This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge
          management on high performance. This study was applied on Al-Quds Open University 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 (306). 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.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM
          assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The
          most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and
          Innovation. 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 in the universities of Palestine explores the status of KMM using the Asian productivity Model.

Philosophy

KMM Factors Affecting High Performance in Universities Case study on Al-Quds Open University in Gaza-Strip

©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 46 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering KM Maturity Factors Affecting High Performance in Universities "Case study on Al-Quds Open University in Gaza-Strip" 1Samy S. Abu Naser, 2Mazen J. Al Shobaki, 3Youssef M. Abu Amuna 1Faculty of Engineering & Information Technology, Al-Azhar University, Gaza,, Palestine 2Faculty of Engineering & Information Technology, Al-Azhar University, Gaza,, Palestine 3Faculty of Engineering & Information Technology, Al-Azhar University, Gaza, Palestine E-mail: , , ABSTRACT This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge management on high performance. This study was applied on Al-Quds Open University 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 (306). 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.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is subhypotheses. The most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and Innovation. 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 in the universities of Palestine explores the status of KMM using the Asian productivity Model. Keywords: Knowledge Management, KM Maturity, High performance, Universities, Asian Productivity Model. 1. INTRODUCTION Knowledge management in universities 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 is 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 knowledge management maturity 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 [26, 41]. The objective of this study is to measure knowledge management maturity in the universities. Also the study aims to define KMM level in the universities to encourage them moving to a higher level. The literature review shows that although this subject has got much attention, in general, the studies concentrate on KM implementation. Many diverse attempts to regulate a common model have been done, but knowledge management maturity still a concept requires a standard framework to use in the universities. In view of the literature review, the study raises the question of: Q1. What are the most influential factors on the University performance resulting from knowledge management maturity? Q2. How to link knowledge management maturity with performance and benefit from it for future performance improvements? 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 status of KMM using the Asian productivity model. 2. LITERATURE REVIEW 2.1 Knowledge Management in Universities Universities 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 [1, 2, 24, 49]. ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 47 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering Ramachandran et al. defined KM in universities as "Systematic attempt to develop and implement knowledge practices in universities with the support of major strategic assistance factors "[40]. Also defined by Petrides and Nodine as" 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"[34]. 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"[25]. According to previous definitions, KM in universities 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. Universities offer their services primarily to the community, and represents members of the community the main beneficiaries of Universities. Researchers identified the most reasons why universities environment is the most suitable to adopt KM: The existence of technology infrastructure, confidence and knowledge sharing is normal in universities and students enroll in a college to access to knowledge [1, 12, 14, 22, 28, 44, 57-59, 61-65]. 2.2 Critical factors for KM in Universities: The most critical variables that has an effect on sharing knowledge in universities are benefits and rewards [48]. While the research about this still few, Hislop suggests that the issues that concern to the staff regarding to assessment of advantages and disadvantages of sharing knowledge [16]. Benefits can be real rewards which improve the organization's performance and stability. It will be a big mistake when participation "is likely to" lead to abandonment of a source of strength and experience to others. While 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 [7, 39]. Leadership style is an additional important factor which play a 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 [41, 42, 43, 48, and 53]. The role of the leader can be completely different in the educational institutions where there are two types of leadership. First type is academic leadership which is interested in highlighting the knowledge, professional cognition, experience, personal qualities and teams. The second type is hierarchical management leadership with features such as: work, responsibilities, control and give power to position rather than ability. Significant tensions can exist when people with administrative capacity control the academic environment [11]. There is a immense dispute about the role of culture in educational institutions in the field of knowledge management and exchange [10, 26, 30, 41, 43, and 48]. For example, (Cronin) talked about contrast between the existence of companies cultures such as HP Computer Inc. "The HP Way" and the lack of any global culture like this in an universities [11]. Also (Lee) pointed that 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 have a culture of participation more than other forms of organizations and that cooperation is the essence of knowledge management [37]. One more important factor is organizational structure [10, 26, 41]. The structure of educational institutions diverge significantly from the other organizations. This structure might be a major difficulty for the exchange of knowledge, as the physical and psychological issue may be other obstacles which can guide to individualism. 