A Proposal Model of developing
Intelligent Tutoring Systems based on Mastery Learning
Dr. Usama M. AbdelsalamAssistant Professor of Educational Technology Faculty of Educational - Hail University – Saudi Arabia Faculty of Educational - Suez Canal University – Egypt
Abstract:
The study aimed to propose a model for the production of intelligent Tutoring System (ITS) Based on mastery learning strategy, Through analysis of related previous studies; to get out of the outline used by researchers at the designing ITS, The study also analyzed the most important distinguishing characteristics for ITS programs, As well as the characteristics of mastery learning to be linked together Systemically, The study also identified mechanisms of action programs ITS , These mechanisms linking with variables mastery learning model that based on the theory of Bloom on Mastery Learning Model, And that the researcher conducted after modifications, as Researcher linking between the components of mastery learning With components of the ITS, The researcher divided the basic model of two sub-models: Educational model for the production of ITS and Technical model for the production of ITS. Keyword
:
Intelligent Tutoring Systems (ITS)- Mastery Learning- ITS Components- Technical
model-
Educationalmodel-
Domain Model- Tutorial Model- student model.1- Introduction
The use of computers in teaching and learning processes has a far-reaching impact on changing the learning process dramatically; and that has been shown on changing the learning styles, the roles of both teachers and learners, the structure system of schools, the emergence of new learning theories or making a change in the structure of some existing theories. Besides, emphasizing on the principles of self-learning, collaborative learning, cooperative learning, active learning and distance learning. While applying some interactive and integrated
processes to these changes, some new educational systems have appeared such as blended learning, e-learning and Intelligent Tutoring Systems (ITS), which are learner-centered teaching styles that depend mainly upon learners' potentials and their capabilities. Those teaching styles go along with the so-called mastery learning which provides a wide range of successful educational experiences for most learners, increases their confidence in their abilities and competence, and motivate them to further learning and achievement.
2- Intelligent Tutoring Systems ITS:
As a result of the application of the principles of artificial intelligence in education, two types of systems have appeared: Intelligent Tutoring Systems (ITSs), Expert Systems (ES). Despite the fact that ES is one of the most important areas of artificial intelligence in general [1], ITS are the first and main application of (AI) in the educational processes in particular [2]. Intelligent Tutoring System is also called: Intelligent Tutoring Program, [3], Tutorial Program [4], Shehata also showed [5] a number of terms such as Intelligent Tutor, Intelligent Computer Assisted Instruction System, Intelligent Learning Environment (ILE), Expert Tutoring System (ETS). Intelligent Tutoring System (ITS) is considered the prevalent term.
Saleh [6] has defined ITS as: "it is a program which entirely supports learners to the extent that they master their learning. This program is characterized by its ability to generate infinite number of exercises and problems according to a particular sequence. It also examines the learner's abilities and identifies his weaknesses and improves them". Mortiz [7] has defined ITS as: "it is an Educational Software contains one or more of the smart components. This software enables
Educational experts to apply their knowledge to solve the learner's problems. This knowledge can be used in analyzing the learner's answers and comparing his conclusions and identifying his understandings and provides him with customized feedback". This program saves, also, a History of Student Performance that shows the learner's progress".
(2-1) ITS Components
Many studies have agreed on the fact that ITS consists of four main components as it is displayed in figure (1):
The components of ITS are characterized by being separated from each other allowing the possibility of applying any modifications on one of the components in a good way without applying any modifications on the other components. On the contrary, the components of CAI programs are related to each other in only one structure which makes it difficult to apply any required modification. Despite the separation of the components, they are interacted with each other as it is shown [8] in figure (1) that the ITS functions in an integrated way
(2-1-1) Domain Model:
This model could be termed also: Expert Model, Expertise Model, Domain Expert, Domain Knowledge Base, Knowledge Model, Subject Model, Target Model, Idealized Model [9] and Correction Model [10]. This model is considered the knowledge base of the Expert System which means the experts' knowledge of a specific area [11]. In this model, learners are in need of it and it enables the ITS to evaluate the learner's actions and choices within the program and identifying whether they are right or not in addition to the possibility of diagnosing the learners' mistakes[12]. This Domain Model presents methods and strategies in order to
related problems [13]. This model can follow one of the following strategies[14]:
Black Box Expert Model: results are hidden from students and the other components.
