Abstract. Intelligent Tutoring System Builder (ITSB) is an authoring tool designed and developed to aid teachers in constructing intelligent tutoring systems in a multidisciplinary fields. The teacher is needed to create a set of pedagogical fundamentals, which, in line, are inured to automatically build up a broad tutor framework and construct an intelligent tutoring system. In this paper an explanation of the theory and the architecture of the tool is outlined. A presentation of several system components, the requirements of the different components, integration of these components in ITSB tool are shown. Furthermore, implanting of requirements, cognitive principle, and common design fundamentals in the tool to ease the use of teachers. A variety of design matters, an example of building an intelligent tutoring system for teaching Java language using ITSB tool and an evaluation are presented.

Philosophy

Available online  Journal of Scientific and Engineering Research 63 Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR ITSB: An Intelligent Tutoring System Authoring Tool Samy S Abu Naser Professor of Artificial Intelligence, Faculty of Engineering & Information Technology, Al-Azhar University, Gaza, Palestine Abstract Intelligent Tutoring System Builder (ITSB) is an authoring tool designed and developed to aid teachers in constructing intelligent tutoring systems in multidisciplinary fields. The teacher is needed to create a set of pedagogical fundamentals, which, in line, are inured to automatically build up a broad tutor framework and construct an intelligent tutoring system. In this paper an explanation of the theory and the architecture of the tool are outlined. A presentation of several system components, the requirements of the different components, integration of these components in ITSB tool are shown. Furthermore, implanting of requirements, cognitive principle, and common design fundamentals in the tool to ease the use of teachers. A variety of design matters, an example of building an intelligent tutoring system for teaching Java language using ITSB tool and an evaluation are presented. Keywords ITSB, Intelligent Tutoring Systems, Java Language Introduction Intelligent Tutoring System (ITS) is a software that aims to provide immediate and customized instruction or feedback to learners [1-14], typically without interference from a human teacher. ITSs have the general aim to facilitate learning in a evocative and efficient way by using a diversity of computing technologies. There are a lot of examples of ITSs being used in both official education and professional situations in which they have confirmed their abilities and boundaries. There is a strong association between intelligent tutoring, cognitive learning theories and design. ITS intends to solve the problem of over-dependency of students over teachers for superiority education. It intends to offer access to high-class education to every student, consequently improving the whole educational system. The aim of ITS authoring tool is to simplify the development of construction ITSs and reduce the ability threshold for constructing them. In addition, they permit the quick prototype of ITS design. Accomplishing these objectives will assist ITS developers and users who do not have computational thoughts construct ITSs. Computational thoughts concerned with creating problems and their solutions so that the solutions are formulated can efficiently carried out by an information processing agent. Literature Review The Cognitive Tutor Authoring Tool allows a teacher to include learning by doing to online lessons [15]. It facilitates the creation of two categories of tutors: example-tracing tutors that can be produced with no programming however it needs problem-specific authoring, and cognitive tutors, that need Artificial Intelligence programming to create a cognitive model of student problem solving however, provide tutoring across a variety of problems [15]. The Extensible Problem Specific Tutor [16] is web authoring tool that assists quickly developing exampletracing tutors on existing interfaces that reduces tutor development time, and permits the interface to be Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 64 separable from the tutoring component. The Presentation Manager gives visual feedback to the student on the interface. RIDES is an authoring tool for creating and delivering graphical simulations and simulation-based training. With RIDES, the author can convert a set of simulations into interactive, graphical tutorials. Actually, the reason why RIDES might never be used for reproducing Ms. Lindquist is due to its limitation to construct tutors based on simulations only [17]. ASPIRE an authoring tool for assisting with the creation and delivery of constraint-based tutoring systems. It produces constraints that structure the domain model with the help of the domain expert, reducing the programming expertise necessary for developing a new constraint-based tutor. The system also offers all the domain-independent functionality of constraint-based ITSs [18]. ITSB Authoring Tool Overview ITSB authoring tool is a shell for crating intelligent tutoring systems. It is designed and developed using Delphi Embarcadero XE8, 2015; ITSB authoring tool is two systems in one application. The first one is the teacher system where he/she add the course materials, questions and answers etc. and the second system is the students where he/she learn the course material and practice exercises. The following sections describe the system architecture and next an illustration of the different modules, their purpose, functions, and goals etc are outlined. ITSB System Architecture A normal ITS has four fundamental modules: domain model, teaching model, student model and user interfaces. The domain model adds the course configuration in a structured style. A course may have a variety of parts, such as division, sub-divisions, and topics. These parts are stored in the domain model together with their dependencies. All the materials and resources necessary to tutor a student are also kept in this module. The student model is the demonstration of the students the