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Personalization needs to identify the learners’ preferences and their characteristics as an important part in any e-learning environment which without identify learners’ mental characteristics and their learning approaches, personalization cannot be possible. Whatever this identifying process has been done more completely and more accurately, the learner model that based on it will be more reliable. Using the combination and relation of effective theories in learning approaches detection such as learning style and cognitive trait, have been used in this research. Also for reducing ambiguity in learners’ opinions and their feedbacks, have been used fuzzy logic. This study was conducted during one semester on some e-learning students in engineering field based on fuzzy recommender system in two phases. This recommender is part of Intelligent Tutoring System as prepared some recommendations based on learning style in first phase and on half of courses and in second phase and on remaining courses, prepared recommendations based on combination of two mentioneed theories. Learners’ ability have been monitored and evaluated based on fuzzy item response theory in all steps. Measures of Intelligent Tutoring System have been optimized after this combination that clarifies the presentation of accurate recommendations in appropriate time. The time of effective learning and amount of referee to tutor have decreased, learner’s and tutor’s view to e-learning that define such as learners’ success rate and the learner’s satisfaction have improved increasingly.

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The purpose of this study was the evaluation of e-learning in Iran higher education by using benchmarking approach. The population was all of students in four universities which presented e-learning. By multistage clustered sampling method, 702 students were selected. The research instrument was a researcher made questionnaire included e-learning (organization and management affair, technology, educational aspects, interface design, support services, evaluation and ethical and legal considerations). After calculating validity and reliability, the questionnaires were distributed among sample students and data was analyzed by ANOVA and Scheffe post hoc. Results showed: the university D with higher mean in all of seven dimensions had the best position among three other universities. Furthermore, the university B in five dimensions and the university C in two dimensions was the best. While benchmarking can be tested processes and models from other universities and implementing techniques and approaches used them more.

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As computer networks become the backbones of science and economy, enormous quantities documents become available. So, for extracting useful information from textual data, text mining techniques have been used. Text Mining has become an important research area that discoveries unknown information, facts or new hypotheses by automatically extracting information from different written documents. Text mining aims at disclosing the concealed information by means of methods which on the one hand are able to cope with the large number of words and structures in natural language and on the other hand allow handling vagueness, uncertainty and fuzziness. Text mining, referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text that high-quality information is typically derived through the patterns and processes. Moreover, text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting patterns or knowledge from text documents. In this research, a survey of text mining techniques and applications in e-learning has been presented. During these studies, relevant researches in the field of e-learning were classified. After classification of researches, related problems and solutions were extracted. In this paper, first, definition of text mining is presented. Then, the process of text mining and its applications in e-learning domain are described. Furthermore, text mining techniques are introduced, and each of these methods in the field of e-learning is considered. Finally, a model for the information extraction by text mining techniques in e-learning domain is proposed.

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Today the world is moving toward a virtual space. Whereas the business, health, education, and many of the other activities would devote time and money in the past, by appearing the virtual space most of them have suitable function today. Educating is an inseparable part of human which is entered to electronic world as individuals and organizations are gradually moving towards this type of educating. In this research, the relationship between emotional intelligence and trends of e-learning in organizations has been studied. The purpose of this study is applied and the research method is descriptive- survey. The questionnaire method was used to collect data. The Cronbach’s Alpha coefficient of the first questionnaire was obtained 0.76 and the second questionnaire was 0.83 which confirm the validity of the questionnaires. The content validity to test questions was used and for this purpose, experts, academics and experts were used. The population of this research is all the Alborz Province Bank Employees which 80 individuals were selected as sample size and sampling is done randomly. To analyze data the Spearman correlation coefficient and multiple regression was used. Finally all the theories proved. Self-motivation and self-awareness variables were determined as predictor variables which can entry into the ultimate regression equation to describe the tendency of employees to accept changes in their electronic education.

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Virtual university, offering an appropriate model for its creation and e-Learning realization, is a significant issue that should be considered seriously by managers and higher education administrative. Higher education system, through virtual university development not only could enhance the accessibility of people who interested in learning regardless of time and place constraints but also could full fill the issues such as developments of new strategies on leaning, representing the distinguished course materials, employed the outstanding faculty members, teaching and learning based on individuals ability, augmentation on effectiveness, increase the individual responsibility in learning (student base), learners society realizations and research association establishment.

An attempt have been made to investigate the faculty members’ competencies in virtual environment. Considering a descriptive correlation type model and presenting a conceptual model four quarries utilized in order to assessing the model which Mehralborz university choose for field study and evaluating the practical results and their implementations. The questionnaire choose for purpose of data gathering which is checked and verified for validity and reliability and the produced results assessed in two stages. Using the check lists the most adapted competencies with model determined and by using three underlying factors they categorized in three groups. On the second stage, considering the competency discriminations the questionnaire handed out among the faculty members (all these people are working in the field of e-learning) and they gathered for analysis after questionnaires complementation. The results show that there is a significant correlation coefficient between the faculty members’ competency factors in virtual environment. Furthermore the dimensions and competency’s factor as well as students and teachers approach were examined and these two approaches were prioritized.

