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Personalized recommendation in e-learning has attracted the interest of many researchers. How to select the proper learning objects (LOs) and provide a suitable learning path for learners is a complex task. The effectiveness of personalized recommender systems are mostly decided by the reasonable models of learners and learning resources. However, the modeling method needs further research for the learners' special natures in e-learning. Heuristic methods have achieved significant successes on personalized recommendation, but the operators of some heuristic algorithms are often fixed, which diminishes the algorithms' extendibility. In this paper, we propose a learner oriented recommendation approach based on mixed concept mapping and immune algorithm (IA). First, we build universal models for learners and LOs respectively, then apply mixed concept mapping to assimilate their attributes. Second, we model the learner oriented recommendation as a constraint satisfaction problem (CSP) which aims to minimize the penalty function of unsatisfied indexes. Last, we propose an advanced IA which takes the inherent characteristics of personalized recommendation into consideration, and we design the monomer vaccine and block vaccine to optimize the IA. Our approach is compared with other heuristic algorithms and traditional teaching method. From the experimental results, it can be concluded that the proposed approach shows high adaptability and efficiency in e-learning recommendation.

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners’ interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students’ needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students’ preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system’s efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system’s capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems.

This paper presents an intelligent recognizer of the cognitive state of an e-learner as an integral part of confidence-based e-learning (CBeL) system. It addresses the problem of providing technology-driven pedagogical support to an e-Learner to achieve the desired cognitive state of mastery which is endowed by high levels of both knowledge and confidence. As per best of our knowledge concerned, no prior work has been done in the area of CBeL. The issue is crucial in the present scenario of teaching–learning in the twenty-first century where lifelong learning is gaining increasing importance vis-a-vis traditional classroom teaching–learning. However, self-learning is an indispensable mode of lifelong learning. It is felt that e-learning systems should have the capacity to simulate the behavior a human expert to identify the gap between the learners’ cognitive state and the learning objective with the intension of guiding the self-learner take initiative to bridge the gap with appropriate action and eventually achieve his learning objective. An artificial neural network-based intelligent recognizer has been designed to identify the CBeL state of the learner on the basis of his performance in a CBeL test. This recognizer is the major agent that facilitates the implementation of the proposed CBeL system. Extensive experimentation has been carried out to ensure the performance of the recognizer. Results show ample evidence that the ANN-based intelligent recognizer is able to faithfully simulate the behavior of a human evaluator.

information and communication technologies are emerging in all segments of life, including education. Affordable digitalization and low-budget content development allow for virtually unlimited access to shared knowledge. Formal education is already more or less supported by such additional material everywhere. Methodologies are developed to support automation of content management, as well as to tailor-make material suggestions and delivery according to individual learner's preferences. Such an adaptive approach inevitably faces challenges like following individual learning habits and behaviour, maintaining an up-to-date knowledge and evaluation coordinative instances, along with credible low-budget media development keeping pace with the curricula evolution. An integrated Intelligent Agent-Based University Information System (IABUIS) is successfully implemented at the Faculty of Natural Sciences and Mathematics with the University Ss. Cyril and Methodius, in Skopje. An AeLS (Adaptive e-Learning System) within it, attempts to propagate faster individual learning curves by employing agent-based system consisted of agent-based algorithms for adaptive interaction with the consumers (students), and adaptive content/course selection and delivery of appropriate material intended for improved knowledge acquisition, thus better learning results - subject of official examination. This paper presents latest improvements in the user - AeLS interaction point.

The delivery of adaptive instructional materials to learners is a good way of achieving effectiveness in learning session. Fitting teaching material varies from one student to another based on students’ knowledge level, that is, cognitive status and ability to learn. This study proposes a model for selecting appropriate learning materials to learners. The proposed model was formulated using the K–means Clustering Algorithm and represented using Unified Modelling Language (UML). The adaptive model was simulated using the KMeans Clustering Algorithm to train and test the adaptive model using the historical data collected from a developed e – Learning system for Undergraduate students taking Introduction to FORTRAN programming Language course. The evaluation results showed that the system had precision of 0.7143, 0.6667, 1.000 and 0.2942 for learning object 1, learning object 2, learning object 3 and learning object 4 respectively. Also, the recall results were 0.6250, 0.8889, 0.6667 and 0.3864 for learning object 1, learning object 2, learning object 3 and learning object 4 respectively. The system can be used to effectively and successfully assign learning materials to learners based on their cognitive level. General Terms Data Mining, Clustering

