald G. Askin, Ph.D., is a Professor of Industrial Engineering and Director of the School of
Computing, Informatics, and Decision Systems Engineering at Arizona State University. Professor
Askin received his B. S. in Industrial Engineering from Lehigh University followed by an M.S. in
Operations Research and PhD in Industrial and Systems Engineering from the Georgia Institute of
Technology. He has over 30 years of experience in the development, teaching and application of
methods for systems design and analysis with particular emphasis on production and material low
systems. Other interests include quality engineering and decision analysis. He has published over
120 journal and conference proceedings papers in these areas.
Dr. Askin is a Fellow of the Institute of Industrial Engineers (IIE) and serves as Editor-in-Chief
of IIE Transactions. He has served on the IIE Board of Trustees, as President of the IIE Council
of Fellows, Chair of the Association of Chairs of Operations Research Departments (ACORD)
Chair of the Industrial Engineering Academic Department Heads (CIEADH) and President of the
INFORMS Manufacturing and Service Operations Management Society (MSOM). He was also
General Chair of the 2012 INFORMS Annual Conference. His list of awards includes a National
Science Foundation Presidential Young Investigator Award, the Shingo Prize for Excellence in
Manufacturing Research, IIE Joint Publishers Book of the Year Award (twice), IIE Transactions on
Design and Manufacturing Best Paper Award (twice), the Eugene L. Grant best paper award from
The Engineering Economist, and the IIE Transactions Development and Applications Award.
Keynote Speech
Keynote Speech III
Big Data Management
Oct 14 (Tuesday) 13:00-14:00
Room: Ramada-1
Sungzoon Cho
Seoul National University, Korea.
Sungzoon Cho is currently professor of Industrial Engineering Department, the director of Data
Mining Center at Seoul National University (SNU) and a member of Government 3.0 Committee
of Korean government. He is on the editorial board of International Journal of Operations Research
and Information Systems and International Journal of Cognitive Biometrics. He served as the presi
yundai Motors, Hyundai Heavy Industries, POSCO, Daewoo Shipbuilding and Marine Engineering, LG Electronics, Doosan Infracore, SK Hynix, SK Telecommunication and CJ. He advised nine
PhDs and 56 Master students. He teaches Data Mining and Computational Intelligence at SNU as
well as at irms. He received BS and MS in Industrial Engineering at SNU. He won a Fulbright
Scholarship to obtain Masters and PhD at University of Washington in Seattle, US, and University
of Maryland in College Park, US, respectively.
Conference at a Glance
Oct 12 (Sunday)
10:00-18:00
Oct 13 (Monday)
08:00-17:00
Registration
08:30-10:10
Technical sessions
MA
10:10-10:30
Coffee break
10:30-11:00
Opening addresses :
APIEMS President,
KIIE President,
General Chair
08:00-17:00
Technical sessions TA
10:40-11:00
Coffee break
11:00-12:00
Keynote speech I
(Prof. Chung-Yee Lee:
Research issues in
Future Logistics)
11:00:12:00
Keynote speech II
(Prof. Ronald Askin:
Data-Driven Decision
Making in
Manufacturing)
12:00-13:30
Lunch
12:00-13:00
Lunch
13:00-14:00
Keynote speech III
(Prof. Sungzoon Cho:
Big Data
Management)
14:00-14:20
Coffee break
Registration
Technical sessions
MB
Excursion
15:30-15:50
Coffee break
14:20-16:00
Technical sessions
TB
15:50-17:50
Technical sessions
MC
16:00-16:20
Coffee break
16:20-18:00
Technical sessions
TC
13:00-18:00
Poster Session
18:30-21:00
General Reception
Registration
18:00-20:00
Welcome
Reception
Registration
08:40-10:40
13:30-15:30
13:00-17:20
Oct 14 (Tuesday)
Oct 15 (Wednesday)
08:00-12:00
Registration
08:30-10:10
Technical sessions
WA
10:10-10:30
Coffee break
10:30-12:10
Technical sessions
WB
12:10-13:30
Lunch
Oct 12 (Sunday)
10:00-18:00
Registration
13:00-17:20
Excursion
18:00-20:00
Welcome Reception
Oct 13 (Monday)
Registration
08:00-17:00
Room
08:30-10:10
Session
name
Paper #
Mara
Biyang
Udo
Chuja
Ramada-1
Ramada-2
Ramada-3
Ramada-4
Halla(8F)
Technical sessions MA
MA1
MA2
MA3
MA4
MA5
MA6
MA7
MA8
MA9
Data Mining 1
Management
of Technology
and
Innovations 1
ERP/
E-Business
Service
Sciences 1
Quality
Engineering
&
Management 1
Production and
Operations
Management 1
Metaheuristics
Financial
Models &
Engineering
Uncertainty
Theory (Session I)
528
100
37
54
23
75
42
41
551
207
111
38
55
28
158
43
146
555
276
143
352
108
109
211
175
180
556
324
44
360
215
113
269
353
267
584
296
97
255
244
226
213
465
273
10:10-10:30
Coffee break
10:30-11:00
Opening addresses: APIEMS President, KIIE President, General Chair
11:00-12:00
Keynote speech I (Prof. Chung-Yee Lee: Research Issues in Future Logistics)
12:00-13:30
Lunch
13:30-15:30
Session
name
Paper #
Technical sessions MB
MB1
MB2
MB3
MB4
MB5
MB6
MB7
MB8
MB9
Decision Support Systems
& Expert
Systems
Probability
& Statistical
Modeling
Ergonomics/
Human
Factors 1
Service
Sciences 2
Quality
Engineering
&
Managment 2
Production
and
Operations
Management 2
Green
Manufacturing/
Management
Transportation
Ergonomics &
Welfare Management
173
190
96
322
227
338
417
73
488
254
299
131
401
228
362
550
91
484
290
333
305
411
229
394
119
103
530
460
334
315
479
346
396
156
312
485
116
3354
326
504
294
442
342
340
471
538
450
332
323
307
361
53
505
15:30-15:50
15:50-17:50
Session
name
Paper #
Coffee break
Technical sessions MC
MC1
MC2
MC3
MC4
MC5
MC6
MC7
MC8
MC9
Supply Chain
Management 1
Reliability &
Maintenance
Ergonomics/
Human
Factors 2
Network
Optimization
Quality
Engineering
&
Management 3
Simulation 1
Healthcare
Systems 1
Optimization
Techniques 1
Educational
Support
System
252
118
456
407
325
500
482
374
501
261
121
359
363
328
196
99
217
562
279
153
393
268
339
424
112
201
448
280
320
419
515
346
66
194
169
455
355
580
449
319
370
179
248
206
154
336
582
341
142
402
271
507
Oct 14 (Tuesday)
Registration
08:00-17:00
Room
08:40-10:40
Session
name
Paper #
Mara
Biyang
Udo
Chuja
Ramada-1
Ramada-2
Ramada-3
Ramada-4
Halla(8F)
Technical sessions TA
TA1
TA2
TA3
TA4
TA5
TA6
TA7
TA8
TA9
Supply Chain
Management 2
Communication
Support
Data Mining 2
Tourism
Management/
Topics in
IE/MS
Sustainable
Management
Simulation 2
Production &
Operations
Management 1
Logistics
Management
Uncertainty
Theory
(Session II)
50
443
128
472
35
98
282
440
558
59
535
147
444
114
105
327
477
559
60
489
203
564
136
221
349
483
560
61
536
392
15
137
272
431
543
561
130
480
412
264
291
295
104
344
565
161
537
216
225
347
356
218
313
428
10:40-11:00
Coffee break
11:00-12:00
Keynote speech II (Prof. Ronald Askin: Data Driven Decision Making in Manufacturing)
12:00-13:00
Lunch
13:00-14:00
Keynote speech III (Prof. Sungzoon Cho: Big Data Management)
14:00-14:20
Coffee break
14:20-16:00
Session
name
Paper #
Technical sessions TB
TB1
TB2
TB3
TB4
TB5
TB6
TB7
TB8
TB9
Supply Chain
Management 3
Management
of Technology
and
Innovations 2
Data Mining 3
Scheduling &
Sequencing 1
Knowledge &
Information
Management
Production &
Operations
Management 2
Healthcare
Systems 2
Flexible
Manufacturing