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 [9, 41, 48, 56,60]. 2.3 High Performance in Universities: The educational institutions are service organizations providing education and knowledge to students and conducting scientific research. 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. Teaching process represents the intellectual capital for a variety of activities characterized by mental and intellectual nature and a host of other traditional activities. This performance measured by many excellence models such as BSC, Malcolm Baldrige American model, European model and Canadian model [3]. 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 [27]. ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 48 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering While operational scales provide a hidden image for performance such as new products, product quality, market share, innovation, customer retention, social responsibility[8,38]. 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 [34, 47]. Rani, Sania, AL-Hayaly and Alnajjar added that KM positively affects organizational outcomes of company innovation, product improvement and employee improvement[54, 55 ]. These researchers [1, 5, 14, 29] 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 gain 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: Knowledge management maturity 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 [23,57]. 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, those institutions that rely mainly on innovation and dissemination of knowledge like universities and thus improve the overall performance of the institution [46]. The maturity models describe the nature of things with the passage of time, such as development of knowledge management, what is necessary to move from a given level to another and stability at a certain level [20, 51]. O'Brien, Hoss and Schussed determined the importance of KM measurement as follow [18, 32]:  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 have 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 [51]. 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. ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 49 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering Figure 1. KM framework 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 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. 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 ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 50 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering The next step is to determine the level of maturity of knowledge management in the organization and comparing it with the maturity levels model. Figure 3: Maturity levels model Knowledge management maturity consist of five levels is composed as follow: 1Reaction: The organization is not interested in knowledge management and focused on enhancing productivity and competitiveness. 2Initiation level: The organization begin to realize the need for knowledge management or has already begun in a pilot project for knowledge management. 3Expansion: Knowledge management is fully applied. 4Refinement: Organization evaluates knowledge management on an ongoing basis. 5Maturity level: Knowledge management exist 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 [52]. 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 [6]. When reaching level three and four, organization begin to integrate knowledge sharing and collaboration in its main operations and set resources for knowledge management. Employees in levels three and four using 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 [19, 20, and 23]. ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 51 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering Figure 4: The conceptual framework The relation between the model used in this study 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 universities 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[1,4]. 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. Also the leadership need to link the mission and the vision and the objectives of the organization with knowledge management strategies. This leads to the following first sub-hypothesis in this study: h1-1: There is a statistically significant effect for KM leadership on universities high performance. Operations are considered a complete knowledge inside organization. As value chain reflect how far can organization add value in each production step to achieve organizational efficiency and increase performance [23, 33]. This leads to the following second sub-hypothesis in this study: h1-2: There is a statistically significant effect for operations on universities high performance. Many KM research confirmed individual's impact on high performance. These authors [1, 23, 37, and 41] 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 universities 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 [22, 23, and 35]. This leads to the following forth sub-hypothesis in this study: h1-4: There is a statistically significant effect for technology on universities high performance. Knowledge process like generation, storage, distribution and implementation facilitates work within the organization. The presence of a specialist team to capture knowledge and encourage workers to invest and participate 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 [5, 24, 50, and 53]. This leads to the following fifth sub-hypothesis in this study: h1-5: There is a statistically significant effect for Knowledge process on universities 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 [15, 43]. This leads to the following sixth sub-hypothesis in this study: h1-6: There is a statistically significant effect for learning and innovation on universities high performance. KM outcomes must reflex on effectiveness and efficiency inside the organization. This leads to high performance at individual level and organizational level [23, 24, and 41]. This leads to the following seventh sub-hypothesis in this study: h1-7: There is a statistically significant effect for KM outcomes on universities high performance. KMM MODEL HEI EXCELLENCE PERFORMANCE MATURITY LEVEL ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 52 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering 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 [21, 36, 45]. As established earlier, the current study examine the validity of the Asian productivity model for measuring KMM in universities. 