The acquisition of knowledge along with its use facilitated and distributed by electronic means is called as E-Learning. E-learning can take the form of smaller learning objects or courses as well as modules. The physical barriers of learning are eliminated and have enhanced indigenous participation in education. E-learning also allowed flexible learning opportunities. A lot of resources are needed by an individual to gain an expertise in a particular domain. The knowledge required to gain expertise is now easily available due to the advent of E-learning. The resources on the internet are growing at a very fast rate. A secure and efficient system is needed so that users can be directed to area of his interest such that he can learn and gain insights in the domain. Development of an organized learning atmosphere will help the user to find the required information at a same location. ELearning has been identified as positive, interesting and creative way for indigenous students to learn by the literature. This paper presents Youteach an E-Learning portal which focuses on providing an accessible and user friendly interface to the students to learn any educational concepts based on varied fields. The portal will be made with the capability of generating multimedia content (video or files) which will be uploaded by distinguished professors of the college or competent users and the content could be on varied educational topics. General Terms Cloud Computing, Content Management System

Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

E-Learning is the technology that targets the community seeks learning through various means such as material, information, courses to facilitate the ease of access. Online delivery of educational instruction provides the opportunity to bring colleges and universities new energy, students, and revenues. However e-learning system has rapid development in making activities of learning online and providing a vast set of resources for the material and online assignments to complete. Although personalized e-learning systems developed and provides services they limited to focus their recommendations of the material is only about the student‟s level interest of surfing on the learning material but never considered the level of understanding of the learning material. This system developed a model that aim to recommend the assignments and material of the course to the student based on the level of understanding by analyzing the performance of the student in past. Experimental results on the proposed recommender system exhibited significant results than the traditional elearning system. This shown the impact of personalized assignment recommendations in improving the student‟s interest towards the course.

Entry to the information age and effective life in information-oriented society requires an understanding of its characteristics. One of the social institutions that will undergo extensive changes at this age is general and higher education and training institutions. The aim of this review study is to evaluate the effectiveness of e learning in learning. This paper is a review study carried out using Medline and CINAHL databases and Google search engine. The studies used include review articles and English meta-analysis of language.Of the retrieved results,38 documents including articles, books and web sites were investigated and classified. At first, the background of e learning was studied history and in the next section its effectiveness in learning was briefly examined. The overall benefits of e-learning include the promotion of learning, independence and individual satisfaction, learning at anytime, anywhere and with any background, learning without the same prerequisites, speed and process of learning due to individual needs, individual learning along with cooperative learning, saving time and costs significantly, the possibility of teaching and learning for all people, mutual teaching and learning, getting quick results in learning, learning more by using multimedia and maintaining resources and reducing environmental and audio pollution. The results of studies suggest positive effects of e-learning on learning thus it is suggested that this approach be used more in education, which requires building the required grounds.

E-learners face a large amount of fragmented learning content during e-learning. How to extract and organize this learning content is the key to achieving the established learning target, especially for non-experts. Reasonably arranging the order of the learning objects to generate a well-defined learning path can help the e-learner complete the learning target efficiently and systematically. Currently, knowledge-graph-based learning path recommendation algorithms are attracting the attention of researchers in this field. However, these methods only connect learning objects using single relationships, which cannot generate diverse learning paths to satisfy different learning needs in practice. To overcome this challenge, this paper proposes a learning path recommendation model based on a multidimensional knowledge graph framework. The main contributions of this paper are as follows. Firstly, we have designed a multidimensional knowledge graph framework that separately stores learning objects organized in several classes. Then, we have proposed six main semantic relationships between learning objects in the knowledge graph. Secondly, a learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner’s target learning object. The experiment results indicate that the proposed model can generate and recommend qualified personalized learning paths to improve the learning experiences of e-learners.

