Advanced Science Letters
ISSN: 1936-6612 (Print): EISSN: 1936-7317 (Online)
Copyright © 2000-2015 American Scientific Publishers. All Rights Reserved.
EDITORIAL BOARD
EDITOR-IN-CHIEF
Dr. Hari Singh Nalwa,
USA
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ADVANCED SCIENCE LETTERS
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EUROPEAN EDITOR
Prof. Dr. Wolfram Schommers
Forschungszentrum Karlsruhe
Institut für Wissenschaftliches Rechnen
D-76021 Karlsruhe, Germany
Tel.: +49-7247-82-2432
Fax: +49-7247-82-4972
HONORARY EDITORS
Richard Ernst*
, ETH Zürich, Switzerland
Eric B. Karlsson+
, Uppsala University, Sweden
Douglas Osheroff*
, Stanford University, USA
*Nobel Prize Laureate
+Member of the Nobel Committee for Physics 1987-1998 (chairman in 1998)
ASSOCIATE EDITORS
Diederik Aerts
(Quantum theory, Cognition, Evolution theory)
Brussels Free University, Belgium.
Yakir Aharonov
(Physics, Quantum Physics)
School of Physics and Astronomy, Israel.
Jim Al-Khalili
(Foundations of Physics, Nuclear Reaction Theory)
University of Surrey, UK.
Jake Blanchard
(Engineering Physics, Nuclear Engineering)
University of Wisconsin–Madison, USA.
Simon Baron-Cohen
(Cognitive Neuroscience)
University of Cambridge, UK.
Franz X. Bogner
(Cognitive Achievement)
University of Bayreuth, Germany.
John Borneman
(Anthropology)
Princeton University, USA.
John Casti
(Complexity Science)
Internationales Institut für Angewandte Systemanalyse, Austria.
Masud Chaichian
(High Energy Physics, String Theory)
University of Helsink, Finland.
Sergey V. Chervon
(Gravitation, Cosmology, Astrophysics)
Ulyanovsk State Pedagogical University, Russia
Kevin Davey
(Philosophy of Science)
University of Chicago, Chicago, USA.
Tania Dey
(Colloids/Polymers/Nanohybrids)
Canada.
Roland Eils
(Bioinformatics)
Deutsches Krebsforschungszentrum Heidelberg, Germany.
Thomas Görnitz
(Quantum theory, Cosmology)
University of Frankfurt, Germany.
Bert Gordijn
(Nanoethics, Neuroethics, Bioethics)
Radboud University Nijmegen, The Netherlands.
Ji-Huan He
(Textile Engineering, Functional Materials)
Soochow University, Suzhou, China.
Nongyue He
(Biosensors/Biomaterials)
China.
Irving P. Herman
(Materials and Solid State Physics)
Columbia University, USA.
Jucundus Jacobeit
(Climate, Global Change Ecology)
University of Augsburg, Germany.
Yuriy A. Knirel
(Bioorganic Chemistry)
N. D. Zelinsky Institute of Organic Chemistry, Russia.
Arthur Konnerth
(Neurophysiology, Molecular Mechanisms)
University of Munich, Germany.
G. A. Kourouklis
(Physics Solid State Physics)
Aristotle University Thessaloniki, Greece.
Peter Krammer
(Genetics)
Deutsches Krebsforschungszentrum Heidelberg, Germany.
Andrew F. Laine
(Biomedical Engineering)
Columbia University, USA.
Minbo Lan
(Organic Functional Materials)
China.
Martha Lux-Steiner
(Physics, Materials Science)
Hahn-Meitner-Institut Berlin, Germany.
Klaus Mainzer
(Complex Systems, Computational Mind, Philosophy of Science)
University of Augsburg, Germany.
JoAnn E. Manson
(Medicine, Cardiovascular Disease)
Harvard University, USA.
Mark P. Mattson
(Neuroscience)
National Institute on Aging, Baltimore, USA.
Lucio Mayer
(Astrophysics, Cosmology)
ETH Zürich, Switzerland.
Efstathios Meletis
(Physics, Thin films, Nanomaterials, Corrosion, Tribology)
University of Texas at Arlington, USA.
Karl Menten
(Radioastromy)
Max-Planck-Institut für Radioastromie, Germany.
Yoshiko Miura
(Biomaterials/Biosensors)
Japan.
Fred M. Mueller
(Solid State Physics)
Los Alamos National Laboratory, USA.
The Institute for Molecular Medicine, Huntington Beach, USA.
Nina Papavasiliou
(DNA Mutators, Microbial Virulence, Antiviral Defence, Adaptive
Immunity, Surface Receptor Variation)
The Rockefeller University, New York, USA.
Panos Photinos
(Physics)
Southern Oregon University, USA.
Constantin Politis
(Physics, Engineering)
University of Patras, Greece.
Zhiyong Qian
(Biomedical Engineering, Biomaterials, Drug Delivery)
Sichuan University, CHINA.
Reinhard Schlickeiser
(Astrophysics, Plasma Theory and Space Science)
Ruhr-Universität Bochum, Germany.
Surinder Singh
(Sensors/Nanotechnology)
USA.
Suprakas Sinha Ray
(Composites/Polymer Science)
South Africa.
Koen Steemers
(Architechture, Environmental Building Performance)
University of Cambridge, UK.