Glass Box Expert Model: results are visible to students aiming to making the expert's way of thinking is clear for students. (2-1-2) Tutorial Model:
This model could be termed also: Instruction Model, Pedagogic Model, Tutoring Strategy Model, Teacher Model, Tutorial Knowledge Model [15] and Instructor Model [16]. This model is responsible for applying the teaching strategies within ITS [17] or it is the model which enables ITS to teach through encoding the teaching strategies used by the program. This model provides the suitable teaching styles for the learning objectives depending on the weaknesses and strengths of learners' experience[18]. Consequently, this model depends on the knowledge that can be got of the learner's form and compare it with the expert's knowledge in order to make the right decision with regard to the following learning step.
User
Student Model
User Interface Tutoring Model
Domain Knowledge Model
(2-1-3) Student Model:
This model could be termed also: Learner Model [19] and Student Diagnosis Model [20]. This model represents the data structure which shows the current level of learners' understanding of the learning area [21] which reflects Student's Current Knowledge State. That leads to making smarter teaching decisions; these decisions and actions allow a sequence of the curriculum, solving the interactive problems supportively, a smart analysis of the learners' solutions, an ability to understand the special teaching strategy for every learner [22]. Student model consists of two subcomponents: Collective Student Model, Unique Student model (Model [23]. Wei has mentioned another term: Student Profile in which all the learners' data can be saved and also their performance [24]. Moreover, this profile, as evidence, shows the students' learning needs. Usually, modeling techniques use some mechanism to compare the expert knowledge with the learner's knowledge. We find some student models use Bug Libraries (e.g. Misconception) and the other could use the so-called Overly Model (e.g. Missing Conceptions [25].
(2-1-4) Model User Interface:
Model User Interface is responsible for connecting the user with the system [26]; or it is a graphic design that enables the learners to interact with the learning content and receive the program's feedback [27]. The interface is designed on the basis of meeting the learners' needs, through which questions can be asked and information can be saved. Guidance,warning or correction signals are available. So, the Interface should be simple and easy to be accessible in a very short time and effortlessly- it should be User-friendly [28]. (2-2) ITS Action Mechanism:
That knowledge of components ITS alone is not enough to know how it works, although the accuracy of the description of these components and the functions performed by each component, and thus can complement what already recognize the mechanism of action of the program by displaying some of the tracks planning, which describes the track program in some of the literature
Figure (2) Track-based Tutoring ITS [29] Learning objective (Base programming) Example Exercise Learner Response Course معن No New learning step Or a new learning objective Finish Objective Exit Achievement test Wrong Expected Response Smart Learning Diagnostic Questions Good Remedial Learning Knowledge base Correct Response مقر 1 2 3 Wrong Expected Response Reinfor cement & Guiding Response Analyze Student profile Brief Additional Information
Figure (3) Mechanism of ITS Based on Hints [30]
Figure (4) Mechanism of ITS based on Tutorial Dialog [31]
Is it correct response?
Students respond to every
dialogue
The program displays
dialog
The student chooses the next
step
Finish
Give Positive Feedback
Yes
No
Respon
se
Yes
Yes
No
No
Start
Program Displays the
Problem
Give Negative Feedback
Is there a dialogue teaching of this
error?
The student chooses the next step or asks for
help
(2-3) ITS characteristics: ITS is characterized by:
1. It is an educational system in a form of Instructional Software that is based on a teaching strategy through which learning is presented individually.