Systems
Topics in IE/MS
165
188
437
122
250
49
95
579
575
176
425
469
233
278
124
106
48
354
208
317
486
284
445
151
306
62
378
160
150
502
287
297
187
379
286
212
234
22
581
309
389
12
76
457
202
16:00-16:20
16:20-18:00
Session
name
Paper #
Coffee break
Technical sessions TC
TC1
TC2
TC3
TC4
TC9
Heuristics/Metaheuristics
Inventory Modeling / Artiicial
Intelligence
Artiicial Intelligence
Scheduling &
Sequencing 2
Lean Production Management
70
381
182
399
542
464
123
260
405
546
481
101
490
418
94
520
318
391
398
545
499
79
547
192
POSTER Session
13:00-18:00
Paper #
18:30-21:00
47
149
166
204
220
245
253
265
205
365
366
382
400
414
422
432
435
524
451
473
487
522
527
491
420
145
General Reception
Oct 15 (Wednesday)
Registration
08:00-12:00
Room
08:30-10:10
Session
name
Paper #
Mara
Biyang
Udo
Session
name
Paper #
12:10-13:30
Ramada-3
Ramada-4
WA1
WA2
WA3
WA4
WA5
WA6
Inventory Modeling & Management
SCM and
Forecasting 1
Production
Design &
Management 1
Scheduling &
Sequencing 3
Fuzzy Logic
Optimization
Techniques 2
65
92
117
85
30
125
80
31
162
120
58
69
71
34
198
177
224
288
446
32
222
316
576
577
518
102
249
509
415
Coffee break
10:10-10:30
10:30-12:10
Chuja
Technical sessions WA
Technical sessions TB
WB1
WB2
WB3
WB4
WB5
WB6
Industrial
Engineering
Education
SCM and Forecasting 2
Production
Design &
Management 2
Scheduling &
Sequencing 4
Quality
Engineering &
Reliability
Lean
Manufacturing
526
52
283
329
453
129
139
36
348
46
508
371
256
87
350
403
270
553
495
413
93
426
517
110
84
454
421
516
Lunch
Ramada-1
Ramada-2
Floor Plan
8F
Tamna Hall
Halla Hall
Ora
Hall
2F
Po
s
r
te
Se
i
ss
Ara
Hall
Technical
Session(10/13~14)
on
Ballroom Lobby
Registration
Ramada
Ballroom
Mara Hall
Udo Hall
Biyang Hall
Chuja Hall
Technical
Session
Ramada Ballroom −> Banquet
Ramada 2,3,4 −> Welcome Reception
Ramada 1,2,3,4 −> Technical Session
Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2014
Analysis and Proposal about the Effect of Time, Types of
Subject and Types of Room Factor
to the Students’ Concentration
Elty Sarvia
Department of Industrial Engineering
Maranatha Christian University, Bandung, Indonesia
Tel: (+62) 22-2012186 ext 1262/1276, Email : eltysarvia@yahoo.com
Evan Pratama Sentosa
Department of Industrial Engineering
Maranatha Christian University, Bandung, Indonesia
Tel: (+62) 22-2012186 ext 1262/1276, Email: evan_sentosa@yahoo.com
Abstract. Decreasing of the learning concentration was defined as a decreasing ability to concentrate on
learning activity which was reflected through one's behavior (Ahmadi Abu, 2003). This condition affects a
person's understanding. This study aimed to analyze the effect of time, types of subject and types of room
factor to the decrease of students’ concentration in learning and analyze the maximum point of the students to
concentrate in learning and propose ergonomic systems (GWM H02C05 room and H02A07 room,
Department of Industrial Engineering, Maranatha Christian University, Bandung).
Data that were collected in this study were Visual Analogue Scale, Group Bourdon Test and field observations
with 48 total respondents. The further observations were processed using ANOVA test with between-subjects
design (3-ways interaction)
ANOVA test results showed that the time factor and the types of subject factor affected to the learning
concentration of students. Types of room factor did not affect to the learning concentration of students. The
result of Visual Analogue Scale, Group Bourdon Test and observations gave the same result, that learning
concentration of the students was decreased. The proposals that could be given were doing a good course
scheduling such as mathematical subject should be placed in the morning time (at 07.00 am - 11.00 am) and
theoretical subjects placed on the day time (at 11.00 am - 03:00 pm).
Keywords: time, types of subject, types of room factor, VAS, Group Bourdon Test
1. INTRODUCTION
If the decrease of the learning concentration was
further reviewed, it would lead to misunderstanding and
ignorance about the learning materials, which was
essentially a student must know and understand the
learning material provided by an institution, so that there
will be a change in behavior in the learning process that
exist (Moh. Surya, 1977). Thus, it could be said that the
level of understanding in learning was affected by the
learning concentration. If there was a decrease in the
learning concentration, then there was a decrease in the
ability to concentrate on learning activities (Ahmadi Abu,
2003). This condition was reflected from each of the
behavior which is an indicator of a persons’ psychological.
The decrease of the students’ learning concentration was
affected by various factors, including the time, type of
subject and type of room factor.
Researchers determined the initial hypothesis based on
the results of preliminary processing of the data
questionnaire that had been distributed by the researchers to
the students and also the results of the interviews conducted
by researchers introduction. Thus, the following hypothesis
were proposed:
1
724
Sarvia and Sentosa
1.
2.
3.
4.
5.
6.
7.
8.
H1A : There was an effect for students’ learning
concentration from time factor (Factor A).
H1B : There was an effect for students’ learning
concentration from type of subject factor (Factor B).
H1C : There was an effect for students’ learning
concentration from type of room factor (Factor C).
H1AB : There was an effect for students’ learning
concentration from the interaction between the time
factor and type of subject (AB Factor Interactions).
H1AC : There was an effect for students’ learning
concentration from the interaction between the time
factor and type of room factor (AC Factor
Interactions)
H1BC : There was an effect for students’ learning
concentration from the interaction between the type
of subject and type of room factor (BC Factor
Interactions)
H1abc : There was an effect for students’ learning
concentration from the interaction between time
factor, type of subject factor and type of room
factor (ABC Factor Interactions)
H 1 : Maximum point (how long (in hours) a student
would be able to concentrate) students’
concentration on learning was set as 1 hour from the
beginning of learning process.
The limitations of this study were as follows :
Participants who became the object of research were
the student of Industrial Engineering Department,
Faculty of Engineering, Maranatha Christian
University.
The total number of respondents would be observed
in this study were 6 respondents for each interaction,
which the total of the interactions were 8.
The independent variable was only based on the time
of factor, type of subject and type of room factor to
know a decrease in the concentration of student
learning. Other independent variables such as age,
gender, consumption and health conditions, physical
work environment, the level of understanding and
ability of students, lecturers way of explanation and
exposure, psychological receiver and so on, did not
discussed in this study.