Shinsuke Tanabe
(Environmental Chemistry and Ecotoxicology)
Ehime University, Japan.
James R. Thompson
(Solid State Physics)
The University of Tennessee, USA.
Uwe Ulbrich
(Climat, Meteorology)
Freie Universität Berlin, Germany.
Ahmad Umar
(Advanced Materials)
Najran University, Saudi Arabia.
Frans de Waal
(Animal Behavior and Cognition)
Emory University, USA.
EDITORIAL BOARD
Filippo Aureli,
Liverpool John Moores University, UK
Marcel Ausloos,
Université de Liège, Belgium
Martin Bojowald,
Pennsylvania State University, USA
Sougato Bose,
University College, London, UK
Jacopo Buongiorno,
MIT, USA
Paul Cordopatis,
University of Patras, Greece
Dionysios Demetriou Dionysiou,
University of Cincinnati, USA
Simon Eidelman,
Budker Institute of Nuclear Physics, Russia
Norbert Frischauf,
QASAR Technologies, Vienna, Austria
Toshi Futamase,
Tohoku University, Japan
Leonid Gavrilov,
University of Chicago, USA
Vincent G. Harris,
Northeastern University, USA
Mae-Wan Ho,
Open University, UK
Keith Hutchison,
University of Melbourne, Australia
David Jishiashvili,
Georgian Technical University, Georgia
George Khushf,
University of South Carolina, USA
Sergei Kulik,
M.V.Lomonosov Moscow State University, Russia
Harald Kunstmann,
Institute for Meteorology and Climate Research, Forschungszentrum
Karlsruhe, Germany
Alexander Lebedev,
Laboratory of Semiconductor Devices Physics, Russia
James Lindesay,
Howard University, USA
Michael Lipkind,
Kimron Veterinary Institute, Israel
Nigel Mason,
Open University, UK
Johnjoe McFadden,
University of Surrey, UK
B. S. Murty,
Indian Institute of Technology Madras, Chennai, India
Heiko Paeth,
Geographisches Institut der Universität Würzburg, Germany
Matteo Paris,
Universita' di Milano, Italia
David Posoda,
University of Vigo, Spain
Paddy H. Regan,
University of Surrey, UK
Leonidas Resvanis,
University of Athens, Greece
Wolfgang Rhode,
University of Dortmund, Germany
Derek C. Richardson,
University of Maryland, USA
Carlos Romero,
Universidade Federal da Paraiba, Brazil
Andrea Sella,
University College London, London, UK
P. Shankar,
Indira Gandhi Centre for Atomic Research, Kalpakkam, India
Leonidas Sotiropoulos,
University of Patras, Greece
Roger Strand,
University of Bergen, Norway
Karl Svozil,
Technische Universität Wien, Auastria
Kit Tan,
University of Copenhagen, Denmark
Roland Triay,
Centre de Physique Theorique, CNRS, Marseille, France
Rami Vainio,
University of Helsinki, Finland
Victor Voronov,
Bogoliubov Laboratory of Theoretical Physics, Dubna, Russia
Andrew Whitaker,
Queen's University Belfast, Northern Ireland
Lijian Xu,
Hunan University of Technology, China
Alexander Yefremov,
Peoples Friendship University of Russia, Russia
Avraam Zelilidis,
University of Patras, Greece
TABLE OF CONTENTS
A SPECIAL ISSUE
Selected Peer-Reviewed Articles from 2013 International Conference on Internet
Services Technology and Information Engineering (ISTIE 2013), Bogor, Indonesia,
11-12 May 2013
Guest Editors:
Ford Lumban Gaol and Benfano Suwito
Adv. Sci. Lett. 20, 1-2 (2014)
RESEARCH ARTICLES
A Study on Intelligent System for Fire Rescue Operation
Azwin Hazrina Ahmad, Ahmad Kamil Mahmood, Jafreezal Jaafar, and Ahmad Aminudin
Abdul Rahim
Adv. Sci. Lett. 20, 3-7 (2014)
Placement and Configuration of Antenna for Indoor Femtocell Application
Ita Triana, Rina Pudji Astuti, and Budi Prasetya
Adv. Sci. Lett. 20, 8-14 (2014)
Semantics Weaving in Aspects Modeling for Mobile Software
Yong-Yi Fan Jiang, Wan-Jung Huang, and Yang Syu
Adv. Sci. Lett. 20, 15-20 (2014)
Dynamic Failure Detection in Distributed Environment
Ahmad Shukri Mohd Noor and Mustafa Mat Deris
Adv. Sci. Lett. 20, 21-25 (2014)
An Adaptive JPEG Image Compression Using Psychovisual Model
Ferda Ernawan, Nur Azman Abu, and Nanna Suryana
Adv. Sci. Lett. 20, 26-31 (2014)
A Study of Cloud Computing Performance as a Game Platform: An Indonesia Case
Aldo Wijanarko and Raymond Kosala
Adv. Sci. Lett. 20, 32-36 (2014)
Rapid Identification Method of Aerobic Bacteria in Diabetic Foot Ulcers Using
Electronic Nose
Azian Azamimi Abdullah, Tan Woei Jing, Chua Ai Sie, Nurlisa Yusuf, Ammar Zakaria,
Mohammad Iqbal Omar, Ali Yeon Md Shakaff, Abdul Hamid Adom, Latifah Munirah
Kamarudin, Yeap Ewe Juan, Amizah Othman, and Mohd Sadek Yassin
Adv. Sci. Lett. 20, 37-41 (2014)
Gesture Recognition Based on Bayesian Inference of Distributed Arm Trajectory