The advent and increased use of new technologies, such as innovative mulsemedia and multi-modal content distribution mechanisms, have brought new challenges and diverse opportunities for technology enhanced learning (TEL). NEWTON is a Horizon 2020 European project that revolutionizes the educational process through innovative TEL methodologies and tools, integrated in a pan-European STEM-related learning network platform. This article focuses on one of these novel TEL methodologies (i.e., mulsemedia) and presents how NEWTON enables mulsemedia- enhanced teaching and learning of STEM subjects, with a particular focus on telecommunication and networking related modules. The article also discusses the very promising results of NEWTON case studies carried out with engineering students across two different universities in Spain and Ireland, respectively. The case studies focused on analyzing the impact on the learning process of the mulsemedia-enhanced teaching in the context of telecommunication and networking modules. The main conclusion of the article is that mulsemedia-enhanced education significantly increases students' learning experience and improves their knowledge gain.

The use of e-learning for educational purposes has been the focus of researchers for a long time, because of the difficulty to determine if a tool based on this concept is able to instruct the educational content that a professor can easily teach on a classroom. In this document the available tools and teaching methods to deliver Adaptive Learning to each user are investigated. Through the development of this paper, the adaptive techniques to be implemented to the Educational Tool are proposed; how and which contents will be presented to the students and which adaptation model will be applied to that content. Thanks to the investigations on the topic, and the analysis of the performance of the students on the Object Orientation course, from the Pontificia Universidad Catolica de Valparaiso (PUCV), preliminary models for the Educational and Adaptive Tool have been defined, with promising results.

An eye-tracking study examining the benefits of a proactive human-computer-interaction has been conducted. Regarding the beneficial aspects of knowledge transfer, the data shows a clear benefit once the system has been enhanced by the so called electronic educational instance which tracks a user’s gaze and thereby infers whether or not someone is actually looking at a screen while the e-learning software is conveying its knowledge. To further show how a physiological input might be used as a form of input, current literature of smoothing pupillary response data is discussed and a preliminary tool is presented. Due to this, pupillary data can be used to indicate cognitive load levels while learning and would therefore allow a proactive system to change the e-learning program accordingly.

This paper proposes a gamification design model that can be used to design and develop gamified e-learning systems. Furthermore, a controlled and carefully designed experimental evaluation in terms of learning effectiveness of gamification is offered. The experiment was conducted with 44 participants randomly assigned to an experimental ‘gamified’ condition and a controlled ‘non-gamified’ condition. In both conditions the same learning material, to teach computer security, were used. The main difference between the two conditions was the integration of gamification in an e-learning system designed based on the proposed model. The results indicate that learning using the gamified version of the e-learning system produces better short-term and medium-term learning gain than learning using the non-gamified e-learning version. Future avenues of research are also provided.

Learning management systems (LMS) are increasingly being used in the academic field as a supplement to face-to-face learning and are intended to effectively support knowledge transfer through individually integrable e-learning modules. In practice, however, it can be observed that LMS often does not live up to these expectations. Although the learning effectiveness of LMS is discussed in the literature, it is sometimes limited to the system level. Previous studies on the learning effectiveness of LMS have dealt in particular with the usability and adaptivity of the system. However, the one-sided focus on the system seems insufficient in the context of learning effectiveness, since motivational factors at the learner level are largely ignored. This study addresses the motivational factors in the use of LMS by students and uses a prototype to determine which e-learning modules have a positive effect on learning motivation. For this purpose, an e-learning prototype is developed on an LMS with various e-learning modules, and the extent to which the individual modules have an effect on learning motivation is investigated. The results of the study show that e-learning modules which demonstrate individual learning progress are suitable for promoting the learning motivation of students when using LMS.

Peer tutoring is a unique and efficient method of teaching that has been widely investigated. Related works in the literature ranges from cross-age peer tutoring, peer tutoring for the disabled, reciprocal peer tutoring, to peer tutoring for children. One unique method that has been scarcely documented, however, is peer tutoring with the aid of the Internet. In this paper, therefore, a web framework is proposed for online peer tutoring application in a smart campus. The peer tutoring web application identifies two key target users: the tutor and the tutee. The tutors will help in teaching other students; they are responsible for accepting requests from tutees and in turn holding tutoring sessions for the tutees. They also have the responsibility of uploading important documents to the platform which are accessible to tutees. On the other hand, the tutees search for the tutors with prowess in their course of need and make a request for a tutoring session. They also have access to the materials uploaded by the tutors. The peer tutoring web application is designed in such a way that the web browser communicates with the web server by making Hyper Text Transfer Protocol (HTTP) request to the server. The proposed framework consists of the client-side and the server-side which are connected by the web browser. In essence, web-based peer tutoring application will go a long way in improving students’ academic performance in a more efficient manner.