system is coping with. The student model provides the system with all required information so it can adapt itself with the student. Therefore, student model is a vital tool for the adaptation process. The teaching module contains all the decision-making procedure concerning course preparation and adaptation. Often, this module is called the control engine, because this module controls the entire system, by accepting inputs from the other parts. Lastly, the user interfaces have two sections one for the student and the other for the teacher. Teacher's interface is accustomed to arrange and adjust the system and its different parts. So, the teacher's interface behaves as the authoring tool. By his interface, the teacher can add new lessons, adjust the established ones, and revise teaching methods. The student's interface is used to convey all the teaching commands. The sort and the type of these commands would differ with student's ability and performance level. A general system architecture is shown in Fig. 1. Figure 1: Overall System Architecture of ITSB Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 65 ITSB Domain Model The domain model is concerned with the lessons, its arrangement and a range of elements. There are two fundamental components in domain model. The first component, Domain Organization Model, deals with the arrangement and organization of the lessons and its topics. The second one, Repository, deals with the materials being taught themselves. ITSB Student Model State based approach was implemented in the student model. However, there are quite a few parameters for educational modeling of a student throughout a learning procedure. Two parameters were taken into account in this paper:  Coverage: the topics covered by a student and  Performance of a student: (measured through his ability to comprehend and his problem solving skills. ITSB Teaching Module Teaching module is considered to be the most important component of an ITS. The primary task of this module is to arrange a sequence of teaching actions to be taken during a teaching process. These actions and their sequence should go with the student's ability, requirement and objectives. The arrangement is done at two stages. At the first stage, ordering of the topics for the student needs to be arranged. This stage begins from the initial state and finishes when all the topics are included in the sequence. At the second stage, after a topic is chosen another arrangement is essential to compute the exact technique of teaching that topic. This engages selecting the proper type of the document and the proper medium. ITSB User Interfaces Interfaces are an essential part of the ITSB system. There are two classes of users, teachers and the students. The ITSB authoring tool has both interfaces. Each class of users see different interface for their interactions with the system. The teachers interface is the shell of ITSB for configuration and adjustment of the system. The teacher's interface or the authoring interface consists of three parts, used to configure the different parts of the system, one to configure the Student Model, one for authoring the Domain Organization Model and the third for maintaining the Repository (see Fig. 1). Through these interfaces a teacher can configure various aspects of the system, like initial information about the student , enter students lessons, questions and answers, configure and adjust the color, font name and size of all menus, buttons, comb boxes etc. Thus, this interface provides the system with the required flexibility and robustness. Moreover, due to this interface the system can become domain independent. A screenshot of the teacher's interface is shown in Fig 2, Fig 3, Fig 4 and Fig 5. Figure 2: Form for adding Lessons and Examples Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 66 Figure 3: Form for adding initial students information Figure 4: Form for adjusting Fonts of all screens of the system Figure 5: Form for adding constants of the system Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 67 Figure 6: Form for adding questions and answers Student interface is the front-end for the student to interact with the system. The interface has a bidirectional communication mechanism (see Fig. 1). The system presents all the learning documents and test materials to the student through this interface. Performance of the student in the tests is conveyed back to the system, specifically to the student model by it. This feedback is vital because the adaptation process would depend on this. So, the success of adaptive planning depends on it and its communication with teaching module (See Fig 7, Fig 8, Fig 9). Figure 7: Student lessons and examples form Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 68 Figure 8: Student Exercises form Apart from these, the student interface also provides various courses and performance related information to the student, such as the performance history of the student, how much of the course has been covered, detailed test results etc. These help a student to identify and rectify his status (See Fig 9). Figure 9: Student statistics form Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 69 Multiple Language Support ITSB authoring tool currently support two languages. The default language is English language. If the user prefer Arabic Language, He/she can just click on one button in first Login form. Once the user clicks the Arabic button, it translate all buttons, menus, titles, and subtitles; furthermore it switch the direction of the forms from Left-to-Right into Right-to-Left (See Fig 10, Fig 11). Figure 10: Logging Form in English Language Figure 11: Logging Form in Arabic language ITSB Evaluation Two evaluations were carried out to test the ITSB intelligent tutoring system authoring tool. The first evaluation was to let a group of teachers each prepare the materials: lessons, examples, exercises, answers, student information, and system constants etc. for introductory java programming. Then each teacher was asked to enter the prepared materials using a copy of the ITSB tool. The next step was to gather the opinion of each teacher in team of how easy, efficient, and friendly was the ITSB tool. The second evaluation was to let a group of students use one of the resultant intelligent tutoring system to see how easy, efficient, and friendly. Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 70 The result of the first evaluation (teachers evaluation) and second evaluation (students evaluation) are promising as shown in Table 1. Table 1: The results of both evaluations Category Teachers Evaluation Results Students Evaluation Results The system is easy to use 88% 92% The System is efficient 85% 90% The System is friendly 82% 93% Conclusion In this paper we have described the theory and architecture of the ITSB tool. ITSB tool is an authoring tool designed and developed to aid teachers in building intelligent tutoring systems in multidisciplinary fields. Several system components, requirements of the different components, and integration of these components in ITSB tool were outlined. Furthermore, implantation of requirements, cognitive principle, and common design fundamentals in the tool to ease the use of teachers. A variety of design matters, and an example of building an intelligent tutoring system for teaching Java language using ITSB tool was outlined. Two evaluations of the teachers and students perspective were presented and the results were promising. References [1]. Naser, S.S.A., (2008). Developing an intelligent tutoring system for students learning to program in C++. Information technology journal, 7(7), pp.1055-1060. [2]. Naser, S.S.A., (2008). Developing visualization tool for teaching AI searching algorithms. Information Technology Journal, 7(2), pp.350-355. [3]. Naser, S.S.A., (2012). Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system. International Journal of Artificial Intelligence & Applications, 3(2), p.65. [4]. Naser, S.S.A., (2006). Intelligent tutoring system for teaching database to sophomore students in Gaza and its effect on their performance. Information Technology Journal, 5(5), pp.916-922. [5]. Naser, S., (2009). Evaluating the effectiveness of the CPP-Tutor an intelligent tutoring system for students learning to program in C ++ . Journal of Applied Sciences Research, 5(1), pp.109-114. [6]. Naser, S.A., (2008). An agent based intelligent tutoring system for parameter passing in java programming. Journal of Theoretical & Applied Information Technology, 4(7). [7]. Abu-Naser, S., Ahmed, A., Al-Masri, N., Deeb, A., Moshtaha, E. and AbuLamdy, M., (2011). An Intelligent Tutoring System for Learning Java Objects. International Journal of Artificial Intelligence and Applications (IJAIA), 2(2). [8]. Abu-Naser, S., Al-Masri, A., Sultan, Y.A. and Zaqout, I., (2011). A prototype decision support system for optimizing the effectiveness of elearning in educational institutions. International Journal of Data Mining & Knowledge Management Process, 1(4). [9]. Naser, S.S.A., (2008). JEE-Tutor: An Intelligent Tutoring System For Java Expressions Evaluation. Information Technology Journal, 7(3), pp.528-532. [10]. Naser, S.S.A., (2012). A Qualitative Study of LP-ITS: Linear Programming Intelligent Tutoring System. International Journal of Computer Science & Information Technology, 4(1), p.209. [11]. Naser, S.A., Ahmed, A., Al-Masri, N. and Sultan, Y.A., (2011). Human Computer Interaction Design of the LP-ITS: Linear Programming Intelligent Tutoring Systems. International Journal of Artificial Intelligence & Applications (IJAIA), 2(3,), pp. 60-70. [12]. Abu Naser, S.S., (2001). A comparative study between animated intelligent tutoring systems AITS and video-based intelligent tutoring systems VITS. Al-Aqsa Univ. J, 5, pp.72-96. [13]. Abu Naser, S.S. and Sulisel, O., (2000). The effect of using computer aided instruction on performance of 10th grade biology in Gaza. J. Coll. Edu, 4, pp.9-37. Naser SSA Journal of Scientific and Engineering Research, 2016, 3(5):63-71 Journal of Scientific and Engineering Research 71 [14]. Abu Naser, S., (2008). JEE-Tutor: An Intelligent Tutoring System for Java Expression Evaluation. Information Technology, 7(3), pp.528-532. [15]. Koedinger, K. R., Aleven, V. A., & Heffernan, N. (2003). Toward a rapid development environment for cognitive tutors. Artificial intelligence in education: Shaping the Future of Learning Through Intelligent Technologies, 97, 455. [16]. Gilbert, S., Blessing, S. B., & Kodavali, S. (2009). The Extensible Problem-Specific Tutor (xPST): Evaluation of an API for tutoring on existing interfaces. Paper presented at the Artificial Intelligence in Education. [17]. Allen Munro, Mark C. Johnson, Quentin A. Pizzini, David S. Surmon, Douglas M. Towne, James L. Wogulis (1997). Authoring Simulation-Centered Tutors with RIDES. International Journal of Artificial Intelligence in Education, 8, 284-316. [18]. Mitrovic, A., Suraweera, P., Martin, B., Zakharov, K., Milik, N., & Holland, J. (2006). Authoring constraint-based tutors in ASPIRE. Paper presented at the 8th International Conference on Intelligent Tutoring Systems.


This paper aims to determine knowledge management (KM) factors which have strong impact on high performance. Also, the study aims to compare KMM between intermediate colleges. This study was applied on three intermediate colleges in Gaza strip, Palestine. Asian productivity organization model was applied to measure KMM. Second dimension which assess high performance was developed by the authors. The controlled sample was 190. Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach’s alpha, “ANOVA”, simple linear regression and step wise regression and LSD test. The overall findings of the current study show that maturity level is in the second level. Findings also support the main hypothesis and its sub- hypotheses. The most important factors effecting high performance are the processes, KM leadership, people and KM outcomes. In addition, there are differences in high performance for college (PTC). Furthermore, the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first comparative study between intermediate colleges in Palestine that explores the status of KMM using the Asian productivity model. Research limitations was that the survey findings were based on intermediate colleges in Gaza Strip.