2. Learners go through their learning throughout sub-tracks which they choose depending on their response.
3. It is capable of imitating human teachers in their behaviors and decisions in different teaching situations and their reactions with learners.
4. It is capable of diagnosing mistakes and being customized according to the learners' knowledge and skills which is called pedagogically "Remedial Teaching" [32].
5. It saves a history of the student performance (Student Record) which shows the students' progress.
6. It contains the items of multimedia, on the basis of the learners' learning.
These characteristics are valuable as they elevate learners to reach Mastery Learning which is considered one of the main objectives of learning. By looking into the related literature to Mastery Learning, I found that there are many similarities between the ITS learning principles and the mastery learning principles.
(3) The concept of Mastery Learning
Adepoju, considers Mastery Learning a kind of innovation which is designed in order to help learners perform well. Through Mastery Learning, the performance level (standard) is pre-determined upon which learners complete their learning. Mastery Learning adopts frequent evaluation in order to provide learners with Corrective Instruction, Cues, Feedback and Reinforcement [33].
El-Far [34] sees that Mastery Learning means: learners reach a level of academic achievement
which is pre-determined as a condition for success in studying the curriculum. This level of achievement is usually high to the extent that reaches the subject mastery; Standard 90/ 90/90 is usually used that means 90% of learners achieve 90% of learning objectives through 90% assessments. He also sees that in order to make mastery learning reach its needed goals, it depends through some of its phases upon individual learning to achieve the individual needs and reach the needed level of mastery. Mastery learning depends on the fact that learners have disparate abilities in learning; however they can achieve the learning objectives by using an individual program that is designed on the basis of learners' abilities i.e. the learning duration differs from a learner to another according the rate of progress. In other words, mastery learning means: to allow enough time for learners to achieve the learning objectives efficiently [35]. This is based on the "Karol's" assumption which is accepted by "Bloom": "Most learners can master the main skills if they allowed enough time to learn"[36].
(3-1) Mastery Learning Variables
Some studies have discussed Mastery Learning Variables under the title: Mastery Learning Model on the basis that Mastery Learning Variables and Mastery Learning Model refer to Bloom's theory on mastery learning which is based on Cognitive Introduction Behaviors (the pre-learning which is supposed to be necessary for studying the unit) which contain Student's Characteristics and Emotional Introduction Features (motivation level to learn the unit) the quality of the learning activities which are considered a main indication of learning outputs. Variables (Feedback and Correction, Student's Participation, Reinforcement, Clue), which Bloom described as the quality of learning activity, they (i.e. variables) explain the activities prepared by teachers in order to achieve mastery learning. According to this theory, if the learner's input characteristics are related to the learning activities, the learning outputs reach a high level. Concerning these outputs, the differences between learners reach a lower level [37], [38].
Figure (5) Modification Mastery Learning Variables Based on Bloom's Theory
(3-2) The principles upon which Mastery Learning is based
Mastery learning is based on a number of basic principles [39] which go in accord with the pedagogical principles upon which ITS is based, such as:
Time is considered a main factor in learning. Teachers should allow enough time for every learner because of their disparate levels of achievement.
The quality of teaching plays an important role in learning; that if the quality of teaching was poor, learners would need more time to learn.
Motivation and learners' capabilities of understanding are considered learning basics by which tasks are achieved. The learners' pace of learning differs
according to their capabilities and characteristics.
Immediate feedback helps learners to correct their mistakes.
Dividing the topic or task into specific procedures helps learners to understand well and achieve the learning objectives. In order to achieve the objectives of
assessment, it should be continuous upon which mastery learning uses diagnostic, formative and summative.
(3-3) Components of Mastery learning
According to Karol, Mastery learning consists of five components:
Opportunity: all time allowed for learning. Perseverance: the amount of time in which
learners are ready to learn.
Aptitude: the amount of time which learners are in need of in order to reach the mastery level in an ideal learning environment.