Learners face various challenges when accessing cross-border learning services or collaborating with learners from other countries in shared learning environments. This paper shows how the eIDAS Regulation can be used for enabling seamless registration to learning services and access to the learning content, as well as facilitating transmission of the personal academic information in an electronic form from reliable sources. Several eID4U improvements to existing procedures and learning services are proposed, including three upgraded cross-border e-services based on electronic identities and trusted sources of academic information. The improvements help learning service providers improving the reliability of the identification of foreign learners, while at the same time reducing the administrative burden in dealing with such learners.

Currently, scientific researches in the field of e-learning in general and m-learning in particular are oriented towards learning platforms where learner's expectations, motivation, learning styles, habits and needs are increasingly taken into consideration. This research direction enables developers to build a model of goals, preferences and knowledge of each individual user in order to adapt the learning to his/her needs and characteristics. For this purpose, we present in this paper a comparative study of the existing learner models in adaptive m-learning systems that consider a combination of learner's characteristics and context in order to provide each learner with the most suitable learning content and format of presentation. We end our paper by suggesting a future proposal after criticizing the related works.

A comprehensive e-learning system with simulation capabilities for understanding of complex equations is proposed. Through experiment with the proposed e-learning system, it is found that the proposed system is much effective and comprehensive than the conventional e-learning system without any mathematical expressions of processing and simulation capabilities by 14.5 %, the time required for learning increased by 18.2% though. In addition, the proposed system not only helps students to understand subjects and complex equations but also can be effective to increase students’ motivation to learn.

After conducting the historical review and estab-lishing the state of the art, the authors of this paper focus on the incorporation of Project Based Learning (PBL), in an adaptive e-Learning environment, a novel and emerging perspective, which allows the application of what today constitutes one of the most effective strategies for the process of teaching learning. In PBL, each project is defined as a complex task or problem of reality, for which resolution, the student must develop research activities, planning, design, development, validation, testing, etc. For the proposal of the Hybrid Architecture of the e-Learning system model, the authors use artificial intelligence techniques, which make it possible to identify the Learning Styles (LS), with the purpose of automatically assigning the projects, according to the characteristics, interests, expectations and demands of the student, who will interact with an e-Learning environment, with a high capacity of adaptation to each individual. Finally, the conclusions and recommendations of the research work are established.

Infekce močových cest tvoří zhruba třetinu infekcí spojených se zdravotní péčí (Healthcare-associated Infections–HAI). Více než 80 % těchto infekcí vzniká v souvislosti se zavedeným močovým katetrem (Carter et al., 2014; Jindrák et al., 2014; Powers, 2016; Underwood, 2015; Yatim et al., 2016), riziko močových infekcí související se zdravotní péčí (Catheter-associated urinary tract infection – CAUTI) se zvyšuje o 5 % za každý den katetrizace (Underwood, 2015) a po třiceti dnech katetrizace je riziko téměř 100 % (Andreessen et al., 2012; Jain et al., 2015; Jindrák et al., 2014). Používáním správných preventivních postupů lze předejít 20–70 % CAUTI (AACN Practice Alert, 2016; McNeill, 2017; Podrazilová, 2016). Hodnocením rizik CAUTI byly za zásadní oblasti určeny prolongovaná katetrizace, vyšetřovací či operační výkony v oblasti urogenitálního traktu, dále opakované rozpojení drenážního systému, nekompetentní zdravotnický pracovník, nízká kvalita ošetřovatelské péče a samotný pacient (Andreessen et al., 2012; Jain et al., 2015; Jindrák et al., 2014; Jirouš, 2012; Sujijantararat et al., 2005; Underwood, 2015). Z uvedeného vyplývá, že rizika vzniku CAUTI vycházejí z více oblastí, preventivní opatření musejí být tedy řešeny komplexně. Urinary tract infections account for about one-third of Healthcare-associated Infections (HAI). More than 80% of these infections are due to an established urinary catheter (Carter et al., 2014; Jindrák et al., 2014; Powers, 2016; Underwood, 2015; Yatim et al., 2016), the risk of urinary infections related to health Catheter-associated urinary tract infection (CAUTI) increases by 5% for each day of catheterization (Underwood, 2015) and the risk of nearly 100% after thirty days of catheterization (Andreessen et al., 2012; Jain et al., 2015; Jindrák et al., 2014). By using the right preventive procedures, 20–70% CAUTI can be avoided (AACN Practice Alert, 2016; McNeill, 2017; Podrazilová, 2016). CAUTI risk assessments have identified prolonged catheterization, urogenital tract examinations, surgical procedures, reconnection of the drainage system, incompetent healthcare professional, poor quality of nursing care, and the patient himself (Andreessen et al., 2012; Jain et al., 2015; Jindrak et al., 2014; Jirous, 2012; Sujijantararat et al., 2005; Underwood, 2015). It follows that CAUTI risks arise from several areas, so preventive measures must be addressed comprehensively.

