Design of Intelligent Tutoring System for Mastery Learning
Advanced Science Letters
ISSN: 1936-6612 (Print): EISSN: 1936-7317 (Online)
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Southern Oregon University, USA.
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University of Patras, Greece.
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Sichuan University, CHINA.
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Sergei Kulik, M.V.Lomonosov Moscow State University, Russia
Harald Kunstmann, Institute for Meteorology and Climate Research, Forschungszentrum
Karlsruhe, Germany
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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
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Wolfgang Rhode, University of Dortmund, Germany
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Andrea Sella, University College London, London, UK
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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
Alexander V. Zolotaryuk, Ukrainian Academy of Sciences, Ukraine
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)
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Dynamic Failure Detection in Distributed Environment
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An Adaptive JPEG Image Compression Using Psychovisual Model
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Modeling and Simulation Library's Service for Net Generation: A System Dynamics
Framework
Lily Puspa Dewi and Erma Suryani
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Controlling the Signal Applied from Fluidic Proximity Sensor
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An Optimal Tchebichef Moment Quantization Using Psychovisual Threshold for
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Exchange for Oil and Gas Contractors
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Adv. Sci. Lett. 20, 144-147 (2014)
Analysis Study on Human Emotion in Human-Robot Collaboration
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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
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System Development and Gap Analysis Enterprise Resources Planning in Oracle
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in Cloud Computing by Machine Learning Techniques
Mostafa Behzadi, Ramlan Mahmod, Mehdi Barati, Azizol Bin Hj Abdullah, and Mahda
Noura
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Norwati Mustapha
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Systems Template Prototyping Using Rapid By-Customer Demand with Business
Principles Approach and Systems Features Analysis
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Ubaidillah Zuhdi, Nur Arief Rahmatsyah Putranto, and Ahmad Danu Prasetyo
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Forecast to Plan Cycle in Oracle E Business Suite (Case Study Automotive
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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
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A Prototype Model for Handling Fuzzy Query in Voice Search on Smartphones
Dae-Young Choi
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Haryani Haron and M. Hamiz
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A Conceptual Analysis on Mobile Reminiscence Transactive Memory
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Feature Subset Selection Using Genetic Algorithm for Intrusion Detection System
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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
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Wirot Yotsawat and Anongnart Srivihok
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Community Detection Methods in Social Network Analysis
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Sim Choon Ling, Azian Azamimi Abdullah, and Wan Khairunizam Wan Ahmad
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Mapping of Quality of Service Parameter with Monitoring End-to-End Method of ITUT Y.1541 Standard in the Hotspot Area
Muhammad Agung Nugroho and Ferry Wahyu Wibowo
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Acceptance of Internet Banking Systems Among Young Users; the Effect of
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Arithmetic Coding Differential Evolution with Local Search
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The Impacts of e-Service Quality and e-Customer Satisfaction on e-Customer
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Context-Aware Mobile Patient Monitoring Framework Development: An Architectural
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A Conceptual Framework: Dynamic Path Planning System for Simultaneous
Localization and Mapping Multirotor UAV
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Effectiveness of Integrated Algorithm-Program Visualization: A Case Study with the
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Analysis of Satisfaction and Improvement Design of Electronic Insurance Claim
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Improvement of Text Compression Using Subset of Words
Jan Platos
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A Tensor-Based Approach for Semantic Recommenders in eGovernment
Luis Álvarez Sabucedo, Juan M. Santos Gago, and Manuel J. Fernández Iglesias
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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
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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
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E-Tendering Security Issues and Countermeasures
Mohd Afdhal Muhammad Robie, Haslina Mohd, Fauziah Baharom, Norida Muhd Darus,
Azman Yassin, and Wan Farez Saini Muner
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A Methodology for the Development of New Mobile Application Service Concepts
Moon-Soo Kim and Chulhyun Kim
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Pollard Rho Algorithm for Elliptic Curves Over with Negation and Frobenius Map
Intan Muchtadi-Alamsyah, Tomy Ardiansyah, and Sa'aadah Sajjana Carita
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Prediction of Domestic Water Leakage Based on Consumer Water Consumption
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RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
Copyright © 2014 American Scientific Publishers
All rights reserved
Printed in the United States of America
Advanced Science Letters
Vol. 20 (1), 213-217, 2014
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
Department of Electrical Engineering and Information Technology, Univeritas Gadjah Mada, Yogyakarta Indonesia
4
Department of Education Psychology, Universitas Gadjah Mada, Indonesia
2,3
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
1
Adv. Sci. Lett. Vol. 20, No. 1, 2014
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
One of the problems in the implementation of
mastery learning, is that each student has a different
learning speed making it difficult for teachers to give
students individual treatment, especially in the number of
students learning a lot. ITS can be used to solve this
problem because it can be integrated with the LMS to
handle many of the students but is able to provide
individual treatment.