Type of room factor (Factor C) which consists of
two levels as H02C05 and H02A07 room (Graha
Widya Maranatha).
Preliminary Study
Preliminary questionnaire
Interview with students
Preliminary Data Processing
Tabulation of the results of the preliminary questionnaire
7 Null Hyphotesis Research
The Limitations of Study
Participants who became the object of research were
the student of Industrial Engineering Department,
Faculty of Engineering, Maranatha Christian
University.
The total number of respondents would be observed
in this study were 6 respondents for each interaction,
which the total of the interaction were 8 .
The independent variables was only based on the
time of factor, type of subject and type of room factor
to know a decrease in the concentration of student
learning.
Research Goal
Identify and analyze the effect of time, type of
subject and type of room to decrease of students’
learning concentration.
Identify and analyze the maximum point (hours) of
student would be able to concentrate on learning
process.
Propose an ergonomic system in order to enhance
student learning in terms of the concentration of the
factors that affect the decrease of the students’
learning concentration .
Data Collecting
1. Visual Analogue Scale (VAS)
2. Bourdon Group Test
3. Key Behaviour Weight
Data Processing
1. Testing Assumption of ANOVA
2. ANOVA test
3. Descriptive Statistics test
Discussion
2. RESEARCH METHOD
Conclusion dan Suggestion
The independent variables used by researchers in the
study are:
The time factor (Factor A), which consists of two
levels as before lunch and after lunch conditions.
Type of subject factor (Factor B) which consists of
two levels as mathematical and theoretical subjects.
Figure 1. Research Framework
725
Sarvia and Sentosa
Table 1. Key Behavior
1
FOCUS VIEWS
2
ATTENTION CONCENTRATION
3
VERBAL RESPONSE
1
Eyes looked at the left side or right side (turning to the left or right)
2
Eyes looked at downward (head down or asleep)
3
Blank stare (eyes) or daydreaming
1
Pay attention to other things (attention to others conversation or to outside of classroom)
2
Concentration focused to an object
1
Did not give a response (question) as oral speech (verbal response) from lecturer
4
DISCLAIMS OR COMPARE
-
-
5
ANSWER
1
Answering questions negatively (deviate from the problem) or doubtful (uncertain)
6
REPRESENTATION (STATEMENT)
1
Not responding when lecturer asked to respond
1
The position of the body which indicated unpreparedness in learning
7
8
PSYCHOMOTOR RESPONSE
EXPRESSIVE RESPONSES
Before
Treatment
2
Yawning
3
Conduct activities outside the classroom that does not mean
4
Rubbing eyes (sleepy)
5
Blinking eyes very often
6
Did not give a response (movement) as a psychomotor response from lecturer
7
No meaning hand gestures
1
Did not have motivation to listen to the lecturer
During
Treatment
Post
Treatment
Key
Behavior
Researchers’
benchmark for
Observation
Initial
Visual
Analogue
Scale (VAS)
Initial
Group
Bourdon Test
Field
Observation
Final
Visual
Analogue
Scale (VAS)
Final
Group Bourdon
Test
Key
Behavior
Weight
Figure 2. Data Collecting Scheme
3. DATA COLLECTION
Data collecting for the Visual Analogue Scale (VAS)
was a data collecting carried out by the researcher to
obtained students’ concentration conditions in a
subjectively manner because measuring the perceived level
of concentration of an individual at the time.
Visual Analogue Scale (VAS) is a measurement
instrument that tries to measure a characteristic or attitude
that is believed to range across a continuum of values and
cannot easily be directly measured. For example, the
amount of pain that a patient feels ranges across a
continuum from none to an extreme amount of pain.
Operationally a VAS is usually a horizontal line, 100 mm in
length, anchored by word descriptors at each end, as
illustrated in Figure 3. The VAS score is determined by
measuring in millimetres from the left hand end of the line
to the point that the patient marks. The visual analogue
scale (VAS) has been reported to be the most standardized,
valid and easy to comprehend self-report pain assessment
instrument. (Gould et al, 2002).
Group Bourdon Test is a train driver concentration test.
It is also knows as dot cancellation test. This test based
train driver psychometric used to maintain vigilance, speed,
accuracy, and concentration while looking a group of 4 dots.
Data collection for Group Bourdon Test is a data
collection conducted by researchers to obtain students’
concentration condition in a objectively manner, by
measuring objectively and calculating mathematically
about one’s concentration level.
Data Collecting in a subjectively-objectively manner
by :
a. Measurement of the respondents conducted by the
makers of observation data through behavior of the
respondents (subjective). Weighting on the indicator of
this research conducted individually by each
726
Sarvia and Sentosa
Figure 3. Visual Analogue Scale (VAS)
b.
respondent due to the weight of one with the other
respondents will create different results.
Measurement of behavior of the respondents through
the key behavior (objective) shown in table 1.
Figure 2 illustrates a data collection scheme conducted by
researchers of the 48 respondents :
Before Treatment : Data collection was performed
outside the classroom before the lecture begins by
using initial Visual Analogue Scale (VAS) and initial
Group Bourdon Test.
During Treatment : Data collection was performed by
observations in the classroom. Initial benchmark of
this observation is the key behavior that have been
described previously (Table 1)
Post Treatment : Data collection was performed
outside the classroom after the lecture is finished by
using the Final Visual Analogue Scale (VAS), Final
Group Bourdon Test and weights of key behavior.
4. RESULT AND DISCUSSION
The overall condition of the concentration of
respondents (using the Visual Analogue Scale: subjective)
before treatment was higher than the post treated condition
as shown in figure 5. The overall condition of the
concentration of respondents (using the Group Bourdon
Test : objective) before treatment was higher than the posttreated conditions as shown in figure 6. Table 2 showed the
results of the data collection which were performed by the
researchers could be concluded as an eligible data for
ANOVA test (the data is independent, normal distribution
and homogeneous). Table 3 showed the results of the
ANOVA test (used by researchers to answer the initial
research hypothesis 1 to hypothesis 7), it could be
concluded that there are only 2 factors that affected student
learning decreased concentration i.e. the time factor and
interaction between time and type of subject factor using
0.05.
This research found that from the three methods, i.e
Visual Analogue Scale (VAS) ratings, Group Bourdon Test
Figure 4. Group Bourdon Test
and ANOVA test, all had the same conclusion (Table 4).
The conclusion was there was an effect for students’
concentration (there was a significant decrease from
students’ learning concentration prior student learning
activities in the classroom to the students’ learning
concentration after learning activities in the classroom).
Descriptive statistics of test results (used by
researchers to answer the initial research hypothesis 8), it
showed that the maximum point required for students to
concentrate is between 0,750 first hours to 1,139 first hours
of their learning process, with a standard deviation 0,178
hours up to 0.643 hours.
So it could be concluded that the maximum point for
the students’ learning concentration required was
approximately 1 hour starting from the beginning of the
first lecture as shown in figure 7.
From the data processing and analysis result, therefore
it was suggested an ergonomic system to enhance the
student’s learning concentration as follow:
a. Allocating particular subjects on certain period
within student’s class time table such as
mathematical subjects should be placed in the
morning time (7.00 am – 11.00 am) and theoretical
subjects placed on the day time (11.00 am- 3.00
pm).
b. Notice the condition of the maximum point of
students in learning, approximately the first 1
hour lecture. Lecturer should be able to regain
students’ concentration by setting their tone up and
down during the lecture or designing games for
the lecture so that students are not bored or sleepy.
c. Changing the 3-credits-course (2 hours 30 minutes)
which only held in one class meeting, became two
classes meeting. (1 hour 40 minutes at the first
class meeting and 50 minutes at the second class
meeting).
d. Hence, for the 2-credits-course (1 hour 40 minutes)
would remain as it is, according to in accordance
with the conditions of the initial conditions of the
Industrial Engineering Department, Maranatha
Christian University.