This work concerns the definition of applications, called GIFT (GeoGebra Interactive Formative Test), realized with GeoGebra and integrated into an e-learning platform, allowing the implementation of manipulative and linguistic tasks. The innovative nature of GIFT consists in keeping track of student manipulations on the platform and use that information to design accurately personalized learning paths. One of these applications allows the construction of sentences and their automatic assessment. Thus it allows to pose open questions whose answers can be constructed by the application, being a new resource between closed-ended questions, which have well-known didactic limits, and open-ended questions, which pose the problem of automatic assessment. In addition, it can be used to improve the use of literate registers by manipulating digital tiles that are appropriately chosen, constructed and made available. Finally, they can be integrated into tasks aimed at constructing argumentative competency of students. We present preliminary results on an experimentation of GIFT in a case study engaging 10 degree High School students.

The adaptive e-learning system (AE-LS) research has long focused on the learner model and learning activities to personalize the learner’s experience. However, there are many unresolved issues that make it difficult for trainee teachers to obtain appropriate information about the learner's behavior. The evolution of the Learning Analytics (LA) offers new possibilities to solve problems of AE-LS. In this paper, we proposed a Business intelligence framework for AE-LS to monitor and manage the performance of the learner more effectively. The suggested architecture of the ALS proposes a data warehouse model that responds to these problems. It defines specifics measures and dimensions, which helps teachers and educational administrators to evaluate and analyze the learner’s activities. By analyzing these interactions, the adaptive e-learning analytic system (AE-LAS) has the potential to provide a predictive view of upcoming challenges. These predictions are used to evaluate the adaptation of the content presentation and improve the performance of the learning process.

The modern digital world requires its users to learn continuously in order to enhance their knowledge in the working environment and the academic sector. This kind of learning is significantly facilitated by the E-Learning platform, which is better than the traditional methods. As E-Learning offers benefits like time and space independence, many learners have made it their choice. However, since an abundant of E-Learning courses are available on websites, learners are confused as to which is the right one to choose. This paper proposes an Automated Intelligent Learning (AIL) methodology which covers the entire Teaching-Learning Process (TLP) to overcome this issue. It enables the selection of suitable topics and framing an appropriate course syllabus and assessment questions for the users. In it, the learner satisfies topic selection based on Bloom's taxonomy. This enables high-quality knowledge outcomes in the learner. The subject curriculum is framed by using Hierarchical clustering techniques. This helps the user to fix suitable topics and conveniently generate questions using machine learning techniques. The proposed methodology was evaluated by carrying out post and pre-assessment tests on undergraduate students from computer science courses. The performance analysis of the proposed methodology was compared with that of the existing methodology. It was observed that the proposed methodology is effective in applying the topic selection hierarchical method to make a perfect syllabus for the course, and assessment questions. Besides, it was found to enable the learner to learn without any confusion or distraction.

Today, there are many educational institutions and organizations around the world, especially the universities have adopted the e-learning and learning management system concepts because they want to enhance and support their educational process since the number of students who would like to attend universities and educational institutions is increasing. This paper has many objectives, the first one is comparing between different types of most popular learning management system (LMS) software such as Moodle and Blackboard based on their uniqueness features. The second objective is presenting the learning management systems and their benefits in e-learning. Finally, this research paper presents a proposed model, which consists of six independent variables (application and integration, communication, assessment, content, cost, and security), and one dependent variable which is e-learning success. A questionnaire has been developed and distributed to 450 respondents, and then data was collected from 418 valid questionnaires. The result showed that there is a statistically significant impact of learning management system major characteristics on e-learning success.