doi: 10.1166/asl.2014.5313
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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.
remember, (2) understand, (3) apply, (4) analyze,
evaluate, and (6) create. On the other hand,
knowledge process consists of knowledge about:
factual, (2) conceptual, (3) procedural, and
metacognitive.
Table 1. Revised taxonomy table9
3. OBJECTIVE AND MASTERY LEARNING
4. MODEL FORMULATION
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)
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
Knowledge
Factual
Conceptual
Procedural
Metacognitive
Remember
Activity
1
Activity
3
-
No
Level
1
Repetition
2
Completion
3
Enrichment
(5)
the
(1)
(4)
Cognitive
Understand Apply Analyze Evaluate Create
Activity
2
Activity
Objective
4
-
-
-
-
-
-
-
-
-
-
Sub Part
Factual
Conceptual
Conceptual
Procedural
Procedural
Metacognitive
Cognitive Level
1, 2
2, 3
2, 3, 4
2, 3, 4, 5
4, 5, 6
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.
Every intelligent learning has a pre-test and post-test,
which function as a placement test. Pre-test is to determine
the initial state of the material or learn the topic for students.
The state of student learning indicates the level of
understanding for students learn at mastery learning. Student
learning state measured by the test with a value between 0
2
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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.
Start
Pre Test
S/L
F/L
Less
P/G
R/L
S/G
Good
F/L
Time is over
S/E
P/E
R/G
Excellent
R/E
F/G
Time is over
Post test
Time is over or
Pass
End
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":
3
Adv. Sci. Lett. Vol. 20, No. 1, 2014
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
progress
3. One way to determine a student's learning
progress is to measure the ability to answer
random questions from a subject of study
Intelligent 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
vi g qi ( Ri ) Pij ( Ri )v j
jI
c. Determining decision k to each condition i I so
that giving maximum value:
k N
Maximum, qi Pij (a ).v j ( R ) g
kA( i )
jI
d. Value k maximum to each condition i resulting
new decision R={Ri, iI}
k
e. If the new decision resulting qi is not equal to
previous decision then repeated the step b and c,
otherwise stop
f. 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
Intelligent learning module (ITS module) is applied
with respect to the learning outcomes of the lecturing set.
The working principle is choosing sequence ITS module by
doi: 10.1166/asl.2014.5313
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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
PSQ
State
L
A:{Teach}
State
So
C
Correct
Q:{Questions}
1-PSQ
IC
Incorrect
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
qIK Pijk Rijk
j i
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
g vi qi R Pijk Rijk
j i
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
vi g qi ( Ri ) Pij ( Ri )v j
jI
e. Determining decision k to each condition i I so
that giving maximum value:
N
Maximum, qik Pij (a ).v j ( R ) g
kA( i )
jI
f. Value k maximum to each condition I resulting new
decision R= {RI, I (I}
k
g. If the new decision resulting qi 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
m. Giving the posttest.
4
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
Table 3. Matrix of ITS states
REFERENCES
Probability Reward
State
(1:Excellent, Alternative
2:Good,
K
3:Less)
State
i=1, 2, 3
Pijk
Rijk
EIR (qi)
State
j=1, 2, 3
State
j=1, 2,3
qik Pi ,kj Rik, j
[1]
N
j 1
3
Pass
(k=1)
Pi1, j
Ri1, j q11 Pi1, j Ri1, j
Repeat
(k=2)
Pi ,2j
Ri2, j q12 Pi ,2j Ri2, j
Fail
(k=3)
Pi ,3j
Ri3, j q13 Pi ,2j Ri2, j
[2]
j 1
3
[3]
j 1
3
j 1
[4]
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.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
SUGGESTION
The 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.