Philosophy

KM Factors Affecting High Performance in Intermediate Colleges and its Impact on High Performance - Comparative Study

Computational Research Progress in Applied Science & Engineering ©PEARL publication, 2016 CRPASE Vol. 02(04), 158-167, October 2016 157 ISSN 2423-4591 KM Factors Affecting High Performance in Intermediate Colleges and its Impact on High Performance Comparative Study Samy S. Abu Naser, Mazen J. Al Shobaki, Youssef M. Abu Amuna Faculty of Engineering & Information Technology, Al-Azhar University, Gaza, Palestine Keywords Abstract Knowledge Management, KM Maturity, High performance, Intermediate Colleges, Asian Productivity Model This paper aims to determine knowledge management (KM) factors which have strong impact on high performance. Also, the study aims to compare KMM between intermediate colleges. This study was applied on three intermediate colleges in Gaza strip, Palestine. Asian productivity organization model was applied to measure KMM. Second dimension which assess high performance was developed by the authors. The controlled sample was 190. Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach's alpha, "ANOVA", simple linear regression and step wise regression and LSD test. The overall findings of the current study show that maturity level is in the second level. Findings also support the main hypothesis and its subhypotheses . The most important factors effecting high performance are the processes, KM leadership, people and KM outcomes. In addition, there are differences in high performance for college (PTC). Furthermore, the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first comparative study between intermediate colleges in Palestine that explores the status of KMM using the Asian productivity model. Research limitations was that the survey findings were based on intermediate colleges in Gaza Strip. 1. Introduction Knowledge management in intermediate colleges is the main aim of those organizations, where they produce and manage knowledge through human activities and technical practices to link individuals from various administrative levels and sections. This process establishing working groups and trust relationships which produce share and exchange of knowledge they own, support individual and collective learning processes, and then improve and develop individual and organizational performance. Measuring KMM is an important process and the purpose of the measurement should be obvious and within right criteria based on successful experiences with the capability to recognize knowledge gaps that must be remedied in order to take full advantage of the knowledge [1, 2]. The objective of this study is to measure and compare KMM in HEI. Also the study aims to define KMM level to encourage them moving to a higher level.  Corresponding Author: E-mail address:  - Tel, (+970) 599783837 - Fax, (+970) 82641888 Received: 01 August 2016; Accepted: 05 October 2016 In view of the literature review, the study raises the question of: Q1. What are the most influential factors on intermediate colleges' performance resulting from KMM? Q2. How to link KMM with performance and benefit from it for future performance improvements? Q3. Are there any differences in high performance related to intermediate college? As for originality, the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is explore the differences in high performance at intermediate colleges using KMM. 2. Literature Review 2.1. knowledge Concept During the past two decades, knowledge and information are considered as the most important resources of creating value and the most important factors for creating competitive advantage, as a result, they have been changed into necessary activities [3, 4]. Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 159 Knowledge in an organization can be broadly classified into two categories: explicit and tacit knowledge. Explicit knowledge is that which can be measured, captured, examined, and can easily be passed onto others in a codified format-a formal and systematic language. Tacit knowledge, on the other hand, is highly personal, context-specific and comes from one's experience. It is hard to measure, capture or examine. Since knowledge is valuable it should be managed and utilized wisely [1, 5]. Knowledge has been recognized as a valuable organizational resource and a foundation for competitive advantage in today's business environment. Its value is magnified by it being closely related to another important organizational resource in today's dynamic global markets - time [4, 6, 7]. Today's organizations are viewed as wellsprings of knowledge and thus cannot afford to lose time or looking for old knowledge they are unable to retrieve by trying to know what they know [6, 8]. 2.2. Knowledge Management Importance The rapid growth of data and technologies trigger the transformation of data to useful information, known as knowledge. Nowadays, people are aware of the worth of knowledge and the styles to obtain, recognize, capture, save and leverage it, so that knowledge can be shared without losing it; in the other words know how to manage knowledge. In this way the term of KM is created [6, 9]. Knowledge Management (KM) is a process where organizations have formulated ways in the attempt to recognize and archive knowledge assets within the organization that are derived from the employees of various departments or faculties and in some cases, even from other organizations that share the similar area of interests or specialization [9, 10]. Knowledge management, which can produce the power of compatibility and survival for organizations, is based on the attraction and productivity of knowledge and its management. But, for organizations which are to be responsible in the today's developing information world and have competitive positions, it is vital to experience the access to processes of knowledge management, practically [5, 8, 9]. Nowadays, organizations entering a knowledge community in which the main economic resources are no longer considered as capitals, natural resources, or working, but it is knowledge and knowledge-based staff which have main functions [1, 3, 8]. For many organizations improving performance is not only dependent on the successful deployment of tangible assets and natural resources but also on the effective management of knowledge [11, 12]. Technology infrastructure, organizational structure and organizational culture are linked to the organization knowledge infrastructure capability; and knowledge process (obtain, recognize, capture, share, save and leverage) are linked to the organization knowledge process capability. Taken together, these resources determine the knowledge management capability of the organization, which in turn has been linked to various measures of organizational performance [5, 10, 11, 13]. 