3. RESEARCH DESIGN 3.1 Study population and sampling: This study conducted at Al-Quds Open University in Gaza Strip - Palestine. It is highly reputable one and established in 1991. The population are (306) employees, the control sample (244). The usable sample was (112), which makes the response rate (46%). 3.2 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). This tool contain seven audit criteria categories. The second dimension of the instrument which measure high performance in universities was developed by the current authors with the help of other research literature [1, 5, 29, and 50]. These statements were further revised and modified by 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 3.3 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 values for all dimensions were > (0.5). It indicated that the design of the questionnaire had a high internal consistency. 3.4 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. 3.5 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 2. 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. 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 and the dependent variable, and each dimension in the independent variable effect individually on the dependent variable. Therefore, we accept the main hypothesis and it is sub-hypotheses. ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 53 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering Table 2: Simple Linear Regression and "ANOVA" Variable Sig. Decision at α = 0.05 Regression coefficient Pearson coefficient r sign R2 KM Leadership 0.0 Significant 0.201 0.396 + 15.68% Processes 0.0 Significant 0.211 0.411 + 16.89% People 0.0 Significant 0.291 0.502 + 25.2% Technology 0.001 Significant 0.323 0.467 + 21.8% Knowledge Processes 0.017 Significant 0.189 0.232 + 5.38% Learning and Innovation 0.0 Significant 0.169 0.298 + 8.88% KM Outcomes 0.0 Significant 0.264 0.274 + 7.5% Further analysis, using Step wise regression conducted to arrange the effect of each variable in the knowledge management maturity model on the dependent variable and excluding of other insignificant variables. Table 3. Shows that five variables were effecting significantly (Processes, KM leadership, People, KM Outcomes,) and two 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 3: Step Wise R Rank Variable T Sig. Decision at α = 0.05 1 Processes 3.2 0.003 significant 2 KM Leadership 3.16 0.004 significant 3 People 2.69 0.011 significant 4 KM Outcomes 2.11 0.032 significant 5 Knowledge Processes 1.35 0. 68 insignificant 6 Learning and Innovation 1.52 0.035 significant 7 Technology 0.49 0.67 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 4. The total score was (126.94) which means that KMM is in level three (Expansion). University begin to integrate knowledge sharing and collaboration in its main operations and set resources for knowledge management. Employees in levels three using technology and standardized tools to capture, transfer, share and re-use of knowledge in the organization. In that level of maturity, university must expand here KM implementation to reach for level four where KM measuring is a continuous process. Also from table 4 we find that dimensions (5, 6, and 7) need to be improved. Table 4: Radar Chart No. Sub-Domain Al-Quds (5-30) 1 Leadership 22.16 2 Processes 24.13 3 People 20.13 4 Technology 17.92 5 Knowledge Processes 11.23 6 Learning and Innovation 14.36 7 KM Outcomes 16.56 Total (42-210) 126.49 ©2012-16 International Journal of Information Technology and Electrical Engineering ITEE, 5 (5) pp. 46-56, OCT 2016 54 ISSN: 2306-708X Volume 5, Issue 5 October 2016 ITEE Journal Information Technology & Electrical Engineering 4. CONCLUSION The concept of KM implementation is already known in Palestinian universities. 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 is leads to high performance. KMM assessment shows that the university maturity level is in level three where knowledge sharing and collaboration is common. Findings also support the main hypothesis and it is sub-hypotheses. The most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes, Learning and Innovation. Furthermore, the model effect totally on high performance. To have more accurate results (to generalize the model), another assessment must be done in periodical schedule (6 months for instant) after implementing the improved process. Authors recommend to adopt this model in universities in Palestine as a benchmark for knowledge management maturity and develop the model within specific criteria which suit with the MOHE requirements for high performance in higher educational Institutions. 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The paper assesses Knowledge Management Maturity(KMM) in the universities to determine the impact of knowledge management on performance excellence. This study was applied on Al-Azhar University and Al-Quds Open University in Gaza strip, Palestine.
          This paper depends on Asian productivity organization model that used to assess KMM. Second dimension which assess performance excellence was developed by the authors. The controlled sample was (610). 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.
          The overall findings of the current study suggest that KMM is suitable for measuring performance excellence. KMM assessment shows that both universities maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The most important factors effecting performance excellence are: Processes, KM leadership, People, KM Outcomes. 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 in the universities of Palestine explores the status of KMM using the Asian productivity Model.

Philosophy

