Improved algorithm for tag-based collaborative filtering

Critical aspect in the modern e-learning systems is selecting the most adequate learning materials based on learners’ requirements, needs and knowledge goals. It is especially important because of information overload. In addition, e-learning systems should deliver learning materials to learners in the format adequate to their learning style. On the other hand, it is common practice to use tags in order to filter the most useful learning materials because they allow learners to mark or highlight some learning materials with their own tags. In that way learners contribute to organizing and retrieving useful learning materials. Our previous research was focused on tag-based collaborative filtering and learning style determination in order to suggest useful learning material in adequate format. In this paper, we propose a new tag-based collaborative algorithm that takes in consideration the factors that affect the tag-based collaborative filtering in order to develop more efficient and accurate algorithm, and suggest the learning materials based on posted tags rating and students rating. The developed system was implemented at the Faculty of Law – Bitola, and the evaluation results are shown in this paper

Mobile audience response system as a support tool in education

Audience response systems (ARS) allow participants at a meeting or in a classroom to respond to questions, thus increasing the attention of the attendees. These systems are suitable for events with a number of participants where decision-making or assessment must be conducted quickly. ARS can also be used in large classes to increase the level of student engagement and to provide prompt feedback. In this context, we have decided to develop an audience response system that can be used in the educational process. The system contains two parts: server application designed for the teachers and client application designed for the students. Both, the server and the client application have been developed with Java. The first one can be installed on the teachers PC or laptop, the second one on students’ mobile phones. The system support two possible answer's formats: simple text and image. The developed system was tested at the Faculty of Law – Bitola, and the evaluation results are shown in this paper

Adaptive system for providing recommendations based on tags in e-learning

The development of an increasing number of systems for e-learning used as part of the educational process from one site, and the growing number of users and data from the other site, are just some of the reasons that actualized the need of personalization of the e-learning systems based on the users’ needs, their habits, learning styles, their prior knowledge and so on. The modern systems for e-learning tend to develop techniques for increasing their intelligence and adaptability in order to increase their efficiency and productivity. The effectiveness of e-learning, in particular, is based on their ability to adapt based on the users’ needs and to display the most adequate content for users based on their educational goals and their learning styles. On the other hand, searching for useful learning context in a large dataset without some techniques or tools for content filtering and recommendations is almost impossible and leads to inefficient learning process. Therefore, there is a practical need for personalized systems for e-learning or to adapt the system to the users’ needs, their habits and learning styles. The need for personalization of the e-learning systems occurs primarily because of the users’ differences - they have different learning styles, different prior knowledge and goals and so on. Intelligent learning systems represent improvement and updating of computer learning. Their basically purpose is to present the most adequate materials that is adapted to the users’ needs. They use a model of individual characteristics of the user (goals, preferences, knowledge) in order to adjust their operations. The user model can be created by examining, monitoring of the user interactions or by requesting answers from the users. Intelligent web-based learning systems are focused on improving the quality of services and for the precise and appropriate selection of content. In addition, they deliver contents to users that are most appropriate and meet their needs and users do not search through a large archive of learning materials, but through a limited number of materials selected based to their requirements, age, experience, previous content and needs. Moreover, the data is delivered in a format acceptable to the users’ learning style and users got recommendations for useful learning materials. Additional significant feature of intelligent systems for e-learning is the ability to adapt and change their functions and operations based on the reaction of users

Additional Parameters that Affect the Tag-based Collaborative Filtering

Selecting the most useful learning resources is very important aspect in the modern e-learning systems as well as distributing learning content in adequate format. That is e-learning systems need to have ability to determine student needs and their most adequate learning style. Our previous research was focused on tag-based collaborative filtering and learning style determination in order to suggest useful learning material in adequate format and we have proposed an algorithm for tag-based collaborative filtering. Also, we focused on some parameters that are important for tag-based collaborative filtering too. In this paper we are focused on additional parameters that affect the process of tag-based collaborative filtering. In that context, we consider students previous knowledge level, tag's ratings and author's ratings of materials as factors that have impact on the tab-based collaborative filtering

E-health monitoring system

Monitoring of patients’ vital parameters very often is limited to hospitals or other health care centres, which makes the process time consuming and expensive. Rapid advancement in information and communication technologies offers great opportunities for development of remote monitoring systems, which on one hand, will reduce costs and travel time, and on the other will increase health service efficiency and user satisfaction. The goal of this paper is to propose an e-health system that allows doctors to closely monitor patients’ vital parameters, no matter where they are located. Integration of web, mobile and smart TV technology, will provide greater accessibility of patients’ data, and will improve patient – doctor communications

Tag-based Collaborative Filtering in E-learning Systems

The goal of this paper is to propose a system for giving recommendation in e-learning by using tagging technique and collaborative filtering. We plan to use PHP programming language for developing the system, and MySql database for storing information about users, learning items, tag lists and etc. Proposed system proposes and student grouping in virtual learning group, based on their knowledge and interests. Also, students can post tags for the learning materials. Collaborative filtering is using to present more relevant information to students based on how other students from the same virtual group have acted. In that content, tags posted from the students are very useful for the system to figure out which learning materials should be adequate for other students from the same virtual group. Starting point of this paper is that students with similar interests might post similar tags and similar resources might have similar tags, especially when they belong to the same virtual group. Students participating in virtual groups will produce more precise suggestion recommendation for the students, based on tags on other students that belong to the same virtual group

Transition of educational paradigms

This work proposes a complete online learning environment which combines several aspects: categorization of students based on their abilities, learning styles and preferences, adaptability of the presented contents, recommendation of appropriate learning materials. Besides offering series of conventional services, abundancy of information available, message delivery, to effectively meet the needs, preferences, and different knowledge backgrounds the system offers the possibility of selecting the most adequate learning materials: text, images, video, audio and links to external websites. Tag-based collaborative filtering is used for recommendation of learning materials to the student. Within the process of finding appropriate materials to be recommended to the student, the system determines the degree of similarity of the tags most often used by the student with the words in the title, abstract and keywords of the papers. Categorization of students is based on the learning style (VART model) and all activities related to the interaction with the system, such as visited pages, teaching materials and external publications, tags and notes entered in the process of learning, ratings set to the teaching materials, etc. Important aspect of this learning system is generating and recommending adequate teaching materials as well as appropriate tags and ratings. The list of recommended contents is generated by finding similar profiles and learning materials. Information retrieval algorithms are used to determine the similarity between student profiles and teaching materials used. The advantage of this learning system in comparison with the traditional ones lies in the possibility to use knowledge about the domain and the teaching strategies to support individualized learning