727
Sarvia and Sentosa
Table 2. Testing Assumption of Anova
Independence test
Time, type of subject and type of room
factor
Normality test
Time, type of subject and type of room
factor
Homogeneity test
Durbin-Watson
Comparison
1,525
1,5 - 2,5
Shapiro-Wilk
Comparison
0,084
0,05
Levene Test
Comparison
Time factor
0,221
0,05
Type of sucject factor
0,198
0,05
Type of room factor
0,191
0,05
Decision
Conclusion
1,5 - (1,525) - 2,5
There are no differences
between the populations
Accept Null hyphotesis
Decision
Conclusion
(0,084) > 0,05
Normal distribution
Accept Null hyphotesis
Decision
Conclusion
(0,221) > 0,05
Variables are homogeneous
Accept Null hyphotesis
(0,198) > 0,05
Variables are homogeneous
Accept Null hyphotesis
(0,191) > 0,05
Variables are homogeneous
Accept Null hyphotesis
Table 3. Result of Anova Test with between-subject design
Interaction
Source of Variation
F ANOVA
1
Time factor (Factor A)
7,328
2
Type of subject factor
(Factor B)
F Table
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
0,098
3
Type of room factor
(Factor C)
4
Interaction between time and
type of subject factor
(Factor AB)
24,976
5
Interaction between time and
type of room factor
(Factor
AC)
0,271
6
Interaction between type of
subject and type of room
factor (Factor BC)
0,173
7
Interaction between time, type
of subject and type of room
factor (Factor ABC)
1,832
1,312
Conclusion
7,328 > 4,08
There was an effect from time factor for
student learning concentration
Reject null hyphotesis
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
4,08
Figure 5: Visual Analogue Scale (VAS)
Decision
0,098 < 4,08
There was no effect from type of subject factor
for student learning concentration
Accept null hyphotesis
1,312 < 4,08
There was no effect from type of room factor
for student learning concentration
Accept null hyphotesis
24,976 > 4,08
There was an effect between time and type of
subject factor for student learning concentration
Reject null hyphotesis
0,271 < 4,08
There was no effect between time and type of
room factor for student learning concentration
Accept null hyphotesis
0,173 < 4,08
Accept null hyphotesis
1,832 < 4,08
Accept null hyphotesis
There was no effect between type of subject
and type of room factor for student learning
concentration
There was no effect between time, type of
subject and type of room factor for student
learning concentration
Figure 6: Group Bourdon Test
728
Sarvia and Sentosa
Figure 7 : Maximum Point of the Students’ Learning Concentration (hours)
\
Table 4. Analysis of Three Methods
Data Collection
Method
Subjective
Visual Analogue Scale (VAS)
Objective
Group Bourdon Test
Subjective-Objective
Observation in the classroom
Conclusion
There was an effect for students’
concentration before treatment and after
treatment
There was an effect for students’
concentration before treatment and after
treatment
There was an effect for students’
concentration before treatment and after
treatment
Final Conclusion
There was a significant
decrease from students’
learning concentration prior
student learning activities in
the classroom to the
students’ learning
concentration after learning
activities in the classroom
5. CONCLUSION
REFERENCES
From Visual Analogue Scale graphic and Group
Bourdon Test graphic, there was a significant decrease from
students’ learning concentration prior student learning
activities in the classroom to the students’ learning
concentration after learning activities in the classroom.
Based on Anova Testing and analysis result, it was found
the conclusion that there were 2 factors that affected the
students’ learning concentration decrease, which was a
factor of time (Factor A) and the interaction between the
time factor and the type of subject factor (AB Factor
Interactions). Based on descriptive statistics analysis,
students were still able to concentrate on studying for 1
hour (maximum 1,139 hours) in accordance with the initial
hypothesis of the study).
The recommendations that were given to the
Department of Industrial Engineering, Faculty of
Engineering, Maranatha Christian University, Bandung,
Indonesia such as allocating particular subjects on certain
period within student’s class time table; Lecturer should be
able to regain students’ concentration by setting their tone
up and down during the lecture or designing games for the
lecture so that students are not bored or sleepy; Changing
the 3-credits-course became two classes meeting.
Ahmadi, Abu., Supriyono, Widodo. (2003) Psikologi
Belajar, Jakarta, PT Rineka Cipta
Bridger, S R. (2003) Introduction To Ergonomics, Taylor
and Francis Group.
Cognitive Ergonomic, accessed on 15th February 2013,
http://en.wikipedia.org/wiki/Cognitive_ergonomics,
Dot Cancellation Test, accessed on 13th February 2013,
http://en.wikipedia.org/wiki/Dot_cancellation_test.
Pengertian Ergonomi Kognitif, accessed on 18th December
2012,
,
http://ergonomikognitif.blogspot.com/2011/12/pengerti
an-ergonomi-kognitif.
Ghozali, Imam G. (2006) Aplikasi Analisis Multivariate
Dengan Program SPSS, Universitas Diponegoro
Gould DJ, Kelly D, Goldstone L and Gammon (2002)
Examining the validity of pressure ulcer risk assessment
scales: developing and using illustrated patient
simulations to collect the data. J Clin Nurs 697-706.
Halim, Winda. (2012) Evaluasi Penggunaan Psychomotor
Vigilance Task Dalam Konteks Pengukuran Beban
Kerja Mental, Tesis Magister, Program Studi Teknik dan
Manajemen Industri, Institut Teknologi Bandung.
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Helander, Martin. (2006) A Guide To Human Factors And
Ergonomics, Taylor and Francis Group.
Martin, W David. (1996) Doing Psychology Experiments,
Fourth Edition? North Carolina State University,
Brooks/ Cole Publishing Company.
Pengertian dan Ciri-Ciri Konsentrasi, accessed on 15th
February 2013,
http://abudaud2010.blogspot.com /2010/11/pengertiandan-ciri-ciri-konsentrasi.html.
Pengertian Ergonomi, accessed on 15th February 2013,
http://sobatbaru.blogspot.com/2010/03/pengertianergonomi.html.
Psikologi Kognitif, accessed on 15th February 2013,
http://elib.unikom.ac.id/download.php?id=86119.
Reed, K Stephen (2011) Kognisi, Teori dan Aplikasi
(Cognition, Theory and Application), Jakarta: Salemba
Humanika.
Setianingrum, Yeni, Ajeng. (2010) Pengaruh Penggunaan
Telepon Genggam Selama Berkendara Terhadap Waktu
Reaksi Pengemudi Dalam Keadaan Lelah (Studi Kasus
Jalan Bebas Hambatan), Tesis Magister, Program Studi
Teknik dan Manajemen Industri, Institut Teknologi
Bandung.
Teori Konsentrasi Belajar, accessed on 15th February 2013,
http://ilmukata.blogspot.com/2013/01/teori-konsentrasibelajar.html.
Train Driver Group Bourdon Test, accessed on 15th
February 2013,
http://www.careervidz.com/train-driver-group-bourdontest.html.
Visual Analogue Scale (VAS), accessed on 15th February
2013,
http://en.wikipedia.org/wiki/Visual_analogue_scale.
730
Greeting and a warm welcome to the participants of the 15th Asia Paciic Industrial Engineering
and Management Systems Conference. Started in 1998, APIEMS has grown to become the premier
conference for industrial engineering and management systems in the region with participants from
all around the world. The main theme of this year conference: “Sustainable Industrial Systems and
Big Data Management”, is an attempt to address the balance among economic and technical development, social development, and environmental protection in this fast changing world.