Internet and Communication Technology (ICT) is being applied extensively in education to allow students to obtain information at anytime from anywhere. Social media is a new approach that is used to enhance and improve the delivery of education. Facebook groups and YouTube channels are the most operated networks that manage to deliver information. In this paper, we will study the impact of applying Facebook groups and YouTube videos on students’ academic achievements in computer programming labs especially in object-oriented programming 2 (OOP2) lab at the Hashemite University. The practical programming lab plays an important role in understanding the theoretical programming concepts, for this reason programming lab is chosen as a case study. The proposed methodology embeds the social media networks as new major dimension in the teaching process of OOP2 Lab side by side with traditional lectures and e-learning tool such as Moodle. In this research: three surveys are utilized respectively to inspect the reasons of the weakness in OOP2 lab, evaluate the course learning outcomes (CLOs) by students before and after applying the proposed methodology and to investigate the opinions of students toward using social media networks within learning process. The results showed that operating the social media sites used by students on a daily basis creates a friendly and close educational environment as well as enhancing the academic results of students.

The use of the project-based learning approach is one of the emerging trends in education and Adaptive e-Learning platforms. One of the main challenges in this line of research is to identify the learning style of students, whose results are considered in this work, for the assignment of course projects which best suit the characteristics of each student in particular as they incorporate different types of learning strategies and objects in order to contribute to facilitate and simplify the teaching/learning process of the adaptive e-Learning platform, which uses the project-based learning approach. In this work, after carrying out a review of the literature for the theoretical foundation and establishment of the state of Art, a line module of automatic recognition of learning styles is proposed, which uses the information of the interaction of the student with the system and the one that is based on Neural Networks and Fuzzy Logic, whose results are considered by the module of selection and assignment of projects that uses the Case Base Reasoning, later carrying out the tests and the analysis of the results obtained.

Learning programming isn’t only important for programmers. Studies show that learning computation is useful for logical reasoning development, which leads to an easier learning in mathematical fundamentals and language skills. Because of this, several computation platforms have emerged for children and teenagers. These tools can be used by teachers from several fields of knowledge to stimulate their student’s learning. However, these platforms don’t include topics from the Brazilian National Curricular Joint Base (BNCC), which make them difficult to use for this specific purpose. Looking to the relevance of creating or remodeling a platform for this goal, the first step is to survey for necessary features. The LORI method was used by students from elementary public school, using Code and Blockly platforms. This paper describes which and how features were studied during the exploration of two platforms, while analysing the audience. Computer education platforms directed to young people can highlight some important characteristics: daily use and cultural aspects of students in order to approach the platform, giving feedback with enough information for them to understand their error and the possibility for the teacher to visualize and measure in what aspect and how often the students make a mistake.

With the advancement of today's ubiquitous technology, and due to the increasing number of technologies supported by the Internet, a variety of Online Learning Platforms have rapidly grown as modern learning methods. This fast-emerging learning option interests researchers to study and investigate the main features and functionality of the most popular Online Learning Platforms. This paper surveys the state-of-the-art Online Learning Platforms that aim to teach computer programming, in terms of principles, design, and implementations. In addition, the paper investigates the feasibility of incorporating human-oriented Situation-Awareness as the driving factor to facilitate the delivery of improved user learning experiences.

We propose an Agri-InfoScience platform which addresses the need for skill transfer in response to various food problems associated with global population growth. The platform consists of various tools and methods for collection, analysis and reconstruction of information. The agricultural skill learning support system is the central tool of the platforms. This system serves to address the issues pertaining to the acquisition of "master craftsmanship." We conducted an experiment using kiwi pruning operations as an example of a skill that takes a few years to acquire to verify the effectiveness of the learning support system.