2.3. Knowledge Management in Intermediate Colleges Intermediate colleges are cognitive intensity institutions where the primary function is based on knowledge, production of knowledge, documentation and publishing. There is a growing belief that knowledge management in educational institutions help build the future of a dynamic learning environment, development and improvement of the efficiency activities of knowledge sharing and improve the overall performance of the organization [3, 14-26]. Ramachandran et al. defined KM in HEI as the systematic attempt to develop and implement knowledge practices in universities with the support of major strategic assistance factors [6]. Also, as defined by Petrides and Nodine [27], it is a frame or a way for individuals working in the educational institution to develop a set of practices to gather information and share what they know, which resulting in behaviors or actions that will improve the level of services and products offered by the educational institution. Laal defined it as the process of converting information and intellectual assets to a continuing value that connect individuals with the knowledge they need to take action when they need it [9]. According to the previous definitions, KM in HEI is similar knowledge management in industrial organizations or services, in terms of operations and activities, with a focus on the link between individuals and management to enhance the quality of outputs and achieve a competitive advantage in performance and outputs. HEI offer their services primarily to the community, and represents members of the community the main beneficiaries of universities. Researchers identified the most reasons why HEI environment is the most suitable to adopt KM as the existence of technology infrastructure, confidence and knowledge sharing is normal in universities and students enroll in a college to access to knowledge [14, 17, 28-32]. Critical factors for KM in intermediate colleges: The most critical variables that has an effect on sharing knowledge in universities are benefits and rewards [17, 33]. Hislop suggests that the issues that concern to the staff regarding to assessment of advantages and disadvantages of sharing knowledge [34]. Benefits can be real rewards which improve the organization's performance and stability. Rahman et al. and Bock et al. pointed that social sharing benefits cannot be estimated quantitatively; instead it is a personal commitment, trust and gratitude [5, 35]. Leadership style is an additional important factor which plays an essential role in endorsement and development of knowledge exchange behavior, by contributing in experiential learning for staff, providing opportunities for supervising operations, development information technology systems, rewards and opportunities and interaction systems [2, 12, 33, 36, 37]. The role of the leader can be completely different in the educational institutions where there are two types of leadership: academic and hierarchical management leadership. Significant tensions can exist when people with administrative capacity control the academic environment [38]. Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 160 There is an immense dispute about the role of culture in educational institutions in the field of knowledge management and exchange [1, 2, 10-12, 33]. It has been indicated that the academic departments are complex and cultures may be different among departments in different disciplines [10]. The fact that remains dominant here is that the academic community has a culture of participation more than other forms of organizations and that cooperation is the essence of knowledge management [17, 39]. One more important factor is organizational structure [1, 2, 10]. This structure might be a major difficulty for the exchange of knowledge. For that reason, organizational structure should be flexibly designed to persuade participation and cross-border cooperation inside the organization. The combination between formal organizational structure and nonhierarchical structure enhance knowledge generating and sharing [2, 33, 40, 41]. High performance in intermediate colleges: Intermediate colleges are service organizations providing education and knowledge. They are also responsible for providing the society with qualified people for jobs, so they deliberately to achieve high performance in their activities by teaching process. This performance measured by many excellence models such as BSC, Malcolm Baldrige American model, European model and Canadian model [42]. Those models depend on several criteria, leadership, strategic planning, customer orientation, KM, human resource, operations Management and the outcomes. Also the scales might be financial or non-financial. Lee and Teseng pointed that financial scales connected directly with long term objectives, measuring the success of strategic plans and the ability to adapt with changes in external environment. Financial scales consist of ROI, sales growth, income before taxes, net profit, ROA, etc. [43]. While operational scales provide a hidden image for performance such as new products, product quality, market share, innovation, customer retention, social responsibility [8, 44]. Al-hady defined high performance in universities "The performance that helps in achieving strategic objectives and effectiveness according to quality scales". KM can improve this performance in high rates [27, 45]. Rani, Sania, ALHayaly and Alnajjar added that KM positively affects organizational outcomes of organization innovation, product improvement and employee improvement [46, 47]. These researchers [14, 29, 48-49] mention the main fields of high performance in universities:  Reduce costs and increase profits: Educational institutions seeking to cut costs by reducing the costs of services provided to students and the level of operational and administrative costs of operations, leading to an increase in profits.  