Ability to understand the learning: the ability of learners to understand the nature of the subject and the approach they follow to learn it.
The quality of instruction: the high extent of explanation and organizing the learning components. The researcher sees that this element shows the meaning of the quality of teaching as an approach to learning (figure: 5).
(3-4) Applying Mastery learning
Applying mastery learning passes through a number of stages which are termed in different ways through references. Every stage consists of a set of steps or procedures which differ in their number and arrangement from a reference to the other. The following form displays those stages and their sub-steps, regardless of their different terms [40], [41]. Intended Learning Outputs Level of achievement The type of achievement Learning rate
The emotional results Student Learning Learning Inputs Le ar ni ng In pu ts Learning Quality (Activities that are set up to
achieve mastery learning )
Emotional characteristics Entrance
Level of learning motivation Cognitive behavior Entrance
Pre –Requirements for learning Characteristics of learners
Figure (6) Proposed Model for the stages of applying mastery learning
(4) Research problems:
By looking into some studies in ITS and mastery learning, many studies refer to the efficiency of ITS in academic achievement and skills development. We find (Hong, 1997) study has succeeded in presenting ITS for teaching and assessing prolog programming skills [42]. Also, (Emurian, 2003)
study proved the efficiency of using ITS in teaching a Java curriculum for grade 11 &12 students in American high schools [43]. (Abo Atta, 2004) study proved the efficiency of ITS in providing grade 2 students "Computer Teacher Preparation" of faculty of Specific Education, Mansoura University with the related knowledge
Numerical Formulas in Visual Basic Programming [44]. (Mahmoud, 2007) study results refer to the efficiency of ITS depending on a study of private teaching for a group of grade 2 students "Computer Teacher Preparation" of faculty of Specific Education, Mansoura University about developing programming problem solving skills. The study results showed that 80% out of the total sample size passed in the dimensional cognitive test and the observation cards skills of visual basic programming [45]. (Saleh, 2008) study concluded that teachers' intervention, according to students' demand, enhance the efficiency of ITS much better than being used spontaneously without teachers' intervention. The quality of teachers' intervention, either as a response to students' demand or regularly, does not affect the efficiency of the program [46].
By analyzing these studies in order to identify the model which is followed while developing that quality of programs, it has been noticed:
Some of these studies discussed a set of educational designs which could be guided while developing ITS pedagogically and technically, that kind of ITS I suggested. The studies didn't refer to a specific model
of educational design that could be depended upon while developing ITS pedagogically and technically, but they concern about the pedagogical side generally.
Mastery learning literature needs a model of mastery learning concerns about computer programs generally and ITS specifically.
On the basis of the above information, the research problems are represented in the ITS designers' need of a model on the basis of the
strategy of Mastery Learning; that model takes into account the elements and characteristics of that kind of programs which Mastery Learning models lack. Consequently, this question would arise: what is the form of a proposed model of developing ITS based on mastery learning strategy?
(5) The aim of Research
These studies aim to reach a model of developing ITS based on mastery learning strategy. (6) The proposed model:
According to the above, the researcher concluded that in order to develop ITS, the main stages displayed in Figure (7) can be followed. The researcher suggests two complementary models:
- The first: the model of pedagogical ITS design: this model is used at the beginning by recognizing learners' characteristics, identifying the pre-requirements of learning, analyzing the syllabus: determining the objectives, exercises, assessments and remedial and additional activities – in addition to the continuous modification and development in order to reach the required level which could be digitalized.
- The second: the model of programming ITS design: this model is used directly after preparing the pedagogical design where the previous components can be digitalized in a way that accords with the nature of the content and ITS – putting the quality standards, which identified by both El-Hadi and Saleh, into consideration: Authority, Accuracy, Objectivity, Currency, Coverage, Appropriate, Content
Consistency and Modularity.