Khairunizam Wan, Azri A. Aziz, S. K. Za'aba, A. B. Shahriman, Nasir Ayob, Azian Azamimi
Abdullah, and Zuradzman M. Razlan
Control and Analysis of the Automatic Industrial-Position Sensor
Anna Antonyová and Peter Antony
Adv. Sci. Lett. 20, 47-50 (2014)
Optimal Control Solution of a Tuberculosis Transmission Model with Recurrent
Infection and Vaccination Using C# Programming
J. Nainggolan, Sudradjat Supian, A. K. Supriatna, N. Anggriani, and Detiatrimargini
Adv. Sci. Lett. 20, 51-55 (2014)
A Design of 6LoWPAN Routing Protocol Border Router with Multi-Uplink Interface:
Ethernet and Wi-Fi
Ardiansyah Musa Efendi, Ali Fahmi Perwira Negara, Oh Seung Kyo, and Deokjai Choi
Adv. Sci. Lett. 20, 56-60 (2014)
Modeling and Simulation Library's Service for Net Generation: A System Dynamics
Framework
Lily Puspa Dewi and Erma Suryani
Adv. Sci. Lett. 20, 61-65 (2014)
Controlling the Signal Applied from Fluidic Proximity Sensor
Anna Antonyová and Peter Antony
Adv. Sci. Lett. 20, 66-69 (2014)
An Optimal Tchebichef Moment Quantization Using Psychovisual Threshold for
Image Compression
Ferda Ernawan, Nur Azman Abu, and Nanna Suryana
Adv. Sci. Lett. 20, 70-74 (2014)
Trip Guidance: A Linked Data Based Mobile Tourists Guide
Hendrik and Dheddy Husada Fadjar Perdana
Adv. Sci. Lett. 20, 75-79 (2014)
A Comparison of Extraction Techniques for the Rapid Electroencephalogram-P300
Signals
Arjon Turnip, Haryadi, Demi Soetraprawata, and Dwi Esti Kusumandari
Adv. Sci. Lett. 20, 80-85 (2014)
Adopting Online Course into Higher Education Curriculum
Yahya Peranginangin, Andry Alamsyah, and Gadang Ramantoko
Adv. Sci. Lett. 20, 86-90 (2014)
A Multi Agent Framework (MAFSNUD) for Belief-Desire-Intention (BDI) Model's
Decision-Making Problem in Dynamic Situations: An Overview
Ileladewa Adeoye Abiodun and Cheng Wai Khuen
Adv. Sci. Lett. 20, 91-96 (2014)
Enhancement of 5-Axis Micromilling Visibility Through Visualization, Simulation,
and Video Streaming in a Faceted-Based CAM-System
Trusted Information Supply Chain Framework in Clinical Studies
Thomas Kandl and Nawaz Khan
Adv. Sci. Lett. 20, 103-110 (2014)
Frame Error Estimation for Distributed Coordination Function Scheme with High
Throughput-Physical Performance Evaluation
Teuku Yuliar Arif and Riri Fitri Sari
Adv. Sci. Lett. 20, 111-115 (2014)
Comparison of Image Pre-Processing Approach for Classification of Intestinal
Parasite Ascaris Lumbricoides Ova and Trichuris Trichiura Ova
K. H. Ghazali, R. S. Hadi, and M. Zeehaida
Adv. Sci. Lett. 20, 116-119 (2014)
Dynamic
Q
-Learning for Intersection Traffic Flow Control Based on Agents
Felipe P. Vista IV, Xuan Zhou, Ji Hyoung Ryu, and Kil To Chong
Adv. Sci. Lett. 20, 120-123 (2014)
BPMN and SoaML Synergy en Route to Implement Digital Identity-Related Privacy
as a Set of Services
Ghazi Ben Ayed and Nourchene Elleuch-Ben Ayed
Adv. Sci. Lett. 20, 124-128 (2014)
SKK Migas Sistem Operasi Terpadu-Performance Evaluation for Secure Data
Exchange for Oil and Gas Contractors
Bobby Suryajaya, Charles Lim, and Budi Ibrahim
Adv. Sci. Lett. 20, 129-133 (2014)
Comparing the Interface Design of a Cycling Web and the App Version
Sin-Ho Chin
Adv. Sci. Lett. 20, 134-138 (2014)
An Algebraic Approach for Sentence Based Feature Extraction Applied for
Automatic Text Summarization
Nowshath Kadhar Batcha and Normaziah Abdul Aziz
Adv. Sci. Lett. 20, 139-143 (2014)
Genetic Algorithms-Based Quality of Service Service Selection in Cloud Computing
Using Multilayer Perceptron
Ahmad Mosallanejad, Amir Rajabi Behjat, Aida Mustapha, and Rodziah Atan
Adv. Sci. Lett. 20, 144-147 (2014)
Analysis Study on Human Emotion in Human-Robot Collaboration
Automated Visual Inspection for Surgical Instruments Based on Spatial Feature and
K-Nearest Neighbor Algorithm
Murtadha Basil Abbas, Anton Satria Prabuwono, Siti Norul Huda Sheikh Abdullah, and
Rizuana Iqbal Hussain
Adv. Sci. Lett. 20, 153-157 (2014)
Designing the Key Detection of Text Steganalysis Based
Roshidi Din
Adv. Sci. Lett. 20, 158-163 (2014)
Software Certification and Its Implementation in Malaysian Software Industry: A
Survey
Shafinah Farvin Packeer Mohamed, Fauziah Baharom, Aziz Deraman, and Haslina Mohd
Adv. Sci. Lett. 20, 164-168 (2014)
System Development and Gap Analysis Enterprise Resources Planning in Oracle
Procurement (Case Study: PT. XYZ)
Agung Terminanto
Adv. Sci. Lett. 20, 169-174 (2014)