The arise of E-learning systems has led to an anytime-anywhere-learning environment for everyone by providing various online courses and tests. However, due to the lack of teacher-student interaction, such ubiquitous learning is generally not as effective as offline classes. In traditional offline courses, teachers facilitate real-time interaction to teach students in accordance with personal aptitude from students' feedback in classes. Without the interruption of instructors, it is difficult for users to be aware of personal unknowns. In this paper, we address an important issue on the exploration of 'user unknowns' from an interactive question-answering process in E-learning systems. A novel interactive learning system, called CagMab, is devised to interactively recommend questions with a round-by-round strategy, which contributes to applications such as a conversational bot for self-evaluation. The flow enables users to discover their weakness and further helps them to progress. In fact, despite its importance, discovering personal unknowns remains a challenging problem in E-learning systems. Even though formulating the problem with the multi-armed bandit framework provides a solution, it often leads to suboptimal results for interactive unknowns recommendation as it simply relies on the contextual features of answered questions. Note that each question is associated with concepts and similar concepts are likely to be linked manually or systematically, which naturally forms the concept graphs. Mining the rich relationships among users, questions and concepts could be potentially helpful in providing better unknowns recommendation. To this end, in this paper, we develop a novel interactive learning framework by borrowing strengths from concept-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in an interactive fashion for the recommendation in E-learning systems.

Digital technologies challenge maths education as they provide the calculations that used to be the core of maths teaching. Although calculation is still important to learn, the core of maths-education is increasingly the understanding and application of concepts and language to authentic situations in order for machines to perform adequate calculations. While many e-learning applications support learning skills and calculations, maths teachers have difficulties finding digital resources aimed at maths as concepts and language. Accordingly, teachers experience a dilemma when their school managers expect them to digitalise their teaching. This paper is based on results from an action research project running from 2012 to 2016 following a cohort from first to third grade in a Copenhagen suburban school. The school aimed to implement iPads in everyday practice and provide teachers with competence building. Accordingly, all teachers and students were given an iPad. While the project had a broader scope, the present paper focuses on the teachers’ and students’ motivation and learning in relation to the iPad and maths in light of the above-described dilemma. As action researchers, the researchers acted as sparring partners for the teachers. The school provided resources for teacher team meetings on a weekly basis. At the meetings, the teachers became familiar with using iPads in learning settings, brainstormed ideas and co-designed experiments for upcoming sessions. The meetings provided a framework for iteratively sharing knowledge and improving practice. The data were collected using anthropological methods: observations, interviews, summaries, thick description, video recordings and collection of materials produced during the project. The data were analysed using condensation and annotation methods. The present paper identifies teacher practices and use of digital technology that amplifies the teachers’ dilemma of using technology in maths teaching. Equally, the paper provides examples of practices developed during the project that seem to overcome the dilemma. Digital technologies challenge maths education as they provide the calculations that used to be the core of maths teaching. Although calculation is still important to learn, the core of maths-education is increasingly the understanding and application of concepts and language to authentic situations in order for machines to perform adequate calculations. While many e-learning applications support learning skills and calculations, maths teachers have difficulties finding digital resources aimed at maths as concepts and language. Accordingly, teachers experience a dilemma when their school managers expect them to digitalise their teaching. This paper is based on results from an action research project running from 2012 to 2016 following a cohort from first to third grade in a Copenhagen suburban school. The school aimed to implement iPads in everyday practice and provide teachers with competence building. Accordingly, all teachers and students were given an iPad. While the project had a broader scope, the present paper focuses on the teachers’ and students’ motivation and learning in relation to the iPad and maths in light of the above-described dilemma. As action researchers, the researchers acted as sparring partners for the teachers. The school provided resources for teacher team meetings on a weekly basis. At the meetings, the teachers became familiar with using iPads in learning settings, brainstormed ideas and co-designed experiments for upcoming sessions. The meetings provided a framework for iteratively sharing knowledge and improving practice. The data were collected using anthropological methods: observations, interviews, summaries, thick description, video recordings and collection of materials produced during the project. The data were analysed using condensation and annotation methods. The present paper identifies teacher practices and use of digital technology that amplifies the teachers’ dilemma of using technology in maths teaching. Equally, the paper provides examples of practices developed during the project that seem to overcome the dilemma.

There has been a huge growth of data in education due to integration of ICT in educational and the evolution of E-learning, to bring about technology enabled learning. This huge data in education has subjected learners to information overload in education, without a well-known mechanism to enable learners select their relevant learning content from the huge educational data. This has subjected learners, in most of the times, to take a lot of time accessing data which could not be relevant to their particular learning needs. This research therefore creates a model which will recommend relevant learning content to a learner, from the huge available data. In designing the model we employed context awareness recommender approach. The model will collect the learner’s context. It will then apply type-1 fuzzy logic data mining method to recommend relevant learning content to the learner; based on the learner’s assesment score and the time taken to complete the assesment test. The model was tested using a primary designed dataset stored in the database. The model gave recommendations that were commensurate with the assesment test marks and the time taken to complete the exams. Thus the higher the assessment score, the higher the crisp output; while the higher the time taken to complete the exams, the lower the crisp output.