-Figure (7) the Basic Stages of the Proposed Model pedagogical ITS design
(7) The proposed model of technical ITS design: (7-1) the proposed model of programming ITS design based on mastery learning strategy
Figure (8) the proposed model of programming ITS design based on mastery learning strategy
Components within the Single-lined shapes are available in traditional computer programs, but the components within the double-lined shapes are available in ITS only.
(7-2) the proposed model of pedagogical ITS design based on mastery learning strategy:
Figure (9) the proposed model of pedagogical ITS design based on mastery learning strategy
(8) An explanation of the proposed model of programming ITS design Preparing the syllabus content to be digitalized:
digitalizing the objectives, explanation, examples and activities which have been prepared pedagogically in a way that goes in accord with the vision of the ITS designer.
1. Multimedia: ITS is supported with many multimedia by: (adopt: using old
modifications to suit ITS requirements – create: developing new multimedia can be adopted or modified by designers). 2. Preparing exercises: preparing exercises
is considered one of the important assignments that help learners to reach a good level of mastery learning; they (i.e., exercises) emphasize the learners'
were well-digitalized, there would be good educational results. These are some examples of exercises:
- Theoretical exercises: the
thinking and problem solving skills that are related to the syllabus content and the common mistakes that could face learners; either they (i.e. the mistakes) are related to their misunderstanding of theoretical information or to the target concepts or to the steps of the skills' performance within the program.
- Practical exercises: the skills
which are explained by the program. The program could ask learners to stop until they solve a practical exercise that is related to a skill of the program's ones.
- Simulation exercises: it is an
important kind of exercises to be provided along with ITS skills. This kind of exercise helps to diagnose some mistakes which learners could make while performing some skills.
3. Evaluation: preparing some kinds of assessment (pre- assessment, formative, diagnostic and post assessment) in a way that matches the nature of ITS. The diagnostic evaluation is considered one of the important elements in ITS which concerns diagnosing mistakes and correcting them in order to make learners reach mastery learning.
4. Feedback: these are forms of feedback:
- Illustrative information when
learners make mistakes or
additional information for
learners who learn quickly.
- Hints for helping learners to
overcome mistakes.
- The correction of learners
mistakes after diagnosing them. This correction could be by
using some strategies that
explain for learners the causes of such mistakes or explain some misconceptions in the syllabus.
- Knowledge base design
5. Knowledge base design: ITS is distinguished with a knowledge base which contains: (the content: objectives, explanation, examples and activities- all kinds of evaluation- learners' records of their performance and learning). This data is saved in a table format: content table, evaluation table, learners' table…etc. Moreover, the data base could contain more tables according to the nature of the used ITS and the way it is programmed. 6. Adjusting interactions:
- The components of the program are interacted with each other easily and consistently in a way that make ITS achieve its target. The relation between the tables of the database proves the interaction of the ITS components.
- Learners' interaction with the components of the program through designing an interactive interface that would be "User-friendly [47].
(9) Conclusion:
The researcher proposes using this model to
produce an ITS program that depends on
mastery learning strategy.
References
1- Salem, Abdel-Badie Mohammed. (2003). "Systemic Approach & Informatics" . Proceedings of the Third Scientific Conference on "systemic approach in teaching and learning. April. (Available):
http://www.emoe.org/ftp/training/confrences5-4-2003/day2/abdelbadi3.doc (26/11/2003), p 43
2- Abu Atta, Mohammed Kazem Khalil. (2004). "The effectiveness of teaching programs based on artificial intelligence to develop the skills to use computers among students in educational technology" Unpublished Master's Thesis. Faculty of Education, University of Helwan, p 43.
3- Abdul Hameed, Sami. (2007). "Effectiveness of Intelligent Educational Program in the Development of Problem Solving Skills with Hearing People”, Ph.D. thesis. Institute of Educational Studies, University of Cairo, p 17.