A New Framework for Classification of Distributed Denial of Service (DDOS) Attack
in Cloud Computing by Machine Learning Techniques
Mostafa Behzadi, Ramlan Mahmod, Mehdi Barati, Azizol Bin Hj Abdullah, and Mahda
Noura
Adv. Sci. Lett. 20, 175-178 (2014)
Students' Help Seeking: Towards a Design of Supporting Tools
Shamsheer Jauhangeer and Maizatul Akmar Ismail
Adv. Sci. Lett. 20, 179-183 (2014)
The Impact of Enterprise Resource Planning Success in Indonesia Company
Sevenpri Candra
Adv. Sci. Lett. 20, 184-187 (2014)
Feature Subset Selection Using Binary Quantum Particle Swarm Optimization for
Spam Detection System
Amir Rajabi Behjat, Aida Mustapha, Hossein Nezamabadi-Pour, Md. Nasir Sulaiman, and
Norwati Mustapha
Adv. Sci. Lett. 20, 188-192 (2014)
Systems Template Prototyping Using Rapid By-Customer Demand with Business
Principles Approach and Systems Features Analysis
Felipe P. Vista IV and Kil To Chong
Adv. Sci. Lett. 20, 193-198 (2014)
Encouraging Information and Communication Technology Sectors Using
Input-Output Approach: The Case of Indonesia
Forecast to Plan Cycle in Oracle E Business Suite (Case Study Automotive
Company)
Agung Terminanto
Adv. Sci. Lett. 20, 203-208 (2014)
Leaf Recognition Based on Leaf Tip and Leaf Base Using Centroid Contour Gradient
Bong Mei Fern, Ghazali Bin Sulong, and Mohd Shafry Mohd Rahim
Adv. Sci. Lett. 20, 209-212 (2014)
Design of Intelligent Tutoring System for Mastery Learning
Dwijoko Purbohadi, Lukito Nugroho, Insap Santosa, and Amitya Kumara
Adv. Sci. Lett. 20, 213-217 (2014)
Cloud Computing-Based Smart Home-Based Rehabilitation Nursing System for
Early Intervention
Huai-Te Chen, Mei-Hui Tseng, Lu Lu, Jheng-Yi Sie, Yu-Jyuan Chen, Yufang Chung, and
Feipei Lai
Adv. Sci. Lett. 20, 218-221 (2014)
A Prototype Model for Handling Fuzzy Query in Voice Search on Smartphones
Dae-Young Choi
Adv. Sci. Lett. 20, 222-225 (2014)
An Ontological Framework to Preserve Malay Indigenous Health Knowledge
Haryani Haron and M. Hamiz
Adv. Sci. Lett. 20, 226-230 (2014)
A Conceptual Analysis on Mobile Reminiscence Transactive Memory
Sabiroh Md Sabri, Haryani Haron, and Nursuriati Jamil
Adv. Sci. Lett. 20, 231-234 (2014)
Feature Subset Selection Using Genetic Algorithm for Intrusion Detection System
Amir Rajabi Behjat, Najmeh Vatankhah, and Aida Mustapha
Adv. Sci. Lett. 20, 235-238 (2014)
Genetic Feature Selection for Software Defect Prediction
Romi Satria Wahono and Nanna Suryana Herman
Adv. Sci. Lett. 20, 239-244 (2014)
Data Mining of International Tourists in Thailand by Two Step Clustering and
Classification
Wirot Yotsawat and Anongnart Srivihok
Adv. Sci. Lett. 20, 245-249 (2014)
Community Detection Methods in Social Network Analysis
Andry Alamsyah, Budi Rahardjo, and Kuspriyanto
Design of an Automated Breast Cancer Masses Detection in Mammogram Using
Cellular Neural Network (CNN) Algorithm
Sim Choon Ling, Azian Azamimi Abdullah, and Wan Khairunizam Wan Ahmad
Adv. Sci. Lett. 20, 254-258 (2014)
Mapping of Quality of Service Parameter with Monitoring End-to-End Method of
ITU-T Y.1541 Standard in the Hotspot Area
Muhammad Agung Nugroho and Ferry Wahyu Wibowo
Adv. Sci. Lett. 20, 259-263 (2014)
User's Acceptance of HIS System: Role of Involvement, Organizational Support and
Training
Samsudin Wahab, Mohd Sazili Shahibi, Ahmad Nawir Abu Amrin, and Nik Norhaslida Ali
Adv. Sci. Lett. 20, 264-267 (2014)
Acceptance of Internet Banking Systems Among Young Users; the Effect of
Technology Acceptance Model
Mohd Shoki Md Ariff, S. M. Yeow, and Norhayati Zakuan
Adv. Sci. Lett. 20, 268-272 (2014)
Measuring Performance in the Public Administration Sector: An Analysis of
Websites Efficiency
Corrado lo Storto
Adv. Sci. Lett. 20, 273-276 (2014)
Detecting Seller Shilling Behavior Based on Bid Arrival Model
Chu-Fen Li
Adv. Sci. Lett. 20, 277-279 (2014)
Arithmetic Coding Differential Evolution with Local Search
Orawan Watchanupaporn and Worasait Suwannik
Adv. Sci. Lett. 20, 280-284 (2014)
The Impacts of e-Service Quality and e-Customer Satisfaction on e-Customer
Loyalty in Internet Banking
Mohd Shoki Md Ariff, Leong Ooi Yun, Norhayati Zakuan, and Khalid Ismail
Adv. Sci. Lett. 20, 285-289 (2014)
Incorporating Transportation Costs Into Integrated Inventory Model for Single
Supplier and Single Purchaser
Raden Achmad Chairdino Leuveano, Fairul Azni Bin Jafar, and Mohd Razali Bin
Muhamad