Improve Quality: The overall quality management approach depends on the joint efforts by which the participation of all individuals on an ongoing basis to improve the institution's performance.  Scientific research: Scientific research in educational institutions is the key element of high performance which helps in the advancement of professional practice and gains the confidence of the industry, and demonstrates the intellectual contributions of the faculty member.  Community Service: It is an essential element in evaluation process of high performance that clarifies the role of the institution in civil society service and its contribution to solving its problems. 2.4. Knowledge Management Maturity KMM determines the level of organization existing capacity affecting on knowledge management processes, where every organization particular track a special sequence of maturity. Knowledge management maturity models describes the steps of growth, which is expected to be up to the organization to develop their knowledge management and organizational performance [17, 50]. Also it determines the stages of institutional knowledge maturity, which is expected to pass by any institution on its way to improve their practices and competitive advantages and thus improve the overall performance of the institution [51]. The importance of knowledge management measurement can be determined as follows [52, 53]  Helps measure the institution to identify knowledge gaps they have.  Determine the impact of knowledge gaps on the performance, growth and development of the institution.  Helps to manage knowledge possessed by the organization more efficiently.  Provides the enterprise with analytical tools works to promote knowledge and address gaps.  Identify strategies and activities to fill those gaps in knowledge. Asian productivity organization developed a model to measure KMM, designed after a study lasted for five months. This model has been adopted by the Asian Organization of Production (APO) to develop tools and knowledge management techniques. Working team consist of experts in knowledge management from Japan, Singapore, India, China, Malaysia, Thailand, Vietnam and the Philippines [54]. This model consist of a general framework for knowledge management, knowledge management tool and measuring tool to measure the maturity of knowledge management as shown in Figure 1. Figure 1. KM framework Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 161 This framework consists of a row of enablers for knowledge management, starting from the organization's mission and vision in the middle of the circle, which sets strategy and organization capabilities. Then we move on to the second frame, which includes knowledge management processes and the factors that accelerate the processes of knowledge management like leadership, individuals, processes and technology. In the last phase, the results of using knowledge management are represented by quality, productivity, profitability and growth of the organization. The model defined seven fields to measure KM: KM Leadership, process, people, technology, knowledge process, learning and innovation and KM outcomes as in Figure 2. After measuring KM, the results showed on a radar chart identifying the areas that have strength and the areas that need improvement and the organization has an opportunity to improve them. Figure 2. Radar Chart Figure 3. Maturity levels model The next step is to determine the level of maturity of knowledge management in the organization and comparing it with the maturity levels model. Knowledge management maturity consists of five levels are composed as follows (as shown in Figure 3) 1Reaction: The organization is not interested in knowledge management and focused on enhancing productivity and competitiveness. 2Initiation level: The organization begins to realize the need for knowledge management or has already begun in a pilot project for knowledge management. Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 162 3Expansion: Knowledge management is fully applied. 4Refinement: Organization evaluates knowledge management on an ongoing basis. 5Maturity level: Knowledge management exists primarily as a driver in every organization's process. 2.5. KMM and High Performance Knowledge management is not the only factor affecting performance and output of the organization, but it is one of many factors. Factors enabling high performance are leadership style, strategic planning, measurement, analysis, knowledge management, customers oriented, human resource management and administrative processes [13]. In first or second level of maturity, KM operations are local and lead to focus on a particular section in the organization without a comprehensive strategy to support those efforts. Here we can say that the impact of KM processes is not dramatically evident on the organization's performance. Therefore we can say that the first and second level of knowledge management maturity levels represent normal performance [55]. When reaching level three and four, organization begins to integrate knowledge sharing and collaboration in its main operations and set resources for knowledge management. Employees in levels three and four use technology and standardized tools to capture, transfer, share and re-use of knowledge in the organization. Finally, KMM at level five represent full integrated knowledge management operations and employees understand the role of knowledge sharing and cooperation in improving the performance for individuals and organization. Such behavior supports creative activities, leads to better competitive advantages and enhance the value chain to customers and suppliers [50, 56-57]. Figure 4. The conceptual framework The relation between the model used in this study (See Figure 4) and high performance leads to the following main hypothesis in