I congratulate and thank Prof. Dr. Chi-Hyuck Jun, the conference chair, whose leadership made this
APIEMS 2014 conference possible. We are also grateful for the enthusiastic support of APIEMS
from the KIIE and the Korea research community.
On behave of the Asia Paciic Industrial Engineering and Management Society, I wish you a successful conference with many thoughtful discussions and debates with old and new friends.
Professor Voratas Kachitvichyanukul
APIEMS President, (2013-2014)
Professor of Industrial & Manufacturing Engineering
Dean, School of Engineering and Technology
Asian Institute of Technology, THAILAND
Message from the General Chair
Welcome to APIEMS 2014 in Jeju City, a beautiful island located at the most south of Korea. It is
our great pleasure to organize this conference, which is supported by Korean Institute of Industrial
Engineers (KIIE). APIEMS conferences have rapidly emerged as an important forum for exchange
of ideas and information about latest developments in the ield of industrial engineering and management systems among professionals mostly from Asia-Paciic countries. APIEMS 2014 conference encourages contributors to address the topical theme: Sustainable Industrial Systems and Big
Data Management. Papers will represent the latest academic thinking and successful case examples.
The wider audience will beneit from the knowledge and experience of leading practitioners and
academics in this area.
The conference seeks research contributions from researchers, educators, modelers, software developers, users and practitioners. We hope that you enjoy participating in APIEMS 2014 and staying
in Jeju.
Professor Chi-Hyuck Jun
General Chair, APIEMS 2014
Industrial & Management Engineering
POSTECH, Korea
Conference Committee Members
Conference Committee
• Conference Chair
• Chi-Hyuck Jun (POSTECH, Korea)
• Honorary Chairs
• Hark Hwang (KAIST, Korea)
• Mooyoung Jung (UNIST, Korea)
• Kap Hwan Kim (Pusan National Univ., Korea; President, KIIE)
• Conference Co-Chairs (International Advisory Board)
• Abdul Hakim Halim (InstitutTeknologi Bandung, Indonesia)
• Anthony Shun Fung Chiu (De La Salle University, Philippines)
• Baoding Liu (Tsinghua University, China)
• Bernard Jiang (National Taiwan University of Science and Technology, Taiwan)
• C. J. Liao (National Taiwan University of Science and Technology, Taiwan)
• Che-Fu Chien (National Tsing Hua University, Taiwan)
• Du-Ming Tsai (Yuan Ze University, Taiwan)
• ErhanKozan (Queensland University of Technology, Australia)
• HirokazuKono (Keio University, Japan)
• Jin Peng (Huanggang Normal University, China)
• Jinwoo, Park (Seoul National Univ., Korea)
• Katsuhiko Takahashi ( Hiroshima University, Japan)
• Kazuyoshi Ishii (Kanazawa Institute of Technology, Japan)
• Kin Keung Lai (City University of Hong Kong, Hong Kong)
• Mao Jiun Wang (National Tsing Hua Univeristy, Taiwan)
• Min K. Chung (POSTECH, Korea)
• Mitsuo Gen (Fuzzy Logic Systems Institute, Japan)
• P. L. Chang (Feng Chia Uni)
• Shouyang Wan (Chinese Academy of Sciences, China)
• Tae Eog Lee (KAIST, Korea)
• Takashi Oyabu (Kanazawa Seiryo University, Japan)
• VoratasKachitvichyanukul (Asian Institute of Technology, Thailand)
• Yon-Chun Chou (National Taiwan University, Taiwan)
• Young Hae Lee (Hanyang University, Korea)
• ZahariTaha (Universiti Malaysia Pahang, Malaysia)
Organizing Committee
• Technical Program Chairs
• Il-Kyeong Moon (Seoul National Univ., Korea)
• Byung-In Kim (POSTECH, Korea)
• Publication Chairs
• Jaewook Lee (Seoul National Univ., Korea)
• Hosang Jung (Inha Univ., Korea)
• Publicity Chairs
• Chulung Lee (Korea Univ., Korea)
• Yoo-Suk Hong (Seoul National Univ., Korea)
• Sponsorship Chairs
• Minseok Song (UNIST, Korea)
• Young Jin Kim (Pukyong National Univ., Korea)
• Exhibition Chairs
• Hyunbo Cho (POSTECH, Korea)
• Yonghui Oh (Daejin Univ., Korea)
• Finance Chair
• Dong-Ho Lee (Hanyang Univ., Korea)
• Award Chairs
• Kyung sik Lee (Seoul National Univ., Korea)
• Young Jae Jang (KAIST, Korea)
• Local Arrangement Chair
• Dong-Cheol Lee (Jeju National Univ., Korea)
Conference Sponsors
The Korean Federation of Science
and Technology Societies
DOOSAN
SAS KOREA
Pohang University of Science
and Technology
The Korean Operations Research
and Management Science Society
THE KOREAN OPERATIONS RESEARH
AND MANAGEMENT SCIENCE SOCIETY
Keynote Speech
Keynote Speech I
Research Issues in Future Logistics
Oct 13 (Monday) 11:00-12:00
Room: Ramada-1
Chung– Yee Lee
Hong Kong University of Science and Technology, China
Dr. Chung-Yee Lee is Chair Professor/Cheong Ying Chan Professor of Engineering in the Department of Industrial Engineering & Logistics Management at Hong Kong University of Science and
Technology. He served as Department Head for seven years (2001- 2008). He is also the Founding
and Current Director of Logistics and Supply Chain Management Institute. He is a Fellow of the
Institute of Industrial Engineers in U.S. and also a Fellow of Hong Kong Academy of Engineering
Science. Before joining HKUST in 2001, he was Rockwell Chair Professor in the Department of
Industrial Engineering at Texas A&M University. He worked as a plant manager and also had few
years consulting experience in Taiwan. In the past thirty years he has engaged in more than forty
research projects sponsored by NSF, RGC, ITF, IBM, Motorola, AT&T Paradyne, Harris Semicon
ductor, Northern Telecom, Martin Marietta, Hong Kong Air Cargo Terminal, Hongkong International Terminal, Philips Medical, ...,etc.
His search areas are in logistics and supply chain management, scheduling and inventory management. He has published more than 130 papers in refereed journals. According to an article in Int. J.
Prod. Eco. (2009), which looked at all papers published in the 20 core journals during last 50 years
in the ield of production and operations management, he was ranked No. 6 among all researchers
worldwide in h-index.
He received a BS degree in Electronic Engineering (1972) and a MS degree in Management Sciences (1976) both from National Chiao-Tung University in Taiwan. He also received a MS degree
in Industrial Engineering from Northwestern University (1980) and PhD degree in Operations Research from Yale University (1984).
Keynote Speech
Keynote Speech II
Data-Driven Decision Making in Manufacturing:
Lessons Learned and Future Opportunities
Oct 14 (Tuesday) 11:00-12:00
Room: Ramada-1
Ronald G. Askin
Arizona State University, USA
Ronald G. Askin, Ph.D., is a Professor of Industrial Engineering and Director of the School of
Computing, Informatics, and Decision Systems Engineering at Arizona State University. Professor
Askin received his B. S. in Industrial Engineering from Lehigh University followed by an M.S. in
Operations Research and PhD in Industrial and Systems Engineering from the Georgia Institute of
Technology. He has over 30 years of experience in the development, teaching and application of
methods for systems design and analysis with particular emphasis on production and material low
systems. Other interests include quality engineering and decision analysis. He has published over
120 journal and conference proceedings papers in these areas.