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One of the key issues in e-learning is to identify needs, educational behavior and learning speed of the learners and design a suitable curriculum commensurate to their abilities. This goal is achieved by identifying the learners’ different dimension of personality and ability and assigning suitable learning material to them according these features. In this paper, an intelligent tutoring system is proposed which optimizes the LO selection in e-learning environment. In order to evaluate the proposed method, the designed system has been used in a web-based instruction system in different conditions and the results of the "Academically success", "Satisfactory learning achievement" and "Time of the learners’ attendance" have been analyzed. The obtained results show a significant efficiency compared to other applied methods.

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In the third millennium, people deal with multiple, diverse, and complicated problems as they cannot possess full control over the information, which is constantly produced and accumulated. Having a high skill of critical thinking for assessing the results of different issues and decision making about them based on evidences is an unavoidable necessity. The researchers of this work proposed a model with seven factors (components) for critical thinking in e-learning environments. The statistical group of this work is the M.Sc. medical education students of  AZAD university e-learning environments, and the students of the same field from Islamic Azad University traditional education system studying during 2011-2012. Among the research community, 47 members were selected based on a simple random method and divided into two trial (with 23 members) and reference (with 42 members) groups. To train the trial group, the seven-factor critical thinking training scale was utilized in e-learning environments in 15 sessions with empirical sciences course. In the reference group, the same seven-factor critical thinking training scale was used in the classroom environment in lecturing in 15 sessions with empirical sciences course. The model factors and components are challenge, representation, creation of opportunity, creation of motivation, logical analysis, encouragement, responsibility, and commitment. Both groups were subject to two pretest and posttest steps within two trial groups, which were considered as reference to each other. Both groups responded to the Watson- Glaser™ Critical Thinking Appraisal within two pretest and posttest steps, while the covariance analysis statistical test was used for analysis of the results. The results indicate significant difference between the scores between trial and reference groups in improving the critical thinking of the students in terms of inferential, assumption detection, deduction, interpretation, and logical reasoning evaluation components (p=0.001). According to the results, in terms of improving critical thinking, the trial group trained in the e-learning environment indicates higher scores as compared to the group trained in the traditional classroom environment.

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Automatic identification of learners groups based on similarity of learning style improves e-learning systems from the viewpoint of learning adaptation and collaboration among learners. In this paper, a new system is proposed for identifying groups of learners, who have similar learning style, by using learners’ behavior information in an e-learning environment. Proposed clustering method for separation of learners is developed based on ART neural network structure and Snap-Drift neural network learning process. This artificial network enables us to identify learners groups in uncertain group separation parameters, without knowing appropriate number of groups.  The results of an empirical evaluation of the proposed method, which are based on two criteria, “Davies-Bouldin” and “Purity and Gathering”, indicate that our proposed method outperforms other clustering methods in terms of accuracy.

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There is an increasing use of technology in an attempt to enhance teaching and learning in medical education, from the use of websites and virtual learning environments (VLEs) to interactive online tutorials to blogs and podcasts. Beneficiaries of CME programs did not show interest in e-learning subjects and they showed more tendencies to traditional education. We conducted this study to assess the influential factors on development of e-learning subjects from the perspective of beneficiaries of continuing medical education (CME) programs in Jahrom University of Medical Sciences in 2012-2013. This is a randomized and descriptive cross sectional study. 976 people attending in CME program participated in this study on voluntary basis. Approval of doing research was taken from ethic and research committee of JUMS. They were evaluated through a valid and refined questionnaire including demographic information and 62 questions in 7 fields. Data were analyzed with T-test and Fisher’s exacts. Results:. In this study 66.3% were female and 33.7% were male: Instructional factors got the highest rank in development of e-learning materials and role of each field was assessed in e-learning are as below: Instructional factors: 28.37%, Technology: 24.17%, E-learning method: 28.26%, beneficiaries (24.83%), informative literacy (22.7%), references ( 19.07%.) and others(15.94%). Conclusion: Most of the participants believed that instructional factors are the most important one and having an organized program in development of E-learning is important too. It can be understood that the role of planners in programming is of the highest importance and special courses should be held to satisfy the needs of the participants.

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Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the SERVQUAL service quality assessment method has been used as a framework. However the questions have been reviewed based on the previous literature and the experts’ views, leading to a questionnaire designed for the healthcare domain. Data collection has been performed using the questionnaires on subjects selected amongst clients of Rhinoplasty Centers in Tehran. To analyze the data, three machine-learning approaches have been implemented namely Decision Tree, Support Vector Machine and Artificial Neural Networks. A number of possible factors affecting the patient-physician relationship have been used as input and patient satisfaction has been taken as output. Comparing the results of these three methods, Artificial Neural Networks method is shown to have better performance, which has therefore been used for prioritizing the effective factors in this relationship. The results indicate that reaching the information which the patient expects their physician to give is the most effective characteristic in patient satisfaction. The rank of gained features were compared with similar researches. The outcome was very similar and approved the results.