4- Mahmoud Shawky Mohamed. (2007). "Effectiveness of Tutoring Computer in the Development of Problem-Solving Skills Programmer among Students in the Faculty of Specific Education" unpublished Ph.D. thesis. Institute of Educational Studies and Research, Cairo University, Pp. 36- 38.
5- Shehata, Gamal Abdel Nasser Mahmoud. (2005). Effectiveness of some artificial intelligence strategies in the production of educational computer programs on the development of innovative thinking "Unpublished Master's Thesis. Faculty of Specific Education, Ain Shams University, p. 155.
6- Saleh, Saleh Ahmed Shaker. (2008). "Teacher Intervention Impact on the Effectiveness of Smart Computer Programs for Pupils with Learning Difficulties in Mathematics," Proceedings of the Eleventh Annual Scientific Egyptian Society for Educational Technology: E-learning technology and the challenges of educational development in the Arab world. The Egyptian Association for Educational Technology, Vol. XVIII, p. 470.
7- Moritz, S. H. (2008). “Generating and Evaluating Object-Oriented Designs in an Intelligent Tutoring System”. Ph.D. dissertation, Lehigh University, United States -- Pennsylvania. Retrieved January 30, 2010, from Dissertations & Theses: Full Text.(Publication No. AAT 3316897), Pp. 3-4. 8- Jeon, H. J. (2007).Development of Web-based
intelligent tutoring system for SD View”. M.S. dissertation, South Dakota State University, United States -- South Dakota. Retrieved January 30, 2010, from Dissertations & Theses: Full Text.(Publication No. AAT 1446740) , p.33.
9- Abo Atta, Mohammed Kazem Khalil. (2004). op cit., p.69
10- Kenny, C. & Pahl, C. (2005). Automated Tutoring for a Database Skills Training Environment. 37(1), ACM SIGCSE Symposium: New York, Retrieved September 12, 2009, P.p. 58-62.
(Available at):
http://www.computing.dcu.ie/~cpahl/papers/sig
cse05.pdf. (Access in :12\10\2011 ).
11- Sykes, E. R. (2005). Design, development and assessment of the Java Intelligent Tutoring System. Ph.D. dissertation, Brock University (Canada), Canada. Retrieved February 1, 2010, from Dissertations & Theses: Full Text. (Publication No. AAT NR18018), p.18. 12- Ong, J. &Ramachandram, S. (2003).Intelligent
Associates Inc. Retrieved September 12, 2009,
(Available at):
http://www.stottlerhenke.com/papers/ITS_usin g_AI_to_improve_training_performance_and_
ROI.pdf. . (Access in :24\10\2011 )
13- Abo Atta, Mohammed Kazem Khalil. (2004). Op cit., p.69
14- Moritz, S. H. (2008). Op cit., P.p. 7-8.
15- Abo Atta, Mohammed Kazem Khalil. (2004). Op cit., p. 71)
16- Ong, J. &Ramachandram, S. (2003). Op cit., p.5
17- Moritz, S. H. (2008). Op cit., p.5.
18- Ong, J. &Ramachandram, S. (2003). Op cit., p.5
19- Amaral, L. A. (2007).“Designing intelligent language tutoring systems for integration into foreign language instruction”. Ph.D. dissertation, The Ohio State University, United States -- Ohio. Retrieved January 30, 2010, from Dissertations & Theses: Full Text.(Publication No. AAT 3262074), p.19. 20- Brown, Q. (2009). Mobile Intelligent Tutoring
System: Moving Intelligent Tutoring Systems Off The Desktop. Ph.D. dissertation. Faculty of Drexel University, p.23.
21-
Amaral, L. A. (2007). Op cit., p. 1.22- Wei, F. (2007). “A Student Model for an intelligent tutoring system helping novices learns object-oriented design”. Ph.D. dissertation, Lehigh University, United States -- Pennsylvania. Retrieved January 30, 2010, from Dissertations & Theses: Full Text.(Publication No. AAT 3285737), p. 17.