Dr. Askin is a Fellow of the Institute of Industrial Engineers (IIE) and serves as Editor-in-Chief
of IIE Transactions. He has served on the IIE Board of Trustees, as President of the IIE Council
of Fellows, Chair of the Association of Chairs of Operations Research Departments (ACORD)
Chair of the Industrial Engineering Academic Department Heads (CIEADH) and President of the
INFORMS Manufacturing and Service Operations Management Society (MSOM). He was also
General Chair of the 2012 INFORMS Annual Conference. His list of awards includes a National
Science Foundation Presidential Young Investigator Award, the Shingo Prize for Excellence in
Manufacturing Research, IIE Joint Publishers Book of the Year Award (twice), IIE Transactions on
Design and Manufacturing Best Paper Award (twice), the Eugene L. Grant best paper award from
The Engineering Economist, and the IIE Transactions Development and Applications Award.
Keynote Speech
Keynote Speech III
Big Data Management
Oct 14 (Tuesday) 13:00-14:00
Room: Ramada-1
Sungzoon Cho
Seoul National University, Korea.
Sungzoon Cho is currently professor of Industrial Engineering Department, the director of Data
Mining Center at Seoul National University (SNU) and a member of Government 3.0 Committee
of Korean government. He is on the editorial board of International Journal of Operations Research
and Information Systems and International Journal of Cognitive Biometrics. He served as the presi
yundai Motors, Hyundai Heavy Industries, POSCO, Daewoo Shipbuilding and Marine Engineering, LG Electronics, Doosan Infracore, SK Hynix, SK Telecommunication and CJ. He advised nine
PhDs and 56 Master students. He teaches Data Mining and Computational Intelligence at SNU as
well as at irms. He received BS and MS in Industrial Engineering at SNU. He won a Fulbright
Scholarship to obtain Masters and PhD at University of Washington in Seattle, US, and University
of Maryland in College Park, US, respectively.
Conference at a Glance
Oct 12 (Sunday)
10:00-18:00
Oct 13 (Monday)
08:00-17:00
Registration
08:30-10:10
Technical sessions
MA
10:10-10:30
Coffee break
10:30-11:00
Opening addresses :
APIEMS President,
KIIE President,
General Chair
08:00-17:00
Technical sessions TA
10:40-11:00
Coffee break
11:00-12:00
Keynote speech I
(Prof. Chung-Yee Lee:
Research issues in
Future Logistics)
11:00:12:00
Keynote speech II
(Prof. Ronald Askin:
Data-Driven Decision
Making in
Manufacturing)
12:00-13:30
Lunch
12:00-13:00
Lunch
13:00-14:00
Keynote speech III
(Prof. Sungzoon Cho:
Big Data
Management)
14:00-14:20
Coffee break
Registration
Technical sessions
MB
Excursion
15:30-15:50
Coffee break
14:20-16:00
Technical sessions
TB
15:50-17:50
Technical sessions
MC
16:00-16:20
Coffee break
16:20-18:00
Technical sessions
TC
13:00-18:00
Poster Session
18:30-21:00
General Reception
Registration
18:00-20:00
Welcome
Reception
Registration
08:40-10:40
13:30-15:30
13:00-17:20
Oct 14 (Tuesday)
Oct 15 (Wednesday)
08:00-12:00
Registration
08:30-10:10
Technical sessions
WA
10:10-10:30
Coffee break
10:30-12:10
Technical sessions
WB
12:10-13:30
Lunch
Oct 12 (Sunday)
10:00-18:00
Registration
13:00-17:20
Excursion
18:00-20:00
Welcome Reception
Oct 13 (Monday)
Registration
08:00-17:00
Room
08:30-10:10
Session
name
Paper #
Mara
Biyang
Udo
Chuja
Ramada-1
Ramada-2
Ramada-3
Ramada-4
Halla(8F)
Technical sessions MA
MA1
MA2
MA3
MA4
MA5
MA6
MA7
MA8
MA9
Data Mining 1
Management
of Technology
and
Innovations 1
ERP/
E-Business
Service
Sciences 1
Quality
Engineering
&
Management 1
Production and
Operations
Management 1
Metaheuristics
Financial
Models &
Engineering
Uncertainty
Theory (Session I)
528
100
37
54
23
75
42
41
551
207
111
38
55
28
158
43
146
555
276
143
352
108
109
211
175
180
556
324
44
360
215
113
269
353
267
584
296
97
255
244
226
213
465
273
10:10-10:30
Coffee break
10:30-11:00
Opening addresses: APIEMS President, KIIE President, General Chair
11:00-12:00
Keynote speech I (Prof. Chung-Yee Lee: Research Issues in Future Logistics)
12:00-13:30
Lunch
13:30-15:30
Session
name
Paper #
Technical sessions MB
MB1
MB2
MB3
MB4
MB5
MB6
MB7
MB8
MB9
Decision Support Systems
& Expert
Systems
Probability
& Statistical
Modeling
Ergonomics/
Human
Factors 1
Service
Sciences 2
Quality
Engineering
&
Managment 2
Production
and
Operations
Management 2
Green
Manufacturing/
Management
Transportation
Ergonomics &
Welfare Management
173
190
96
322
227
338
417
73
488
254
299
131
401
228
362
550
91
484
290
333
305
411
229
394
119
103
530
460
334
315
479
346
396
156
312
485
116
3354
326
504
294
442
342
340
471
538
450
332
323
307
361
53
505
15:30-15:50
15:50-17:50
Session
name
Paper #
Coffee break
Technical sessions MC
MC1
MC2
MC3
MC4
MC5
MC6
MC7
MC8
MC9
Supply Chain
Management 1
Reliability &
Maintenance
Ergonomics/
Human
Factors 2
Network
Optimization
Quality
Engineering
&
Management 3
Simulation 1
Healthcare
Systems 1
Optimization
Techniques 1
Educational
Support
System
252
118
456
407
325
500
482
374
501
261
121
359
363
328
196
99
217
562
279
153
393
268
339
424
112
201
448
280
320
419
515
346
66
194
169
455
355
580
449
319
370
179
248
206
154
336
582
341
142
402
271
507
Oct 14 (Tuesday)
Registration
08:00-17:00
Room
08:40-10:40
Session
name
Paper #
Mara
Biyang
Udo
Chuja
Ramada-1
Ramada-2
Ramada-3
Ramada-4
Halla(8F)
Technical sessions TA
TA1
TA2
TA3
TA4
TA5
TA6
TA7
TA8
TA9
Supply Chain
Management 2
Communication
Support
Data Mining 2
Tourism
Management/
Topics in
IE/MS
Sustainable
Management
Simulation 2
Production &
Operations
Management 1
Logistics
Management
Uncertainty
Theory
(Session II)
50
443
128
472
35
98
282
440
558
59
535
147
444
114
105
327
477
559
60
489
203
564
136
221
349
483
560
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Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2014
Analysis and Proposal about the Effect of Time, Types of
Subject and Types of Room Factor
to the Students’ Concentration
Elty Sarvia
Department of Industrial Engineering
Maranatha Christian University, Bandung, Indonesia
Tel: (+62) 22-2012186 ext 1262/1276, Email : eltysarvia@yahoo.com
Evan Pratama Sentosa
Department of Industrial Engineering
Maranatha Christian University, Bandung, Indonesia
Tel: (+62) 22-2012186 ext 1262/1276, Email: evan_sentosa@yahoo.com
Abstract. Decreasing of the learning concentration was defined as a decreasing ability to concentrate on
learning activity which was reflected through one's behavior (Ahmadi Abu, 2003). This condition affects a
person's understanding. This study aimed to analyze the effect of time, types of subject and types of room
factor to the decrease of students’ concentration in learning and analyze the maximum point of the students to
concentrate in learning and propose ergonomic systems (GWM H02C05 room and H02A07 room,
Department of Industrial Engineering, Maranatha Christian University, Bandung).