Available online  Journal of Scientific and Engineering Research 4 Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR Knowledge Management Maturity in Universities and its Impact on Performance Excellence "Comparative study" Samy S Abu Naser, Mazen J Al-Shobaki, Youssef M Abu Amuna Faculty of Engineering & Information Technology, Al-Azhar University, Gaza Abstract The paper assesses Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge management on performance excellence. This study was applied on Al-Azhar University and Al-Quds Open University in Gaza strip, Palestine. This paper depends on Asian productivity organization model that used to assess KMM. Second dimension which assess performance excellence was developed by the authors. The controlled sample was (610). 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. The overall findings of the current study suggest that KMM is suitable for measuring performance excellence. KMM assessment shows that both universities maturity level is in level three. Findings also support the main hypothesis and it is subhypotheses. The most important factors effecting performance excellence are: Processes, KM leadership, People, KM Outcomes. 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 in the universities of Palestine explores the status of KMM using the Asian productivity Model. Keywords Knowledge Management, KM Maturity, Performance excellence, Universities, Asian Productivity Model. Introduction Knowledge management in educational institutions 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 knowledge management maturity is an important process and should not be ignored simply by knowledge management processes. 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, followed by procedures to enhance performance and efficiency to meet future challenges and attain outstanding performance [1-2]. The objective of this study is to examine the validity of the Asian productivity model in measuring knowledge management maturity in the universities. Also the study aims to define KMM level in the universities to encourage them moving to a higher level. This leads to performance excellence for individuals and organizations. The literature shows that although this subject has got much attention, in general, the studies concentrate on KM implementation. Many diverse attempts to regulate a common model have been done, but knowledge management maturity still a concept requires a standard framework to use in the universities. The current study draws on the literature to investigate this topic in depth and then turns to an implied exploration to assess the extent to which the model is precise and conclude the most important variables. In view of the literature review, the study raises the question of: Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 5 Q1. What are the most influential factors on the Universities performance resulting from knowledge management maturity? Q2. How to link knowledge management maturity with performance and benefit from it for future performance improvements? As for originality, the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first study in the Universities of Palestine explores the status of KMM using the Asian productivity model. Literature Review Knowledge management in Universities Universities 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 [3-6]. Ramachandran et al. defined KM in universities as "Systematic attempt to develop and implement knowledge practices in universities with the support of major strategic assistance factors "[7]. Also defined by Petrides and Nodine as" 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"[8]. 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 previous definitions, KM in universities 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. Universities offer their services primarily to the community, and represents members of the community the main beneficiaries of Universities. Researchers identified the most reasons why universities environment is the most suitable to adopt KM: The existence of technology infrastructure, confidence and knowledge sharing is normal in universities and students enroll in a college to access to knowledge [3, 10-14]. Critical factors for KM in Universities The most critical variables that has an effect on sharing knowledge in universities are benefits and rewards [15]. While the research about this still few, Hislop suggests that the issues that concern to the staff regarding to assessment of advantages and disadvantages of sharing knowledge [16]. Benefits can be real rewards which improve the organization's performance and stability. It will be a big mistake when participation "is likely to" lead to abandonment of a source of strength and experience to others. While 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 [17-18]. Leadership style is an additional important factor which play a 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, 15, 19-21]. The role of the leader can be completely different in the educational institutions where there are two types of leadership. First type is academic leadership which is interested in highlighting the knowledge, professional cognition, experience, personal qualities and teams. The second type is hierarchical management leadership with features such as: work, responsibilities, control and give power to position rather than ability. Significant tensions can exist when people with administrative capacity control the academic environment [11]. There is a immense dispute about the role of culture in educational institutions in the field of knowledge management and exchange [2, 15, 20, 22-24]. For example, (Cronin) talked about contrast between the existence of companies cultures such as HP Computer Inc. "The HP Way" and the lack of any global culture like this in an universities [25]. Also (Lee) pointed that academic departments are complex and cultures may be different among departments in different disciplines [22]. The fact that remains dominant here is that the academic community have a culture of participation more than other forms of organizations and that cooperation is the essence of knowledge management [26]. One more important factor is organizational structure [2, 22-23]. The structure of educational institutions diverges significantly from the other organizations. This structure might be a major difficulty for the exchange of knowledge, as the physical and psychological issue may be other obstacles which can guide to individualism. 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, 15, 27-28]. Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 6 Performance Excellence in Universities The educational institutions are service organizations providing education and knowledge to students and conducting scientific research. 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. Teaching process represents the intellectual capital for a variety of activities characterized by mental and intellectual nature and a host of other traditional activities. This performance measured by many excellence models such as BSC, Malcolm Baldrige American model, European model and Canadian model [29]. 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 [30]. While operational scales provide a hidden image for performance such as new products, product quality, market share, innovation, customer retention, social responsibility [31-32]. 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 [8-26]. Rani, Sania, AL-Hayaly and Alnajjar added that KM positively affects organizational outcomes of company innovation, product improvement and employee improvement [33-34]. These researchers [3, 11, 35-36] mention the main fields of performance excellence 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 performance excellence, which helps in the advancement of professional practice and gain 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 performance excellence that clarifies the role of the institution in civil society service and its contribution to solving its problems. Knowledge management maturity Knowledge management maturity 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 [37-38]. 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, those institutions that rely mainly on innovation and dissemination of knowledge like universities and thus improve the overall performance of the institution [39]. The maturity models describe the nature of things with the passage of time, such as development of knowledge management, what is necessary to move from a given level to another and stability at a certain level [40-41]. O'Brien, Hoss and Schussed determined the importance of KM measurement as follow [42-43]:  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 [41]. 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. Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 7 Figure 1: KM framework 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 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. 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 The next step is to determine the level of maturity of knowledge management in the organization and comparing it with the maturity levels model. Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 8 Figure 3: Maturity levels model Knowledge management maturity consists of five levels is composed as follow: 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. 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. KMM and performance excellence Knowledge management is not the only factor affecting performance and output of the organization, but it is one of many factors. Factors enabling performance excellence are leadership style, strategic planning, measurement, analysis, knowledge management, customers oriented, human resource management and administrative processes [44]. 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 [45]. When reaching level three and four, organization begin to integrate knowledge sharing and collaboration in its main operations and set resources for knowledge management. Employees in levels three and four using 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 [37, 40, 46]. Figure 4: The conceptual framework KMM MODEL HEI EXCELLENCE PERFORMANCE MATURITY LEVEL Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 9 The relation between the model used in this study and performance excellence leads to the following main hypothesis in this study: h1: There is a statistically significant effect for using the Asian knowledge model to measure universities performance excellence. 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 [3, 47]. 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. Also the leadership need to link the mission and the vision and the objectives of the organization with knowledge management strategies. This leads to the following first sub-hypothesis in this study: h1-1: There is a statistically significant effect for KM leadership on universities performance excellence. Operations are considered a complete knowledge inside organization. As value chain reflect how far can organization add value in each production step to achieve organizational efficiency and increase performance [23, 33]. This leads to the following second sub-hypothesis in this study: h1-2: There is a statistically significant effect for operations on universities performance excellence. Many KM research confirmed individual's impact on performance excellence. These authors [2-3, 26, 37] 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 universities performance excellence. 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 [12, 37, 48]. This leads to the following forth sub-hypothesis in this study: h1-4: There is a statistically significant effect for technology on universities performance excellence. Knowledge process like generation, storage, distribution and implementation facilitates work within the organization. The presence of a specialist team to capture knowledge and encourage workers to invest and participate 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 [5, 21, 49, 50,]. This leads to the following fifth sub-hypothesis in this study: h1-5: There is a statistically significant effect for Knowledge process on universities performance excellence. 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 [20,51]. This leads to the following sixth sub-hypothesis in this study: h1-6: There is a statistically significant effect for learning and innovation on universities performance excellence. KM outcomes must reflex on effectiveness and efficiency inside the organization. This leads to performance excellence at individual level and organizational level [2, 5, 37]. This leads to the following seventh subhypothesis in this study: h1-7: There is a statistically significant effect for KM outcomes on universities performance excellence. 