5
[5]
Adv. Sci. Lett. Vol. 20, No. 1, 2014
[13]
[14]
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doi: 10.1166/asl.2014.5313
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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)
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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
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RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
Copyright © 2014 American Scientific Publishers
All rights reserved
Printed in the United States of America
Advanced Science Letters
Vol. 20 (1), 213-217, 2014
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
Department of Electrical Engineering and Information Technology, Univeritas Gadjah Mada, Yogyakarta Indonesia
4
Department of Education Psychology, Universitas Gadjah Mada, Indonesia
2,3
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
1
Adv. Sci. Lett. Vol. 20, No. 1, 2014
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
One of the problems in the implementation of
mastery learning, is that each student has a different
learning speed making it difficult for teachers to give
students individual treatment, especially in the number of
students learning a lot. ITS can be used to solve this
problem because it can be integrated with the LMS to
handle many of the students but is able to provide
individual treatment.
doi: 10.1166/asl.2014.5313
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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.
remember, (2) understand, (3) apply, (4) analyze,
evaluate, and (6) create. On the other hand,
knowledge process consists of knowledge about:
factual, (2) conceptual, (3) procedural, and
metacognitive.
Table 1. Revised taxonomy table9
3. OBJECTIVE AND MASTERY LEARNING
4. MODEL FORMULATION
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)
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
Knowledge
Factual
Conceptual
Procedural
Metacognitive
Remember
Activity
1
Activity
3
-
No
Level
1
Repetition
2
Completion
3
Enrichment
(5)
the
(1)
(4)
Cognitive
Understand Apply Analyze Evaluate Create
Activity
2
Activity
Objective
4
-
-
-
-
-
-
-
-
-
-
Sub Part
Factual
Conceptual
Conceptual
Procedural
Procedural
Metacognitive
Cognitive Level
1, 2
2, 3
2, 3, 4
2, 3, 4, 5
4, 5, 6
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.
Every intelligent learning has a pre-test and post-test,
which function as a placement test. Pre-test is to determine
the initial state of the material or learn the topic for students.
The state of student learning indicates the level of
understanding for students learn at mastery learning. Student
learning state measured by the test with a value between 0
2
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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.
Start
Pre Test
S/L
F/L
Less
P/G
R/L
S/G
Good
F/L
Time is over
S/E
P/E
R/G
Excellent
R/E
F/G
Time is over
Post test
Time is over or
Pass
End
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":
3
Adv. Sci. Lett. Vol. 20, No. 1, 2014
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
progress
3. One way to determine a student's learning
progress is to measure the ability to answer
random questions from a subject of study
Intelligent 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
vi g qi ( Ri ) Pij ( Ri )v j
jI
c. Determining decision k to each condition i I so
that giving maximum value:
k N
Maximum, qi Pij (a ).v j ( R ) g
kA( i )
jI
d. Value k maximum to each condition i resulting
new decision R={Ri, iI}
k
e. If the new decision resulting qi is not equal to
previous decision then repeated the step b and c,
otherwise stop
f. 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
Intelligent learning module (ITS module) is applied
with respect to the learning outcomes of the lecturing set.
The working principle is choosing sequence ITS module by
doi: 10.1166/asl.2014.5313
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
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
PSQ
State
L
A:{Teach}
State
So
C
Correct
Q:{Questions}
1-PSQ
IC
Incorrect
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
qIK Pijk Rijk
j i
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
g vi qi R Pijk Rijk
j i
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
vi g qi ( Ri ) Pij ( Ri )v j
jI
e. Determining decision k to each condition i I so
that giving maximum value:
N
Maximum, qik Pij (a ).v j ( R ) g
kA( i )
jI
f. Value k maximum to each condition I resulting new
decision R= {RI, I (I}
k
g. If the new decision resulting qi 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
m. Giving the posttest.
4
RESEARCH ARTICLE
Adv. Sci. Lett. 20 (1), 213–217, 2014
Table 3. Matrix of ITS states
REFERENCES
Probability Reward
State
(1:Excellent, Alternative
2:Good,
K
3:Less)
State
i=1, 2, 3
Pijk
Rijk
EIR (qi)
State
j=1, 2, 3
State
j=1, 2,3
qik Pi ,kj Rik, j
[1]
N
j 1
3
Pass
(k=1)
Pi1, j
Ri1, j q11 Pi1, j Ri1, j
Repeat
(k=2)
Pi ,2j
Ri2, j q12 Pi ,2j Ri2, j
Fail
(k=3)
Pi ,3j
Ri3, j q13 Pi ,2j Ri2, j
[2]
j 1
3
[3]
j 1
3
j 1
[4]
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.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
SUGGESTION
The 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.
5
[5]
Adv. Sci. Lett. Vol. 20, No. 1, 2014
[13]
[14]
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