23-
Sykes, E. R. (2005). Op cit., p. 1.24-
Wei, F. (2007). Op cit., p. 17.25-
Amaral, L. A. (2007). Op cit., p. 19.26-
Jeon, H. J. (2007). Op cit., p. 33.27- Hategekimana, C. P. )2008). “Cognition and technology: Effectiveness of intelligent tutoring systems for software training”. Ph.D.
dissertation, Iowa State University, United States -- Iowa. Retrieved January 30, 2010, from Dissertations & Theses: Full Text.(Publication No. AAT 3342253), p.22. 28- Azmy, Nabil Gad. (2008). E-learning
technology. 1st ed. Cairo: Arab Thought House. P. 456.
29-
Mahmoud Shawky Mohamed. (2007). Op cit., p. 110.30- Razzaq, L. M. & Heffernan, N. T. (2004)."Tutorial Dialog in an Equation Solving Intelligent Tutoring System". Workshop on “Dialog-based Intelligent Tutoring Systems: state of the art and new research directions” at
31-
Razzaq, L. M. & Heffernan, N. T. (2004). Op cit., p.4.32- Ibrahim, Magdy Aziz. (2005). Creative Teaching and Learning to Think. 1st ed. Cairo: The World of Books, (series of thinking and teaching and learning; 3), p. 137.
33- Adepoju, A. A. (2002). Mastery Learning Strategy and Secondary School Teaching. In
Ayodele S.O (ed) Teaching Strategies for Nigerian Secondary School. Ibadan: Power house & Publication. In Adeyemi B.A (2007): Learning social studies through mastery approach, Educational Research and Review
vol. 2 (4), pp. 060- 063, April, ISSN 1990- 3839 © 2007 Academic Journals. Retrieved Jan 18, 2011, P.p. 60-63. (Available at): http://wikieducator.org/images/7/7d/Learning_ social_studies_through_mastery_approach.pdf. (access in 4\11\2012).
34- Elfar, Ibrahim Abdul Wakil. (2007). Methods of teaching computer. 1st ed., Tanta: Isra Library House. P. 40.
35-
Elite group of specialists. (2009). General Teaching methods and training. Cairo: United Arab Company for Marketing & Supplies in collaboration with Al-Quds Open University, P.p. 158-159.36- Elfar, Ibrahim Abdul Wakil. (2007). p.39. 37- Abdul Ghani, N., Hamim, N., Ishak, N. I.
(2006). Applying Mastery Learning Model in Developing E-Tuition Science For Primary School Students. Malaysian Online Journal of International Technology (MOJIT), August, Vol. 3, No.2, pp. 43-49, ISSN 1823: 1144, Retrieved Jan 17, 2011, (Available at):
http://pppjj.usm.my/mojit/articles/pdf/0806/04-031006-Norjihan-UM-final.pdf, p.46, (Access
in): 11\1\2012.
38- Özden, M. (2008). "Improving Science and Technology Education Achievement Using Mastery Learning model". World Applied Sciences Journal 5 (1), © IDOSI publications. p.63
39-
Elite group of specialists. (2009). Op cit., P.p. 159-163.40-
Elfar, Ibrahim Abdul Wakil. (2007). P.p. 48-54.41-
Elite group of specialists. (2009). Op cit., P.p. 159-162.42- Hong, J. (1997): "An Intelligent Tutoring and Assessing System for PROLOG Programming", inBoulay, B. du and Mizoguchi, R. (Ed.), Artificial Intelligence in Education: Knowledge and Media in Learning Systems; Proceeding of AI-ed 97 World Conference on Artificial Intelligence in
Education, Kobe, Japan: Amesterdam, IOS Press, P.p586- 588.
43- Emurian, H. H. (2003): A Programmed instruction tutoring system for JavaTM : consideration of learning performance and software self-efficacy, Information Systems Department, USA.
44- Abo Atta, Mohammed Kazem Khalil. (2004). Op cit.