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 [52-54]. As established earlier, the current study examines the validity of the Asian productivity model for measuring KMM in universities. Research Design Study population and sampling: This study conducted at Al-Azhar University and Al-Quds Open University in Gaza Strip - Palestine. Both universities are highly reputable one and established in 1991. The population are (762) employees, the control sample (610). The usable sample was (410), which makes the response rate (67%). 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). This tool contain seven audit criteria categories. The second Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 10 dimension of the instrument which measure performance excellence in universities was developed by the current authors with the help of other research literature [3, 36, 49-50]. These statements were further revised and modified by 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 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 values for all dimensions were > (0.5). It indicated that the design of the questionnaire had a high internal consistency. 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. 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 (Performance Excellence). The results are shown in Table 2. 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. 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 and the dependent variable, and each dimension in the independent variable effect individually on the dependent variable. Therefore, we accept the main hypothesis and it is sub-hypotheses Table 2: Simple Linear Regression and "ANOVA" Variable Sig. Decision at α = 0.05 Regression coefficient Pearson coefficient r sign R2 KM Leadership 0.0 Significant 0.218 0.470 + 22.09% Processes 0.0 Significant 0.241 0.480 + 23.04% People 0.0 Significant 0.187 0.372 + 13.83% Technology 0.001 Significant 0.186 0.254 + 6.45% Knowledge Processes 0.017 Significant 0.142 0.210 + 4.41% Learning and Innovation 0.0 Significant 0.201 0.265 + 7.02% KM Outcomes 0.0 Significant 0.232 0.342 + 11.69% Further analysis, using Step wise regression conducted to arrange the effect of each variable in the knowledge management maturity model on the dependent variable and excluding of other insignificant variables. Table 3. Shows that four variables were effecting significantly (Processes, KM leadership, People, KM Outcomes,) and three were not effecting (Learning and Innovation, Technology). The explanation for that is the effect for the four variables were very strong on performance excellence more than (Knowledge Processes, Learning and Innovation, Technology) from the point of view of the sample. Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 11 Table 3: Step Wise R Rank Variable T Sig. Decision at α = 0.05 1 Processes 3.3 0.001 Significant 2 KM Leadership 2.64 0.005 Significant 3 People 2.31 0.021 Significant 4 KM Outcomes 2.42 0.028 Significant 5 Knowledge Processes 2.17 0. 62 Insignificant 6 Learning and Innovation 0.82 0.49 Insignificant 7 Technology 0.35 0.87 Insignificant According to the model, radar chart had been done by calculating the response of each paragraph in subdomains rates as shown in Table 4. The total score was (129.815) which means that KMM is in level three (Expansion). Universities begin to integrate knowledge sharing and collaboration in its main operations and set resources for knowledge management. Employees in levels three using technology and standardized tools to capture, transfer, share and re-use of knowledge in the organization. In that level of maturity, university must expand here KM implementation to reach for level four where KM measuring is a continuous process. Also from table 4 we find that Al-Azhar University leads in dimensions [3-4, 17, 45, 49]. The anthers refer that lead to the diversity of colleges in Al-Azhar University, the stuff qualifications and experience. While Al-Quds open university leads in dimensions [29, 47]. The explanation of that refer to the education system in the university. Table 4: Radar Chart No. Sub-Domain Al-Azhar (5-30) Al-Quds (5-30) Total (5-30) 1 Leadership 24.62 22.16 23.39 2 Processes 26.71 24.13 25.42 3 People 18.82 20.13 19.475 4 Technology 15.94 17.92 16.93 5 Knowledge Processes 12.68 11.23 11.955 6 Learning and Innovation 16.39 14.36 15.375 7 KM Outcomes 17.98 16.56 17.27 Total (42-210) 133.14 126.49 129.815 Conclusion The concept of KM implementation is already known in Palestinian universities. Many studies conducted on KM, but this is the first study measuring KM maturity concept in Palestinian universities using a model designed by top eight manufacturing countries in Asia. The originality of study comes from being the first one as far as the authors knowledge that discuses KMM using a solid model. The overall findings of the current study suggest that KMM is suitable for measuring and lead to performance excellence. KMM assessment shows that the university maturity level is in level three where knowledge sharing and collaboration is common. Findings also support the main hypothesis and it is sub-hypotheses. The most important factors effecting performance excellence are: Processes, KM leadership, People, KM Outcomes. Furthermore, the model effect totally on performance excellence. One of the important limitation of this study that it was conducted in two Palestinian universities in one geographical region. Comparison study between different regions will be more reliable. To have more accurate results (to generalize the model), another assessment must be done in periodical schedule (6 months for instant) after implementing the improved process. Moreover, a comparison study between AlAzhar and other higher educational Institutions will be more reliable. Authors recommend to adopt this model in universities in Palestine as a benchmark for knowledge management maturity and develop the model within specific criteria which suit with the MOHE requirements for performance excellence in higher educational Institutions. References [1]. Lee, C., & Wong, K. (2015),"Development and Validation of Knowledge Management Performance Measurement Constructs for Small and Medium Enterprises", Journal of Knowledge Management, 19(4): 711 - 734. [2]. Rivera, G., & Rivera, I. (2016), "Design, Measurement and Analysis of a Knowledge Management Model in the Context of a Mexican University", INNOVAR. Revista de Ciencias Administrativas y Sociales, 26:1-15. Naser SSA et al Journal of Scientific and Engineering Research, 2016, 3(4):4-14 Journal of Scientific and Engineering Research 12 [3]. 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Philosophy