this study: H1: There is a statistically significant effect for using the Asian knowledge model to measure intermediate colleges' high performance. As the previous model suggest, leadership plays a critical role in the success of KM implementation. If there is a strong commitment at executive management level to change the organizational culture, then the organization will be able to create the values that lead to knowledge sharing [14, 58]. To achieve that, organization needs a leadership style able to manage organization elements to achieve the best and maximum advantage of the existing knowledge in organization to improve performance. This leads to the following first sub-hypothesis in this study: h1-1: There is a statistically significant effect for KM leadership on intermediate colleges high performance. Operations are considered a complete knowledge inside organization. As value of chain reflects how far can organization add value in each production step to achieve organizational efficiency and increase performance [50, 59]. This leads to the following second sub-hypothesis in this study: h1-2: There is a statistically significant effect for operations on intermediate colleges high performance. Many KM research confirmed individual's impact on high performance. These authors [2, 14, 39, 50] explained that individual's motivations and method of interpretation, transfer and implementation of knowledge management processes influence greatly in determining the shape and nature of knowledge and how to manage it. Therefore, individual is the most powerful element of an effective knowledge management implementation. This leads to the following third sub-hypothesis in this study: h1-3: There is a statistically significant effect for people on intermediate colleges high performance. New technology plays a major role in performance improving by providing the right information at the right time and using them to rationalize decisions. Add to that technology needed to enhance sharing knowledge and learning inside organization. The integration between knowledge and organizational process enhance performance and competitive advantages [30, 50, 60]. This leads to the following forth sub-hypothesis in this study: h1-4: There is a statistically significant effect for technology on intermediate colleges high performance. Knowledge process like generation, storage, distribution and implementation facilitates work within the organization. The presence of a specialist team captures knowledge and encourages workers to invest and participate in it. With the existence of an effective leadership leading those operations to bring harmony between them reduce the total cost of work and increase financial returns for organization. This leads to achieve creativity, innovation and high productivity [3, 37, 48, 61]. This leads to the following fifth sub-hypothesis in this study: h1-5: There is a statistically significant effect for Knowledge process on intermediate colleges high performance. Hila and Sangjae discussed learning and creativity in organization. Modern organizations characterized with continuous learning and applying the gained experience in their daily routine. Organizations seeking to recruit the experience gained from learning process in continuous performance development [12, 62]. This leads to the following sixth sub-hypothesis in this study: h1-6: There is a statistically significant effect for learning and innovation on intermediate colleges high performance. KM outcomes must reflex on effectiveness and efficiency inside the organization. This leads to high performance at individual level and organizational level [2, KMM MODE L HEI EXCELLENCE PERFORMANC E MATURITY LEVEL Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 163 3, 50]. This leads to the following seventh sub-hypothesis in this study: h1-7: There is a statistically significant effect for KM outcomes on intermediate colleges high performance. Other authors and KM experts added other variables and suggested new models which are more manifold. Those models included the pervious variables and variables like intellectual capital, agility, training and cultural capital [4, 7, 63]. H2: There are statistically significant differences for high performance related to the intermediate college. 3. Research Design 3.1. Study Population and Sampling This study conducted at three intermediate colleges: Palestine Technical College (PTC), College of Applied Science (UCAS), College of Science and Technology (SCT). According to the model, the sample must be between 70%-80% from the population of the study [51]. The population are 237 employees, the control sample 190. The usable sample was 167, which makes the response rate 87%). 3.4. Research Instrument The first dimension referring to the model used in the study, is a prepared in advance questionnaire by the Asian productivity organization (KM assessment tool) as in Table 1. The second dimension of the instrument which measure high performance in intermediate colleges was developed by the current authors with the help of other research literature [14, 48-49, 61, 64-66]. Also Trustees validity has been conducted by a group of expert in KM and management field. Those statements were further revised and modified by the experts in a subsequent stage before drafting the final version of the questionnaire. A five-point Lekert scale of agreement was used for measurement, running from "Strongly Agree" to "Strongly Disagree", with a neutral category for scale midpoint. Table 1. Research instrument Dimension 1: (Ind. V.) KM Assessment Tool No. of statements Dimension 2: (D.V.) High performance in HEI No. of statements Cat 1.0: KM Leadership 6 16 Cat 2.0: Processes 6 Cat 3.0: People 6 Cat 4.0: Technology 6 Cat 5.0: Knowledge Processes 6 Cat 6.0: Learning and Innovation 6 Cat 7.0: KM Outcomes 6 3.2. Validity and Reliability Assessment The study adopted Cronbach's α to measure the internal consistence reliability of the questionnaire. The results showed that Cronbach's α value for all dimensions were greater than 0.5. It indicated that the design of the questionnaire had a high internal consistency as shown in Table 2. Table 2. Validity and reliability No. of Statements Validity (√CA 2 ) Reliability(Cronbach Alpha) Dimension 6 0.767 0.875 KM Leadership 6 0.728 0.853 Processes 6 0.795 0.891 People 6 0.766 0.875 Technology 6 0.559 0.747 Knowledge Processes 6 0.560 0.748 Learning and Innovation 6 0.676 0.822 KM Outcomes 16 0.827 0.909 High performance 58 0.818 0.894 All Dimensions 3.3. Statistical Procedures Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach's alpha, "ANOVA", simple linear regression, OLSordinary least squares and step wise regression, LSD test for differences. 