Bogdan Logofatu

Support system for e-Learning environment based on learning activities and processes

In e-Learning domain there has been observed an important effort towards designing and integrating support system software. These software systems may be fully integrated or run as services along e-Learning platforms and have as main goal increasing the effectiveness of the e-Learning processes. This paper proposes a novel structure of a support system for e-Learning infrastructure that is based on data representing learners activities and processes. Data relevance is based on well discipline structuring based on concept maps. Markov chain modeling and classification is used as main intelligent procedure for data analysis.

Intelligent Educational System Based on Personal Agents

This paper presents an intelligent educational system that uses personal agents for providing personalization and adaptation of e-Learning content to learners. More specifically, we focus on a mechanism for guiding the learner through e-Learning material such so as to maximize the possibility to reach specific goals. The mechanism is designed and implemented using personal agents. The guidance is performed in real time and takes into consideration the actions that were performed by learner himself and also by other learners. An inference engine has been designed to decide if a resource (quiz, definition, chapter, etc.) will be proposed to the learner for study. The e-Learning system is organized as a multi-agent system composed of two kinds of agents: personal learner agent and recommender agent.

Employing Bayes Classifier for Improving Learner's Proficiency

Improving learners proficiency is a continuous issue in e-Learning systems. This paper presents a solution for advising the learner regarding the resources he should access and study in order to obtain a required proficiency level. The solution uses a Naive Bayes as classification algorithm with input data represented by learners performed activities. The performed activities are logged and used as training and testing data. The obtained classifier is used for new students and provides them with necessary information regarding what resources need to access and study.

Magnetic field sensor with linear response

Abstract In this work, an InAs classical Hall effect based magnetic field sensor with linear response up to 1.00 T at room temperature is reported. InAs used is homogeneous polycrystalline n-type material. A supplementary simulation circuit and a suitable soft are used to insure a linear dependence of the Hall voltage on the magnetic induction. A ‘butterfly’ form for the sensor is used to increase the magnitude of the Hall signal. The influence of the magnetoresistive effect and irradiation with thermal neutrons on the response of the sensor is also investigated.

Adaptation and Intelligent Retrieval of Learning Content to Enhance On-line Courses

Enhancing on-line courses is one of the most important issues of current e-Learning platforms. These platforms try to upgrade from simple environments where learners can download documents and take tests to complex environments where the learning content is adapted and intelligently retrieved. This paper presents a methodology for achieving such goals in an attempt to enhance on-line courses. The methodology makes intensive use of well structured content, embedded logging mechanism for learner’s performed activities and usage of state-of-the-art algorithms for proper knowledge acquisition.

Design of a Social Intelligent Learner for e-Learning Environments

One of the most important things a learner misses within an e-Learning environment is the social aspect. This paper proposes a design for a social intelligent learner (SIL) that will act as an agent on behalf of the actual learner. The overall system works as a community support system designed for e-Learning environments. The actual learners benefit by using the obtained computer mediated communication system that uses extensively the performed activities in driving interpersonal communication.

Embedding Knowledge Management into Business Logic of E-learning Platform for Obtaining Adaptivity

Obtaining adaptivity is one of the main concerns in current e-Learning development. This chapter proposes a methodology for obtaining adaptivity by embedding knowledge management into the business logic of the e-Learning platform. Naive Bayes classifier is used as machine learning algorithm for obtaining the resources that need to be further accessed by learners. The analysis is accomplished on a discipline that is well structured according to a concept map.

A Structure for Management of Requirements Set for e-Learning Applications

Extracting and managing requirements is one of the most important tasks in creating a reliable software product. This step of the overall software engineering process becomes even more critical when the development process is to become a global one. This paper presents an approach for passing from ad hoc requirements management to systematic requirements management. This step is often needed due to the increasing scale of software system and increasing globalization. The study is presented for an e-Learning platform that has been developed and reached a certain maturity level such that these kind of activities are needed. The proposed solution in the paper allows centralization of requests among involved parties (developers and beneficiaries). It is proposed a custom structure for requests that has at its basis the very specificity of developed application. The structuring of requirements is customized for platform’s functionality such that the process which determines what functionalities may be outsourced is improved.

Knowledge evaluation procedure based on concept maps

Evaluation of accumulated knowledge level for a student is a critical aspect in any learning program and therefore in an e-Learning system. Proper estimation of accumulated knowledge may bring valuable information regarding the student and the effort that he needs to have in order to achieve certain goals. On the other hand, proper estimation can bring very useful information about e-Learning system efficiency. In this context this paper presents a procedure of estimating studentpsilas accumulated knowledge based on concept maps. The procedure computes the percentage of covered concepts for a discipline that is partitioned in chapters.

Personalized Content Delivery by Usage of Concept Maps and Naïve Bayes Classifier

This paper presents a personalized delivery system aimed at directing the learner to the next resources that need to be accessed in order to obtain best proficiency. The analysis is performed at chapter level. A concept map is has been created for a chapter and than concepts were classified using Naive Bayes classifier. The system recommends concepts that need further study which mean either resource access and study or more self-testing.