Mathematics Intelligent Tutoring System

International Journal of Advanced Scientific Research 11 International Journal of Advanced Scientific Research ISSN: 2456-0421; Impact Factor: RJIF 5.32  Volume 2; Issue 1; January 2017; Page No. 11-16 Mathematics intelligent tutoring system Nour N AbuEloun, Samy S Abu Naser Department of Information Technology, Faculty of Engineering & Information Technology, Al-Azhar University, Gaza, Palestine Abstract In these days, there is an increasing technological development in intelligent tutoring systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that help students understand the basics of math and that helps a lot of students of all ages to understand the topic because it's important for students of adding and subtracting. Through which the student will be able to study the course and solve related problems. An evaluation of the intelligent tutoring systems was carried out and the results were encouraging. Keywords: intelligent tutoring system, e-learning, mathematics 1. Introduction Intelligent tutoring systems (ITSs) have been investigated in AI now for several decades. With the enormous development and increasing availability of the program, the application of intelligent learning systems becomes more probable and accurate and research for intelligent features obtains more care than before. As a result, a number of new ITS have been established over the last years, program, and web page [28]. This project is based adaptive learning environment for mathematics. These systems strive for improving long-distance learning, for complementing traditional classroom teaching, and for supporting individual and life-long learning, and allow a user to learn in his own environment and whenever it is appropriate for him [28]. 2. Literature Review There are many ITS that were developed for: ITS for Learning Java Objects [4], ITS teaching grammar English tenses [10], Java Expression Evaluation [3], Linear Programming [23, 26, 27], ITS for Teaching Mongo Database[18], ITS for C# Language [7], effectiveness of e-learning [5, 31], effectiveness of the CPP-Tutor [25], teaching AI searching algorithms [30], teaching database [24], and ITS for Teaching the 7 Characteristics for Living Things [17], ITS for teaching the right letter pronunciation in reciting the Holy Quran [6], ITS for Health problems related to addiction of video game playing [8], ITS for teaching advanced topics in information security [19], Oracle Intelligent Tutoring System (OITS) [9], ITS for learning Computer Theory[14], e-learning system [29], ADO-Tutor: Intelligent Tutoring System for leaning ADO.NET [15], ITS for Parameter Passing in Java Programming [22], and Predicting learners performance using NT and ITS [27], CPP-Tutor for C++ Programming Language [20], a comparative study between Animated Intelligent Tutoring Systems (AITS) and Video-based Intelligent Tutoring Systems (VITS) [21], ITS for stomach disease Intelligent Tutoring System [13], ITS for diabetes [12], Computer Networks [11], DSETutor for Teaching DES Information Security Algorithm [16]. 3. ITS Architecture In this paper, we used ITSB authoring tool [28]. This tool designed and developed by Professor Samy S. Abu Naser [28]. He used Delphi Embarcadero XE8, 2015 [2]; this tool supported two languages: English and Arabic and has two systems in one application. The first system is the teacher system where it allows the teachers to add course materials, questions and answers. The second system is the student system where it allows the student to study course materials and answer exercises. This tool contains four modules: domain module, teaching module, student module and user interfaces module. The first module stores and organizes the material in chapters or lessons. The second module works as control engine. The third module gives the system all essential information so it can adapt itself to the student. The last module has two sections one for the student and the other for the teacher as shown in Fig 1. Fig 1: Overall System Architecture 3.1 Domain Module This model deals with many important topics of interest in the System for Mathematics. The topics covered in intelligent tutoring system are:  Algebra  Real numbers  Integer Exponents  Roots of real numbers  Rational exponents  Radicals polynomials, International Journal of Advanced Scientific Research 12  Addition and subtraction and  Factoring second degree polynomials. 3.2 Student Module A new student account must be created to have a profile where it allows the student to study course materials and answer the exercises. The profile has information about the student such as student name, last session date, student number, current score, overall score, level of difficulty, and problem number during the each session. The current score represents student score for each level. The overall score represents student score for all levels. 3.3 Teaching Module Teaching module works as a manager that controls the functionality of the entire system. Through this module, a student can answer questions on the first level of difficulty and if he/she gets 75% mark or more, he/she can move to the next level. But if he/she fails to get the marks, he/she repeats to the exercises of the same difficulty level again. 3.4 User Interface Module The ITSB tool used for building the current ITS system has an interface that supports two classes of users: teachers and students. When the teacher's log into the system, the teacher can add/modify lessons, exercises, answers, initial information about the student, configure/adjust the color, font name, and size of all buttons, menus, and combo boxes. Therefore, this interface provides the system with the required heftiness and suppleness. A screenshot of the teacher's interface is shown in, Fig 4, Fig 5 and Fig 6. But when the student logs into the system, he/she can study the lessons, examples, solve the exercises for each lesson. A screenshot of the students interface can be seen in Fig 2, Fig 3, Fig 9, and Fig10. Fig 2: Student lessons form. Fig 3: Student lessons and examples form International Journal of Advanced Scientific Research 13 Fig 4: Form for adding Lessons and Examples Fig 5: Form for adding constants of the system Fig 6: Form for adding initial students' information International Journal of Advanced Scientific Research 14 Fig 7: Form for adjusting Fonts of all screens of the system Fig 8: Form for adding questions and answers Fig 9: Student Exercises form International Journal of Advanced Scientific Research 15 Fig 10: Student statistics form. 4. Evaluation We have asked the students who are interested in trying an Intelligent Tutoring System for Mathematics. Almost all students welcomed the idea. After trying the intelligent tutoring system for mathematics, they expressed their felling of the system. They said it is easier for them to study using the new system and it is very efficient in terms of the material and exercises and the difficulty levels of the questions. 