4. Data Analysis and Discussion of Results Simple linear regression and "ANOVA" tests were used to test hypotheses. Simple linear regression used to test whether there is an impact for one independent variable on a single dependent variable (High performance). The results are shown in Table 3. The results of regression test indicate that sig. is less than 0.05 for all independent variables, which mean that there are significant statistically effect for independent variables on excellence performance. Pearson coefficient and regression coefficient sign for all independent variables was positive. This result means whenever the value of independent variables increase, there will be increase in performance. Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 164 Changes in the independent variable is responsible for the interpretation of a rate r2 of all the changes that occur in performance, and there is a rate 100-r2 due to other factors specific to the other independent variables and other factors not mentioned in the model , in addition to the random error. The table reveals that the findings of significance level are less than 0.05, which means that we can rely on the previous model and circulating the sample results on research community. Therefore, the results of the analysis proved the existence of a relationship between the independent variable (KM assessment tool) with its 7 sub-dimensions and the dependent variable (high performance). Each dimension in the independent variable has an effect individually on the dependent variable. According to that, we accept hypothesis H1 and it is sub-hypotheses. Table 3. Simple Linear Regression and "ANOVA" Variable Sig. Decision at α = 0.05 Regression coefficient Pearson coefficient r Sign R2 H1 KM Leadership 0.0 Significant 0.245 0.444 + 19.7%  Processes 0.0 Significant 0.253 0.453 + 20.6%  People 0.0 Significant 0.172 0.315 + 9.9%  Technology 0.001 Significant 0.188 0.192 + 3.7%  Knowledge Processes 0.017 Significant 0.112 0.143 + 2%  Learning and Innovation 0.0 Significant 0.187 0.232 + 5.4%  KM Outcomes 0.0 Significant 0.222 0.307 + 9.4%  Step wise regression conducted to arrange the effect of each variable in KMM model on the dependent variable and excluding of other insignificant variables. Table 4 shows that four variables were effecting significantly (Processes, KM leadership, People, KM Outcomes,) and three were not effecting (Learning and Innovation, Technology, Learning and Innovation). The explanation for that is the effect for the four variables were very strong on high performance more than (Knowledge Processes, Learning and Innovation, Technology) from the point of view of the sample. Table 4. Step Wise R Rank Variable T Sig. Decision at α = 0.05 1 Processes 3.5 0.001 significant 2 KM Leadership 2.88 0.004 significant 3 People 2.47 0.014 significant 4 KM Outcomes 2.24 0.025 significant 5 Knowledge Processes 1.83 0. 068 insignificant 6 Learning and Innovation 0.705 0.48 insignificant 7 Technology 0.27 0.78 insignificant According to the model, radar chart had been done by calculating the response of each paragraph in sub-domains rates as shown in Table 5. The total score was 118.7348 which mean that KMM is in level tow (initiation). Intermediate colleges begin to realize the need for knowledge management or have already begun in a pilot project for knowledge management. In that level of maturity, intermediate colleges must expand KM implementation to reach for level three. Also from Table 4 we find that dimensions 57 need to be improved. For the second hypothesis H2, LSD test conducted, and the results showed that there are differences in high performance for college (PTC) with mean diff. 0.14578 and there are differences in high performance for college (UCAS) with mean diff. 0.14578 as shown in Table 6. Table 5. Radar Chart No. SubDomain

Palestine Technical College (5-30) College of Applied Science (5-30) College of Science and Technology (5-30) Total 1 Leadership 20.08138 19.65659 20.00862 19.91553 2 Processes 18.5036 17.61538 17.67816 17.93238 3 People 18.01079 17.30769 16.89655 17.76811 4 Technology 17.57914 17.46154 16.48276 17.17448 5 Knowledge Processes 16.33453 16.24176 15.54023 16.03884 6 Learning and Innovation 15.8777 15.51648 14.90805 15.43407 7 KM Outcomes 14.80576 14.51648 14.09195 14.47139 Total (42-210) 121.19 118.3159 115.6063 118.7348 Abu Naser et al. Comput. Res. Prog. Appl. Sci. Eng. Vol. 02(04), 158-167, October 2016 165 Table 6. LSD TEST 5. Conclusions The concept of KM implementation is already known in Palestinian intermediate colleges. Many studies conducted on KM. The originality of study comes from being discussing KMM using a solid model. The overall findings of the current study suggest that KMM lead to high performance. KMM assessment shows that the intermediate colleges' maturity level is in the second level where the organization begins to realize the need for knowledge management or has already begun in a pilot project for knowledge management where knowledge sharing and collaboration is common. Findings also support the main hypothesis and it is subhypotheses. The most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes. Others factors (Technology, learning and innovation, knowledge process) effecting also on high performance but cause of other factors are stronger and there are random faults the effect was insignificant in step wise regression. Intermediate colleges need to invest more in those dimensions to enhance their effect on performance. Also we can find that KMM for intermediate colleges is at the second level, therefore, more concentrating on KM factors is needed. 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