Adv. Sci. Lett. 20, 290-293 (2014)
Context-Aware Mobile Patient Monitoring Framework Development: An Architectural
Design
A Conceptual Framework: Dynamic Path Planning System for Simultaneous
Localization and Mapping Multirotor UAV
H. H. Trihaminto, A. S. Prabuwono, A. F. M. Saiffuddin Saif, and T. B. Adji
Adv. Sci. Lett. 20, 299-303 (2014)
Effectiveness of Integrated Algorithm-Program Visualization: A Case Study with the
3De-AlProV
Affandy, Nanna Suryana, and Burairah Husin
Adv. Sci. Lett. 20, 304-308 (2014)
Analysis of Satisfaction and Improvement Design of Electronic Insurance Claim
Service
M. Dachyar, M. Omar, and P. Andri Sena
Adv. Sci. Lett. 20, 309-311 (2014)
Improvement of Text Compression Using Subset of Words
Jan Platos
Adv. Sci. Lett. 20, 312-316 (2014)
A Tensor-Based Approach for Semantic Recommenders in eGovernment
Luis Álvarez Sabucedo, Juan M. Santos Gago, and Manuel J. Fernández Iglesias
Adv. Sci. Lett. 20, 317-320 (2014)
Modelling the Determinants Influencing the Need of Computer Simulation
Framework in Improving the Closing of Final Account in Construction Projects
Zarabizan Zakaria, Syuhaida Ismail, and Aminah Md Yusof
Adv. Sci. Lett. 20, 321-325 (2014)
A New Model for Multicriteria Risk-Based Tender Evaluation
Wan Farez Saini Muner, Fauziah Baharom, Azman Yasin, Haslina Mohd, Norida Muhd
Darus, Zaharin Marzuki, and Mohd Afdhal Muhammad Robie
Adv. Sci. Lett. 20, 326-330 (2014)
E
-Tendering Security Issues and Countermeasures
Mohd Afdhal Muhammad Robie, Haslina Mohd, Fauziah Baharom, Norida Muhd Darus,
Azman Yassin, and Wan Farez Saini Muner
Adv. Sci. Lett. 20, 331-335 (2014)
A Methodology for the Development of New Mobile Application Service Concepts
Moon-Soo Kim and Chulhyun Kim
Adv. Sci. Lett. 20, 336-339 (2014)
Pollard Rho Algorithm for Elliptic Curves Over with Negation and Frobenius Map
Intan Muchtadi-Alamsyah, Tomy Ardiansyah, and Sa'aadah Sajjana Carita
Adv. Sci. Lett. 20, 340-343 (2014)
Prediction of Domestic Water Leakage Based on Consumer Water Consumption
Data
Nur Rasyida Mohd Rashid, Khairul A. Rasmani, N. Shahari, Norul Fadhilah Ismail, Herma
Mohd Hanif, and Nor Aishah Md Noh
Using ZMET to Explore Exchange Experience for Applying in Location-Based
Service
Kaun-Yao Tseng and Wen-Huei Chou
Adv. Sci. Lett. 20, 348-351 (2014)
Structural Path Analysis of the Influences from Smallholder Oil Palm Plantation
Toward Household Income: One Aspect of e-Government Initative
Arif Imam Suroso and Arief Ramadhan
Adv. Sci. Lett. 20, 352-356 (2014)
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213
–
217, 2014
1 Adv. Sci. Lett. Vol. 20, No. 1, 2014 doi: 10.1166/asl.2014.5313
Copyright © 2014 American Scientific Publishers Advanced Science Letters All rights reserved Vol. 20 (1), 213-217, 2014 Printed in the United States of America
Design of Intelligent Tutoring System
For Mastery Learning
Dwijoko Purbohadi1,*, Lukito Nugroho2, Insap Santosa3, Amitya Kumara4
1
Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia
2,3
Department of Electrical Engineering and Information Technology, Univeritas Gadjah Mada, Yogyakarta Indonesia
4
Department of Education Psychology, Universitas Gadjah Mada, Indonesia
Intelligent Tutoring System (ITS) is an interactive e-learning tool to help students learn independently. Currently, the implementation ITS is still in laboratory scale, means ITS has not been used in large scale using internet technology. One of the difficulties is ITS has not fulfill yet the learning needs. The main requirement of current learning based-competency is the high number of students in mastery learning, on the other hand current learning activities could not overcome the problems of student characteristics and different learning speed in each student as well. The concept of mastery learning is an approach used in competency-based learning, which consists of repetition of learning for slow students, mastering for good progress students, and enrichment for fast students. In this paper, the model of ITS is designed to accommodate the competency-based mastery learning needs and be able to follow learning speed of each student. The design of ITS has learning sequences and gradually test student`s cognitive level. It could measure the position of student`s cognitive knowledge, be able to guide to reach mastery level, and give challenges for smart students.