Data that were collected in this study were Visual Analogue Scale, Group Bourdon Test and field observations
with 48 total respondents. The further observations were processed using ANOVA test with between-subjects
design (3-ways interaction)
ANOVA test results showed that the time factor and the types of subject factor affected to the learning
concentration of students. Types of room factor did not affect to the learning concentration of students. The
result of Visual Analogue Scale, Group Bourdon Test and observations gave the same result, that learning
concentration of the students was decreased. The proposals that could be given were doing a good course
scheduling such as mathematical subject should be placed in the morning time (at 07.00 am - 11.00 am) and
theoretical subjects placed on the day time (at 11.00 am - 03:00 pm).
Keywords: time, types of subject, types of room factor, VAS, Group Bourdon Test
1. INTRODUCTION
If the decrease of the learning concentration was
further reviewed, it would lead to misunderstanding and
ignorance about the learning materials, which was
essentially a student must know and understand the
learning material provided by an institution, so that there
will be a change in behavior in the learning process that
exist (Moh. Surya, 1977). Thus, it could be said that the
level of understanding in learning was affected by the
learning concentration. If there was a decrease in the
learning concentration, then there was a decrease in the
ability to concentrate on learning activities (Ahmadi Abu,
2003). This condition was reflected from each of the
behavior which is an indicator of a persons’ psychological.
The decrease of the students’ learning concentration was
affected by various factors, including the time, type of
subject and type of room factor.
Researchers determined the initial hypothesis based on
the results of preliminary processing of the data
questionnaire that had been distributed by the researchers to
the students and also the results of the interviews conducted
by researchers introduction. Thus, the following hypothesis
were proposed:
1
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Sarvia and Sentosa
1.
2.
3.
4.
5.
6.
7.
8.
H1A : There was an effect for students’ learning
concentration from time factor (Factor A).
H1B : There was an effect for students’ learning
concentration from type of subject factor (Factor B).
H1C : There was an effect for students’ learning
concentration from type of room factor (Factor C).
H1AB : There was an effect for students’ learning
concentration from the interaction between the time
factor and type of subject (AB Factor Interactions).
H1AC : There was an effect for students’ learning
concentration from the interaction between the time
factor and type of room factor (AC Factor
Interactions)
H1BC : There was an effect for students’ learning
concentration from the interaction between the type
of subject and type of room factor (BC Factor
Interactions)
H1abc : There was an effect for students’ learning
concentration from the interaction between time
factor, type of subject factor and type of room
factor (ABC Factor Interactions)
H 1 : Maximum point (how long (in hours) a student
would be able to concentrate) students’
concentration on learning was set as 1 hour from the
beginning of learning process.
The limitations of this study were as follows :
Participants who became the object of research were
the student of Industrial Engineering Department,
Faculty of Engineering, Maranatha Christian
University.
The total number of respondents would be observed
in this study were 6 respondents for each interaction,
which the total of the interactions were 8.
The independent variable was only based on the time
of factor, type of subject and type of room factor to
know a decrease in the concentration of student
learning. Other independent variables such as age,
gender, consumption and health conditions, physical
work environment, the level of understanding and
ability of students, lecturers way of explanation and
exposure, psychological receiver and so on, did not
discussed in this study.
Type of room factor (Factor C) which consists of
two levels as H02C05 and H02A07 room (Graha
Widya Maranatha).
Preliminary Study
Preliminary questionnaire
Interview with students
Preliminary Data Processing
Tabulation of the results of the preliminary questionnaire
7 Null Hyphotesis Research
The Limitations of Study
Participants who became the object of research were
the student of Industrial Engineering Department,
Faculty of Engineering, Maranatha Christian
University.
The total number of respondents would be observed
in this study were 6 respondents for each interaction,
which the total of the interaction were 8 .
The independent variables was only based on the
time of factor, type of subject and type of room factor
to know a decrease in the concentration of student
learning.
Research Goal
Identify and analyze the effect of time, type of
subject and type of room to decrease of students’
learning concentration.
Identify and analyze the maximum point (hours) of
student would be able to concentrate on learning
process.
Propose an ergonomic system in order to enhance
student learning in terms of the concentration of the
factors that affect the decrease of the students’
learning concentration .
Data Collecting
1. Visual Analogue Scale (VAS)
2. Bourdon Group Test
3. Key Behaviour Weight
Data Processing
1. Testing Assumption of ANOVA
2. ANOVA test
3. Descriptive Statistics test
Discussion
2. RESEARCH METHOD
Conclusion dan Suggestion
The independent variables used by researchers in the
study are:
The time factor (Factor A), which consists of two
levels as before lunch and after lunch conditions.
Type of subject factor (Factor B) which consists of
two levels as mathematical and theoretical subjects.
Figure 1. Research Framework
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Sarvia and Sentosa
Table 1. Key Behavior
1
FOCUS VIEWS
2
ATTENTION CONCENTRATION
3
VERBAL RESPONSE
1
Eyes looked at the left side or right side (turning to the left or right)
2
Eyes looked at downward (head down or asleep)
3
Blank stare (eyes) or daydreaming
1
Pay attention to other things (attention to others conversation or to outside of classroom)
2
Concentration focused to an object
1
Did not give a response (question) as oral speech (verbal response) from lecturer
4
DISCLAIMS OR COMPARE
-
-
5
ANSWER
1
Answering questions negatively (deviate from the problem) or doubtful (uncertain)
6
REPRESENTATION (STATEMENT)
1
Not responding when lecturer asked to respond
1
The position of the body which indicated unpreparedness in learning
7
8
PSYCHOMOTOR RESPONSE
EXPRESSIVE RESPONSES
Before
Treatment
2
Yawning
3
Conduct activities outside the classroom that does not mean
4
Rubbing eyes (sleepy)
5
Blinking eyes very often
6
Did not give a response (movement) as a psychomotor response from lecturer
7
No meaning hand gestures
1
Did not have motivation to listen to the lecturer
During
Treatment
Post
Treatment
Key
Behavior
Researchers’
benchmark for
Observation
Initial
Visual
Analogue
Scale (VAS)
Initial
Group
Bourdon Test
Field
Observation
Final
Visual
Analogue
Scale (VAS)
Final
Group Bourdon
Test
Key
Behavior
Weight
Figure 2. Data Collecting Scheme
3. DATA COLLECTION
Data collecting for the Visual Analogue Scale (VAS)
was a data collecting carried out by the researcher to
obtained students’ concentration conditions in a
subjectively manner because measuring the perceived level
of concentration of an individual at the time.
Visual Analogue Scale (VAS) is a measurement
instrument that tries to measure a characteristic or attitude
that is believed to range across a continuum of values and
cannot easily be directly measured. For example, the
amount of pain that a patient feels ranges across a
continuum from none to an extreme amount of pain.
Operationally a VAS is usually a horizontal line, 100 mm in
length, anchored by word descriptors at each end, as
illustrated in Figure 3. The VAS score is determined by
measuring in millimetres from the left hand end of the line
to the point that the patient marks. The visual analogue
scale (VAS) has been reported to be the most standardized,
valid and easy to comprehend self-report pain assessment
instrument. (Gould et al, 2002).
