International Journal Information and Communication Technology Research
Volume:3 Issue: 1, Winter 2011

Learner knowledge evaluation is a process of decision making, personalization and adaptation of learning system. Generally, Traditional methods of knowledge evaluation use few variables in the evaluation process; For instance, the only parameter which is involved in the process is accuracy of answers. While, in case of increasing the accuracy of evaluation in this process, more variables need to be considered. Fuzzy system with ability to accept variety of input variables, inference and output generation, is a good option for the process of evaluation, such that in the recent years there are so much effort in the way of using fuzzy logic and its capabilities in knowledge evaluation that have been done so far. In this paper we will present a method to evaluate the knowledge of learner that by using fuzzy system in three phases which are fuzzification, inference engine, defuzzifaction with considering more variables like accuracy rate, importance and complexity of questions, performs the evaluation process. The prime features of the presented system are clarity, flexibility and simplicity in implementation.

Selection of appropriate learning objects and delivery of them to learners considering student's characteristics are known as a challenging task in e-learning systems. In design and development process of educational material, the attention must be focused on learner’s characteristics and requirements which are defined in terms of content and learning style. To determine the best learning object, a model of learner can be constructed based on some learner's personal and behavioral features like learning styles, user’s browsing history and user’s prior knowledge. Grouping students based on their learning styles is one of appropriate approaches which have been followed in this area. However, some special characteristics and limitations of e-learning environments have led to the fact that any decision making and adaptation based only on static learning style recognition might be deficient. In this paper we introduce an evolutionary fuzzy clustering (EFC) method using genetic algorithm, in which learners are divided into some categories according to their behavioral factors and interactions with the system in order to adopt the most appropriate learning objects, methods, and recommendations. Results of the proposed method are compared with K-means and fuzzy C-means clustering methods using Davies-Bouldin cluster validity index and the comparison shows that EFC method has the better clustering performance than the others.

Transferring information in an organized and coordinated way is an effective factor in forming innovative mentality in learner. Content organization has a significant role on this mentality formation. The objective of content organization may be representing a description, analyzing a subject, presenting a justification, applying a reasoning style and consolidating a situation. The purpose of this study is to present a framework for organizing research support content, considering the projection of learner’s reasoning style, content processing perspective, and existing features of content, onto research abilities. Applying these different orthogonal aspects may bring about a precise research ability. In order to provide an efficient research support e-learning environment, it is necessary to enrich a traditional education system with research support services. Such an enriched environment affects mental/practical ability of researcher as well as accelerating research process through decreasing time, cost and energy.

E-learning as a modern educational system has certain components, and adopting this system will affect the conditions and characteristics of administrative aspects of the educational systems. Some of the critical educational factors such as requirements/ necessary skills /capabilities/ admission factors and learner capabilities / characteristics will evolve significantly in this modern educational paradigm.The main objective of this paper is to present a framework for main indices and factors regarding e-learner readiness evaluation and also identifying the learners’ skills, capabilities and limitations in a way to enable e-learning system administrators to provide preparation and support services for improving learning quality. In this paper, through a survey of e-learners’ readiness characteristics, a framework for e-learners’ primary preparation and corresponding guidelines for e-learners’ support is proposed which is based on level of readiness. The proposed framework is targeted to academic courses, though its analysis mechanism can be easily workable for other educational courses as well.

E-learning environments are being used more efficiently by the rapid growth in internet and multimedia technologies. Adaptive learning is a kind of learning environment which provides individual learning. It can customize the learning style according to the individual’s personality and characteristics. Although there are a lot of elearning systems having adaptive learning feature, they do not satisfy all adaptive learning aspects. This paper proposes a new method which tries to help learners find educational contents adapted to their personalities in an efficient manner. Our proposed method has four essential parts: 1) It finds out learner’s features by MBTI and Kolb learning style tests or Bayesian networks. 2) Then It tries to select the most appropriate adaptive learning objects with 0/1 knapsack problem in a limited amount of time determined by learner. 3) An ant colony optimization algorithm is proposed to solve 0/1 knapsack problem efficiently. 4) Selected learning objects are then sequenced in order to preserve the prerequisites.Also we created a software application based on this method called BehAmooz for learners to find and comprehend the educational contents effectively. The results obtained by experimentations showed that this method could satisfy most of the students.

Educational data mining (EDM) extracts implicit and interesting patterns from large data collections to provide a more effective learning environment. Introducing EDM concepts and techniques, this paper aims to discover existing behavioral pattern of students in course selection and faculty evaluation as well as educators policies in grading. This study has been carried out to determine the correlation between Student Evaluation of Teachers (SET) ratings and their gained scores in University of Tehran, department of Information Technology engineering.Dividing students based on their grades, a weak direct relationship has been demonstrated among weak and good students (0.086, 0.108) meaning that the more students’ grades were, the higher teacher evaluation scores were observed. Insufficient students’ awareness of SET importance and the existence inappropriate questions beyond students’ knowledge in the questionnaire may cause these results. Recognizing effective factors in SET ratings can reveal the strengths and weaknesses of this kind of faculty evaluation and provide the possibility for better planning and obtaining authentic results.

There are many opportunities to improve E-Learning web-based applications with regard to continue challenges e.g. their lack of interaction between teachers and students and structural evaluation of the presented learning activity. In this paper, E-Learning system web server log data and information about its control panel are provided; then the gathered data were preprocessed. After extracting association rules form these data and selecting results using more confidence coefficients, they were used as a virtual consultant for improving the E-Learning system. MySQL was used as the database for storing and extracting patterns. Extracting association rules with more confidence coefficients was done using Weka Software. The selected E-Learning web application was Moodle and the virtual university case study was Iran University of Science and Technology. The obtained results showed the needs which cannot be granted by a human consultant easily and cannot be inferred from current application log directly.

Finding subject experts for problem solving is an important issue in e-learning environment. In e-learningenvironment there is no direct way to find the superior individuals. The current methods like analyzing the discussions or considering the learner actions need a lot of data or have some limitations. In this work, concept maps are utilized to define the experts in an e-learning environment. Concepts maps are graphical explanation of the meaningful relationship among the concepts. 35 M.Sc. and B.Sc. students participated in this work. The test design, implemented software, results and conclusions are described in detail in this article. The novelty of this work is the application of concept maps to find the experts, the combined presented models for concept map construction, and presenting a research methodology in this area.