Keywords: ITS, mastery learning, cognitive level, learning sequence
1. INTRODUCTION
Intelligent Tutoring System (ITS) is interactive application program that is used as an information, a learning and an evaluation medium. One of the ways to make students could be learnt independently is by using ITS1. ITS is developed based on Computer Aided Instructional (CAI). CAI is appropriate for both fast and slow students, because the speed and material learning sequences is chosen by the student itself. Then, Learning Management System (LMS) is an e-learning web-based tool that successfully to manage the activities and learning administration2. ITS and LMS is supporting each other so possible combine both of them become iLMS (Intelligent Learning Management System)3.
In conventional systems, the lecturer has a role as instructor and manager of learning activities. If an LMS is applied, the role as the instructor could be changed as a facilitator to the student to do learning activities independently.
*
Email Address: purbohadi@yahoo.com
LMS was designed as tools to manage the activities in order to the interactive tutorial facility need to be added. If an LMS is combined with ITS, the learning activities could be conducted by student anywhere and anytime. Then, the student has a higher opportunity to learn independently. If a student learns independently using ITS, means the teacher has a chance to monitor, to supervise student, and implement a variety of teaching strategies4 so that the number of students to mastery learning is higher.
2. PROBLEM STATEMENT
Adv. Sci. Lett. 20 (1), 213
–
217, 2014
RESEARCH ARTICLE
2
ITS can be used to help students achieve a certain learning objectives using the principles of mastery learning. In this paper the core of ITS is to provide a set of learning and assessment in order. ITS will adjust the learning sequences according to the level of understanding a student's pace. ITS shall be provided with a program to determine the next step of learning the most appropriate use of a specific algorithm, the paper uses Markov algorithm. The new problem that arises is how to use Markov algorithms for ITS sequence of learning so as to provide the most appropriate for a student to achieve learning objectives as quickly as possible according to the principles of mastery learning.
3. OBJECTIVE AND MASTERY LEARNING
The learning objective is competency for students that involved in an instructional set in form of learning activities. The result of this instructional activity is known by measuring the indicator by using diagnostic test. One of the learning objectives is achieved cognitive competency. The basic assumption of measuring indicator through diagnostic test is the amount of right answer shows the level of instructional objective achievement5. The students are considered the mastery learning if right answer at least 70% of total questions6. The learning based competency uses the mastery learning approach, in order to there are 95% student achieve mastery learning through an instructional set. The mastery learning is attained if the student has enough learning time and appropriate learning7.
The approach of mastery learning was conducted when giving the learning properly and all students able to learn well8. Principally, the mastery learning happens students give appropriate learning, timing of study, and additional learning for students who has not understood yet. These ways are hard to be implemented using conventional one, moreover in group learning. The lecturer is hard to control and handle one-by-one of the student that have difficulties example of the faculty associated with emotional expression9,10, body language and communication11,12, and the provision of educational messages9,10, as well as of the students there is a need for alternative learning experiences, such as the use of interactive software mentioned in several previous studies9,10,13,14. Not only time limitation, but also many reasons are faced. Therefore, this is why the design of ITS is required to help the concept of mastery learning. The advanced cognitive taxonomy that is already made and becomes to be an operational taxonomy15. Table 1 shows the example of the learning objective to understand a concept which has four activities (activity 1,2,3,4). Based on that taxonomy, the instructional design is easier to be made. The sentences arrange an instructional objective consists of processing statement (cognitive) using verb followed by an object that is processed (knowledge). The cognitive process consist of: (1)
remember, (2) understand, (3) apply, (4) analyze, (5) evaluate, and (6) create. On the other hand, the knowledge process consists of knowledge about: (1) factual, (2) conceptual, (3) procedural, and (4) metacognitive.
Table 1. Revised taxonomy table9
Knowledge Cognitive
Remember Understand Apply Analyze Evaluate Create
Factual Activity 1 Activity 2 - - - -
Conceptual - Objective - Activity
4 - -
Procedural Activity
3 - - - - -
Metacognitive - - - - - -
4. MODEL FORMULATION
Every subject of learnings has objective to reach certain competency, or called as objective of general instructional. This competency is reached through sub competency or objective of particular instructional. Each sub competency is a sub topic and each sub topic is arranged to be finished in a certain time. In each sub topic is arranged an intelligent learning system module (ITS) to help students to reach sub competency. Next, the approach of mastery learning is used to divide the sub topic into three levels, such as repetition, completion, and enrichment (Table 2).