Group Bourdon Test is a train driver concentration test.
It is also knows as dot cancellation test. This test based
train driver psychometric used to maintain vigilance, speed,
accuracy, and concentration while looking a group of 4 dots.
Data collection for Group Bourdon Test is a data
collection conducted by researchers to obtain students’
concentration condition in a objectively manner, by
measuring objectively and calculating mathematically
about one’s concentration level.
Data Collecting in a subjectively-objectively manner
by :
a. Measurement of the respondents conducted by the
makers of observation data through behavior of the
respondents (subjective). Weighting on the indicator of
this research conducted individually by each
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Sarvia and Sentosa
Figure 3. Visual Analogue Scale (VAS)
b.
respondent due to the weight of one with the other
respondents will create different results.
Measurement of behavior of the respondents through
the key behavior (objective) shown in table 1.
Figure 2 illustrates a data collection scheme conducted by
researchers of the 48 respondents :
Before Treatment : Data collection was performed
outside the classroom before the lecture begins by
using initial Visual Analogue Scale (VAS) and initial
Group Bourdon Test.
During Treatment : Data collection was performed by
observations in the classroom. Initial benchmark of
this observation is the key behavior that have been
described previously (Table 1)
Post Treatment : Data collection was performed
outside the classroom after the lecture is finished by
using the Final Visual Analogue Scale (VAS), Final
Group Bourdon Test and weights of key behavior.
4. RESULT AND DISCUSSION
The overall condition of the concentration of
respondents (using the Visual Analogue Scale: subjective)
before treatment was higher than the post treated condition
as shown in figure 5. The overall condition of the
concentration of respondents (using the Group Bourdon
Test : objective) before treatment was higher than the posttreated conditions as shown in figure 6. Table 2 showed the
results of the data collection which were performed by the
researchers could be concluded as an eligible data for
ANOVA test (the data is independent, normal distribution
and homogeneous). Table 3 showed the results of the
ANOVA test (used by researchers to answer the initial
research hypothesis 1 to hypothesis 7), it could be
concluded that there are only 2 factors that affected student
learning decreased concentration i.e. the time factor and
interaction between time and type of subject factor using
0.05.
This research found that from the three methods, i.e
Visual Analogue Scale (VAS) ratings, Group Bourdon Test
Figure 4. Group Bourdon Test
and ANOVA test, all had the same conclusion (Table 4).
The conclusion was there was an effect for students’
concentration (there was a significant decrease from
students’ learning concentration prior student learning
activities in the classroom to the students’ learning
concentration after learning activities in the classroom).
Descriptive statistics of test results (used by
researchers to answer the initial research hypothesis 8), it
showed that the maximum point required for students to
concentrate is between 0,750 first hours to 1,139 first hours
of their learning process, with a standard deviation 0,178
hours up to 0.643 hours.
So it could be concluded that the maximum point for
the students’ learning concentration required was
approximately 1 hour starting from the beginning of the
first lecture as shown in figure 7.
From the data processing and analysis result, therefore
it was suggested an ergonomic system to enhance the
student’s learning concentration as follow:
a. Allocating particular subjects on certain period
within student’s class time table such as
mathematical subjects should be placed in the
morning time (7.00 am – 11.00 am) and theoretical
subjects placed on the day time (11.00 am- 3.00
pm).
b. Notice the condition of the maximum point of
students in learning, approximately the first 1
hour lecture. Lecturer should be able to regain
students’ concentration by setting their tone up and
down during the lecture or designing games for
the lecture so that students are not bored or sleepy.
c. Changing the 3-credits-course (2 hours 30 minutes)
which only held in one class meeting, became two
classes meeting. (1 hour 40 minutes at the first
class meeting and 50 minutes at the second class
meeting).
d. Hence, for the 2-credits-course (1 hour 40 minutes)
would remain as it is, according to in accordance
with the conditions of the initial conditions of the
Industrial Engineering Department, Maranatha
Christian University.
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Sarvia and Sentosa
Table 2. Testing Assumption of Anova
Independence test
Time, type of subject and type of room
factor
Normality test
Time, type of subject and type of room
factor
Homogeneity test
Durbin-Watson
Comparison
1,525
1,5 - 2,5
Shapiro-Wilk
Comparison
0,084
0,05
Levene Test
Comparison
Time factor
0,221
0,05
Type of sucject factor
0,198
0,05
Type of room factor
0,191
0,05
Decision
Conclusion
1,5 - (1,525) - 2,5
There are no differences
between the populations
Accept Null hyphotesis
Decision
Conclusion
(0,084) > 0,05
Normal distribution
Accept Null hyphotesis
Decision
Conclusion
(0,221) > 0,05
Variables are homogeneous
Accept Null hyphotesis
(0,198) > 0,05
Variables are homogeneous
Accept Null hyphotesis
(0,191) > 0,05
Variables are homogeneous
Accept Null hyphotesis
Table 3. Result of Anova Test with between-subject design
Interaction
Source of Variation
F ANOVA
1
Time factor (Factor A)
7,328
2
Type of subject factor
(Factor B)
F Table
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
0,098
3
Type of room factor
(Factor C)
4
Interaction between time and
type of subject factor
(Factor AB)
24,976
5
Interaction between time and
type of room factor
(Factor
AC)
0,271
6
Interaction between type of
subject and type of room
factor (Factor BC)
0,173
7
Interaction between time, type
of subject and type of room
factor (Factor ABC)
1,832
1,312
Conclusion
7,328 > 4,08
There was an effect from time factor for
student learning concentration
Reject null hyphotesis
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
df1 = 1
4,08
df2 = 40
α = 0,05
4,08
Figure 5: Visual Analogue Scale (VAS)
Decision
0,098 < 4,08
There was no effect from type of subject factor
for student learning concentration
Accept null hyphotesis
1,312 < 4,08
There was no effect from type of room factor
for student learning concentration
Accept null hyphotesis
24,976 > 4,08
There was an effect between time and type of
subject factor for student learning concentration
Reject null hyphotesis
0,271 < 4,08
There was no effect between time and type of
room factor for student learning concentration
Accept null hyphotesis
0,173 < 4,08
Accept null hyphotesis
1,832 < 4,08
Accept null hyphotesis
There was no effect between type of subject
and type of room factor for student learning
concentration
There was no effect between time, type of
subject and type of room factor for student
learning concentration
Figure 6: Group Bourdon Test
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Sarvia and Sentosa
Figure 7 : Maximum Point of the Students’ Learning Concentration (hours)
\
Table 4. Analysis of Three Methods
Data Collection
Method
Subjective
Visual Analogue Scale (VAS)
Objective
Group Bourdon Test
Subjective-Objective
Observation in the classroom
Conclusion
There was an effect for students’
concentration before treatment and after
treatment
There was an effect for students’
concentration before treatment and after
treatment
There was an effect for students’
concentration before treatment and after
treatment
Final Conclusion
There was a significant
decrease from students’
learning concentration prior
student learning activities in
the classroom to the
students’ learning
concentration after learning
activities in the classroom
5. CONCLUSION
REFERENCES
From Visual Analogue Scale graphic and Group
Bourdon Test graphic, there was a significant decrease from
students’ learning concentration prior student learning
activities in the classroom to the students’ learning
concentration after learning activities in the classroom.
Based on Anova Testing and analysis result, it was found
the conclusion that there were 2 factors that affected the
students’ learning concentration decrease, which was a
factor of time (Factor A) and the interaction between the
time factor and the type of subject factor (AB Factor
Interactions). Based on descriptive statistics analysis,
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hour (maximum 1,139 hours) in accordance with the initial
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