5. Conclusion In this paper, we have designed an Intelligent Tutoring System for teaching Mathematics by using ITSB authoring tool. The system was designed to facilitate the learning Mathematics to overcome the problems students face during their study. An initial evaluation of the intelligent tutoring system as done using a group of students and the results of the evaluation was encouraging. 6. References 1. , Accessed 05 January 2017. 2.  Accessed 05 January 2017. 3. Abu Naser S. JEE-Tutor: An Intelligent Tutoring System for Java Expression Evaluation, Information Technology Journal, Scialert. 2008; 7(3):528-532. 4. Abu-Naser S, Ahmed A, Al-Masri N, Deeb A, Moshtaha E, AbuLamdy M. An Intelligent Tutoring System for Learning Java Objects. International Journal of Artificial Intelligence and Applications (IJAIA). 2011, 2(2). 5. Abu-Naser S, Al-Masri A, Sultan YA, Zaqout I. A prototype decision support system for optimizing the effectiveness of elearning in educational institutions. International Journal of Data Mining & Knowledge Management Process(IJDKP). 2011; 1:1-13. 6. Akkila AN, Naser SSA. Teaching the Right Letter Pronunciation in Reciting the Holy Quran Using Intelligent Tutoring System. International Journal of Advanced Research and Development. 2017, 2(1). 7. Al-Bastami BH, Naser SSA. Design and Development of an Intelligent Tutoring System for C# Language, European Academic Research. 2017, 4(10). 8. Al-Bayed1 MH, Naser SSA. An Intelligent Tutoring System for Health Problems Related To Addiction of Video Game Playing. International Journal of Advanced Research and Development. 2017, 2(1). 9. ALDahdooh R, Naser SSA. Development and Evaluation of the Oracle Intelligent Tutoring System (OITS), European Academic Research. 2017, 4(10). 10. Alhabbash MI, Mahdi AO, Abu Naser SS. An Intelligent Tutoring System for Teaching Grammar English Tenses. European Advanced Research. 2016; 4(9):77437757. 11. Al-Hanjori MM, Shaath MZ, Naser SSA. Learning Computer Networks Using Intelligent Tutoring System. International Journal of Advanced Research and Development. 2017, 2(1). 12. Almurshidi SH, Naser SSA. Design and Development of Diabetes Intelligent Tutoring System. European Academic Research. 2017; 4(9):8117-8128. 13. Almurshidi SH, Naser SSA. Stomach Disease Intelligent Tutoring System. International Journal of Advanced Research and Development. 2017, 2(1). 14. Al-Nakhal MA, Naser SSA. An Intelligent Tutoring System for learning Computer Theory. European Academic Research. 2017, 4(10). 15. El Haddad IA, Naser SSA. ADO-Tutor: Intelligent Tutoring System for leaning ADO.NET. European Academic Research. 2017, 4(10). 16. Elnajjar AA, Naser SSA. DSE-Tutor: An Intelligent Tutoring System for Teaching DES Information Security Algorithm. International Journal of Advanced Research and Development. 2017, 2(1). 17. Hamed MA, Naser SSA. An Intelligent Tutoring System for Teaching the 7 Characteristics for Living Things. International Journal of Advanced Research and Development. 2017, 2(1). 18. Hilles MM, Naser SSA. Knowledge-based Intelligent Tutoring System for Teaching Mongo Database. European Academic Research. 2017, 4(10). 19. Mahdi AO, Alhabbash MI, Naser SSA. An intelligent tutoring system for teaching advanced topics in information security. World Wide Journal of International Journal of Advanced Scientific Research 16 Multidisciplinary Research and Development. 2016; 2(12):1-9. 20. Naser S. Evaluating the effectiveness of the CPP-Tutor an intelligent tutoring system for students learning to program in C++. Journal of Applied Sciences Research. 2009; 5(1):109-114. . 21. Naser SA. A comparative study between Animated Intelligent Tutoring Systems (AITS) and Video-based Intelligent Tutoring Systems (VITS). Al-Aqsa University Journal. 2001; 5(1 Part):1. 22. Naser SA. An Agent Based Intelligent Tutoring System For Parameter Passing In Java Programming. Journal of Theoretical & Applied Information Technology. 2008, 4(7). 23. Naser SA, Ahmed A, Al-Masri N, Sultan YA. Human Computer Interaction Design of the LP-ITS: Linear Programming Intelligent Tutoring Systems. International Journal of Artificial Intelligence & Applications (IJAIA). 2011; 2(3):60-70. 24. Naser SSA. Intelligent tutoring system for teaching database to sophomore students in Gaza and its effect on their performance. Information Technology Journal; Scialert. 2006; 5(5):916-922. 25. Naser SSA. Developing an intelligent tutoring system for students learning to program in C++. Information Technology Journal, Scialert. 2008; 7(7):1055-1060. 26. Naser SSA. A Qualitative Study of LP-ITS: Linear Programming Intelligent Tutoring System. International Journal of Computer Science & Information Technology. 2012; 4(1):209-220. 27. Naser SSA. Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system. International Journal of Artificial Intelligence & Applications. 2012; 3(2):65-73. 28. Naser SSA. ITSB: An Intelligent Tutoring System Authoring Tool. Journal of Scientific and Engineering Research. 2016; 3(5):63-71. 29. Naser SSA, Sulisel O. The effect of using computer aided instruction on performance of 10th grade biology in Gaza. Journal of the College of Education. 2000; 4:9-37. 30. Naser SSA. Developing visualization tool for teaching AI searching algorithms, Information Technology Journal, Scialert. 2008; 7(2):350-355. 31. Mahmoud AY, Barakat MS, Ajjour MJ. Design and Development of Elearning University System. (Journal of Multidisciplinary Engineering Science Studies (JMESS). 2016; 2(5):498-504.