Combining Intelligent Algorithms and E-Learning Styles to Create an Improved Intelligent System in Evaluating an E-Learning Student’s Profile

The e-learning platforms combining both digital contents and knowledge management, are taking an important role in education at the same being used by many enterprises on employee’s training to promote competitiveness. Their characteristics of learning anytime and anywhere, making use of the mobile technology and cloud applications, give them superiority compared to traditional teaching methods in the classroom. Since the students and teachers are on different time and space in an e-learning environment, the learning status of a student is difficult to be controlled by teachers. Also the majority of the existing formation platforms are generally conceived as contents distribution systems, with few concerns about the interests and the immediate reaction ofsingular learners in the virtual classroom. In order to achieve efficiency and trying to avoid the above mentioned disadvantages, there is a need for gathering information regarding each learner’s profile, and building a personalized path of learning for each student or students with similar profile progress in the learning process. In order to get information about the students' profile, meaning the way he wants and is able to gather knowledge, questionnaires to evaluate his/her psychological profile can be of great help. In this paper we address the issue of e-learning personalization through implementing Intelligent Algorithms based on Intelligent Agentsin an e-learning environment. The IAELS Algorithm and the Agent System Based Algorithm are compared in a qualitative and quantitative way. Results are presented based on students’ opini¬ons and their performance achieved in the Microsoft Office Suite 2010 e-learning course. Further developing this kind of intelligent evaluating system we propose development of a questionnaire, so that based on different learners' profiles, we could incorporatea starting point in building e-learning ennvironment for gathering virtual knowledge by the e-student.Keywords:  E-learning platform, Intelligent algorithms, Agent based algorithm, IAELS Algorithm, e-learning path

Pengelolaan Pembelajaran IPS Berbasis Penguatan Karakter Siswa di SD Al Firdaus Surakarta

This research is aimed to describe: the management of material arrangement, the management of media, the management of method, and the problem of social study learning based on students� character empowerment in Al Firdaus Surakarta Elementary School. The researcher used study case qualitative method. The subjects of this research are students, teachers, and headmaster. The researcher used observation, interview, and documentation as the collecting data method. The researcher used source and technique triangulation as the data validating technique. To analyze the data, the researcher used interactive model. They are data collecting, data reducting, data serving, and concluding. The results of this research are: 1) the preparation of learning tools created in the early years of learning; 2) accuracy in selecting instructional media techniques and the use of IPS accompanied by a strengthening of the character; 3) teachers are required are always looking for new ways to adapt his teaching to the situation at hand; 4) learning patterns developed by the teachers tend to be text book oriented, consequently thus learning patterns that cause students saturation, students is not taught to think logically only concerned with understanding and memorization. Keywords: material, media, methods, learning obstacle

Mining Formative Evaluation Rules Using Web-based Learning Portfolios for Web-based Learning Systems

[[abstract]]Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an essential research issue in web-based learning, owing to the rapid growth of e-learning systems and real application in teaching scenes. The traditional summative evaluation by performing examinations or feedback forms is usually employed to evaluate the learning performance for both the traditional classroom learning and the web-based learning. However, summative evaluation only considers final learning outcomes without considering learning processes of learners. This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results of grade levels. In other words, this scheme can help teachers to assess individual learners precisely utilizing only the learning portfolios in a web-based learning environment. Additionally, teachers can devote themselves to teaching and designing courseware since they save a lot of time in evaluating learning. This idea can be beneficially applied to immediately examine the learning progress of learners, and to perform interactively control learning for elearning systems. More significantly, teachers could understand the factors influencing learning performance in a web-based learning environment according to the obtained interpretable learning performance assessment rules.

Mining Formative Evaluation Rules Using Web-based Learning Portfolios for Web-based Learning Systems

Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an essential research issue in web-based learning, owing to the rapid growth of e-learning systems and real application in teaching scenes. The traditional summative evaluation by performing examinations or feedback forms is usually employed to evaluate the learning performance for both the traditional classroom learning and the web-based learning. However, summative evaluation only considers final learning outcomes without considering learning processes of learners. This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results of grade levels. In other words, this scheme can help teachers to assess individual learners precisely utilizing only the learning portfolios i

Adaptive intelligent tutoring for teaching modern standard Arabic

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyThe aim of this PhD thesis is to develop a framework for adaptive intelligent tutoring systems (ITS) in the domain of Modern Standard Arabic language. This framework will comprise of a new approach to using a fuzzy inference mechanism and generic rules in guiding the learning process. In addition, the framework will demonstrate another contribution in which the system can be adapted to be used in the teaching of different languages. A prototype system will be developed to demonstrate these features. This system is targeted at adult English-speaking casual learners with no pre-knowledge of the Arabic language. It will consist of two parts: an ITS for learners to use and a teachers‘ tool for configuring and customising the teaching rules and artificial intelligence components among other configuration operations. The system also provides a diverse teaching-strategies‘ environment based on multiple instructional strategies. This approach is based on general rules that provide means to a reconfigurable prediction. The ITS determines the learner‘s learning characteristics using multiple fuzzy inferences. It has a reconfigurable design that can be altered by the teacher at runtime via a teacher-interface. A framework for an independent domain (i.e. pluggable-domain) for foreign language tutoring systems is introduced in this research. This approach allows the system to adapt to the teaching of a different language with little changes required. Such a feature has the advantages of reducing the time and cost required for building intelligent language tutoring systems. To evaluate the proposed system, two experiments are conducted with two versions of the software: the ITS and a cut down version with no artificial intelligence components. The learners used the ITS had shown an increase in scores between the post-test and the pre-test with learning gain of 35% compared to 25% of the learners from the cut down version