Completion is the main part of learning to achieve sub competency, the repetition is the learning activities to help reach the sub competencies, and the enrichment is to develop knowledge of good students. Factual and conceptual material at a low cognitive level placed on the repetition of the students have studied their own assumptions or follow classroom learning activities, failing at the completion need to learn in the repetition. Mostly, the purpose of studying is on conceptual and procedural so that medium level is the subject of sub completions or a minimum standard of competency to be mastered by the students. Part enrichment material selected from procedural and metacognitive.
Table 2. The design of ITS
No Level Sub Part Cognitive Level
1 Repetition Factual 1, 2
Conceptual 2, 3
2 Completion Conceptual 2, 3, 4
Procedural 2, 3, 4, 5
3 Enrichment Procedural 4, 5, 6
Metacognitive 2, 3
Intelligent of learning formulation is to determine the next best learning material for students, after completing a set of learning competencies to achieve a sub as soon as possible and as deep as possible.
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and 100%, there are three conditions that are prescribed, namely:
1. Learning outcome “excellent”, if it achieves scores of 85%
2. Learning outcome “good”, if it achieves 65% score < 85%
3. Learning outcome “less”, if it is achieved scores of < 65%
Intelligent learning module state diagram shown in Figure 1 below. Good Less Excellent Pre Test Post test Start End P/E S/E S/G R/E S/L F/G F/L F/L R/L P/G R/G
Time is over or Pass Time is over
Time is over
Fig.1. Intelligent learning module state diagram
If students are categorized as "Excellent" means it can be said to achieve basic competence (achieve mastery learning). Students in this condition will be given a series of learning that leads to the enrichment of learning, although it is likely to fail. There are four possible outcomes of learning:
1. Finish (because of time limitation), ready to follow the post - test.
2. Pass with learning outcome “excellent” (P/E) ready to follow the post - test.
3. Repeat with learning outcome “excellent” (R/E) to give re-learning through enrichment materials 4. Failed with learning outcomes “good” (F/G) to give learning for achievement level learning
“medium” (mastering)
Students who have a category of "good" means nearly reach the principal competence (mastering) but still needs reinforcement learning in order to achieve excellent category. There are three possible outcomes of “good”:
1. Finish (because of time limitation), ready to follow the post - test.
2. Pass with learning outcome “excellent” (P/E) so
that able to get student achievement “excellent”
or enrichment.
3. Repeat with category “good” (R/G) ready to have re-learning
4. Failed with category “less” (F/L) must follow remedial
Students who have a category of "less" does not mean to say achieve basic competence though, they require to take a series of lessons to reach the category of "good". There are three possible outcomes of learning in the category of "less":
1. Finish (because of time limitation), out or try to follow the post – test
2. Pass with learning outcomes “good” (P/G), ready
to get learning with learning achievement “medium” or mastering
3. Repeat with category “less” (R/L), ready to get next learning with remedial learning
4. Failed with category “less” (F/L), that will be got link to out.
Decision making in ITS is used to determine next learning if a learning and test already be completed. There are two ways of decision-making that can be used: Markov algorithm (computational intelligence) or condition-action rules (unconventional intelligence). The intelligent learning module is built using three basic assumptions:
1.
A study subject (topic) consists of some basic concepts (sub-topic)2.
Mastery learning of a topic could be achieved through learning accustomed to the learning progress3.
One way to determine a student's learning progress is to measure the ability to answer random questions from a subject of studyIntelligent learning module (ITS) is implemented on a course that consists of theory learning in some subjects (topics) with many supporting concepts (sub-topics). The working principle is to choose the order of ITS Training (tutorials) and quizzes which are most appropriate for a student to achieve mastery learning a topic with reasonable time and steps.
The algorithm to determine the next state computationally using Markov decision theory has been used by Witheley4, the algorithm is as follows:
a. Choosing initial policy R = {Ri, iI}
b. Solving the equation:
N I j j i ij i ii
g
q
R
P
R
v
v
(
)
(
)
c. Determining decision k to each condition i I so that giving maximum value:
( )
,
( ).
( )
N k
i ij j k A i j I
Maximum
q
P a v
R
g
d. Value k maximum to each condition i resulting new decision R={Ri, iI}
e. If the new decision resulting
q
ik is not equal to previous decision then repeated the step b and c, otherwise stopf. The next learning is conducted as alternative k in convergent condition.
Above algorithm will be adapted as the basis for the design of ITS for mastery learning which will be described further in the next chapter.
5. DISCUSSION
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providing lecturing (tutorials) and quizzes that are most appropriate for students to achieve mastery learning a topic with reasonable time and steps. The state of learning is known by looking at the answers and score positions that have been obtained. Students, with the same score and answering questions with the same answer, have a different tutorial subject for next learning. It is depending on the calculation of the next position. More, the Witheley’s adaptation of the algorithm is described as follows:
a. Learning state
State So is a condition arising from an action taken by
the previous decision. State written by i = 0, 1, 2 .... , N and displacement state written by j = 0, 1, 2, ..., N. The set of all states is denoted by I. If (S0) is the student's current state then there are 3 possible subsequent actions and 2 states which is made (Figure 2), namely:
a. Learning state L b. Correct answer state C c. Incorrect answer state IC
State L C Correct IC Incorrect State So A:{Teach} Q:{Questions} PSQ 1-PSQ
Fig.2. States based on Witheley5
b. Next learning activities
Next activities or alternative decisions in ITS are all possible actions taken to a state, denoted by the notation k (I) = {1, 2, ...}. Next activity may be selected are:
a. Continuing to the lower level b. Repeating in the same topic
c. Repeating a new topic on the same level d. Continuing to the higher level
c. Learning transition opportunities
Opportunity of a process to move from one state to another on an alternative decision to k. Opportunity or probability of transition was written by notation PIJ
(k) , i, j = 1, 2, ... N.
d. Learning reward transition
The reward is earned income as interstate implications of the transition to the alternative decision-k. Reward denoted by RIJ(k), i, j = 1, 2, ... N. The reward has a value between -1 and 1 (excellent category), between -1 and 2 (good category), or between -1 to 3 (less category), the value of negative reward called the piece.
e. Next learning decision
The decision is a step in taking an action as a procedural policy to achieve a strategic steps to get the optimal next state for the next learning activity. Mathematically expressed as a set of all the decisions taken in each state after a learning activity done.
f. Optimal Decision
Decision or optimal policy is one of the best decisions among alternative decisions taken. Optimal decision established through a Markov decision process.
Mathematically expressed as a set of all decisions in any state that gives maximum reward or minimal pieces.
g. Expectation Immediate Reward q
Expectation Immediate Reward (EIR) is an award
granted before moving from one state to the next. EIR denoted using q, written by:
N i j k ij k ij KI
P
R
q
h. Reward Expectation Value v
Reward expectation value g(R) is if the system has been running up to infinite time, and use R to policy decisions, if vi= the value of the variable reward, can be
structured equation:
N i j k ij k ij ii
q
R
P
R
v
g
Where i= 1, 2, …. N.
Referring to the state diagram (Figure 1) and the formulation developed a state table can be set up as shown in table 2 below. The table consists of a state variable (i), alternative state (k), the next state probability PIJ
(k)
, reward obtained RIJ
(k)
, and the rewards are obtained according to the answer the EIR (Immediate Reward Expectation).
Table 3 shows the shape of the data matrix to be processed using Whiteley’s algorithm. Here is the procedure that will be done by the ITS’s program to determine the position of the best of next activity.
a. Giving the pretest.
b. Choosing initial value R = {Ri, i (I} by using the
pretest score, where R = pretest scores.
c. Giving a learning and a problem for a theme according to the value R.
d. Solving the equation after a question answered:
N I j j i ij i ii
g
q
R
P
R
v
v
(
)
(
)
e. Determining decision k to each condition i I so that giving maximum value:
( )
,
( ).
( )
N k
i ij j k A i j I
Maximum
q
P a v
R
g
f. Value k maximum to each condition I resulting new decision R= {RI, I (I}
g. If the new decision resulting
q
ik is equal to previous decision not then repeated the step 2 and 3, otherwise (convergent) stop.h. The next learning is conducted as alternative k in convergent condition.
i. Setting: new score = score + new R.
j. Check the results of learning for a new position and move within their corresponding scores.
k. Setting: R = new score.
l. Repeat steps 3 to step 10 until completion or timeout
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Table 3. Matrix of ITS states
6. CONCLUSION
From the above, the working principle is to choose the sequence of ITS module providing training (tutorials) and quizzes that are most appropriate for a learner to achieve mastery learning a topic with time and moves as possible. The next step is determined by looking at the current position and the results of calculations using the Whiteley’s algorithm. An adaptation of the algorithm is done by entering the algorithm into the learning cycle. Learning material adapted to a new position determined based on the score and the calculation of the best R value. Tutorial model of an intelligent learning module is built using three basic assumptions:
a. A study subject (topic) consists of some basic concepts (sub-topic)
b. Mastery learning a topic could be achieved through learning tailored to the learning progress. c. One way to determine a student`s learning
progress is to measure the ability to answer random questions from a subject of study.
7.
SUGGESTIONThe design of ITS with a complete learning
approach using adapted Whiteley’s algorithm needs to be
tested on the side of computing. Furthermore, require further study if applied to the higher number of users, this is necessary because the today mastery learning approach is designed to help a lecture to teach students in large numbers. ITS should be made using the SCORM standard so it can be included in an LMS. Adapted Whiteley’s algorithm involves matrix and repetition to solve an equation for each step and for each student. The greater the weight of students means a server so that the distributed model is more suitable.
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State (1:Excellent, 2:Good, 3:Less) Alternative K Probability k ij
P
Reward k ijR
EIR (qi)State j=1, 2, 3
State
j=1, 2,3
N j k j i k j i ki
P
R
q
1 , ,
State i=1, 2, 3
Pass (k=1)
1 ,j i
P
R
i1,j
3 1 1 , 1 , 1 1 j j i j iR
P
q
Repeat (k=2) 2 ,j iP
R
i2,j
3 1 2 , 2 , 2 1 j j i j iR
P
q
Fail (k=3) 3 ,j iP
R
i3,j