A method is proposed for the distribution of exam-ination problems over multiple course events, to maximize variation and minimize predictability, primarily targeting mathematics education, but generically applicable to all types of examination papers where problems can be quantified along at least two independent dimensions. The suggested method is based on experience gained from the development and implementation of an automatic system for the generation of examination papers in a discrete mathematics course at Uppsala University in Sweden.

IFIN-HH and the Faculty of Physics will always be concerned about the modern educational system in nuclear training and beyond. Students often need additional training to acquire knowledge and skills specific to research requirements, especially in the nuclear field. It is essential for students to be able to use the new education and training systems. Within the Nuclear Training Center (CPSDN), the improvement of training programs in the nuclear field takes place permanently. A few years ago, the CPSDN launched a modernization process that includes, besides the modern laboratory for practical training, an e-Learning platform for monitoring the nuclear training process. By developing a modern electronic system, important steps will be taken to computerize and virtualize the learning process.

Evaluation in serious games is an important aspect; it aims to evaluate the good transmission of pedagogical objectives, the performance of student in relation to these objectives defined in the pedagogical scenario, the content of the course and the predefined criteria. However, the effectiveness of learning is under-studied due to the complexity involved to gamify the assessment concept, particularly when it comes to intangible measures related to the progression of learning outcomes, which is among the most important aspects of evaluation in serious games. This paper reviews the literature regarding assessment due to their importance in the learning process with a detailed assessment plan applied on serious game. Then, it presents a framework used to facilitate the assessment design integrated in serious games. Finally, a significant example of how the proposed framework proved successful with corresponding results will conclude the paper.

Towards the last decade, digital education has become a burning issue in the related scientific literature and involves the production of Intelligent Tutoring Systems (ITSs). ITSs are adaptive educational applications that enrich the tutoring and learning processes with "intelligence" by divulging the abilities and weaknesses of each student, in order to provide him/her with a personalized learning experience. A crucial factor of adaptive learning systems is testing, and indeed adaptive testing. It is a challenge to create an adaptive test that includes the most suitable exercise/question/activity of a large pool of test items for a particular learner taking into consideration her/ his particular learning characteristics, needs and ability. In this paper, a framework for creating automated adaptive tests using multiple-criteria decision analysis and the weighted sum model is presented. The presented framework takes into consideration multiple students' criteria along with the types of exercises and the desirable learning objective. The aforementioned assessment framework was incorporated in two adaptive e-learning systems and was fully evaluated. The evaluation results are very encouraging.

The traditionally taught face-to-face course in statistics for undergraduate non-statistics students suffered from several problems: Time and location constraints, different preknowledge in mathematics, lack of motivation, no use of statistical software etc. Therefore, we decided to develop an e-learning course in statistics to reduce the impact of these problems. This paper describes the different components developed for the e-learning course as well as the teaching framework. Preliminary results from the first in-class usage of the e-learning course indicate positive qualitative effects. In addition, we discuss which of the e-learning components the students mainly preferred to use. Furthermore, we show that the exam results from the e-learning course are significantly better than the corresponding results from the face-to-face course in this first in-class usage.

This paper performed literature analysis for evolution of learning environment focusing on a new learning paradigm which is known as ubiquitous learning environment. This paper also intend at providing specific information about ubiquitous learning concepts and the criteria that needed in the future research. It begins by briefly describing the evolution of learning environment with the comparison for each of the learning paradigms. Finally, the definition of ubiquitous learning and the comparison of characteristics in ubiquitous learning paradigm are also compared and discussed to further enhance the understanding of ubiquitous learning concept. As a conclusion these paper providing a basis foundation for novice researcher that interested in exploring ubiquitous learning environment area.

Nowadays, there is a substantial growth in the use of e-learning platforms in higher education around the world. Currently, the use of e-learning technology has become necessity within the Higher Education Institutions (HEIs) in Myanmar. Learning Management System (LMS) emerged as a tool in teaching and learning environment. LMS such as Modular Object Oriented term Developmental Learning Environment (Moodle) is a course management system through the Internet. This paper aims to investigate the undergraduate students’ and teachers’ perceptions on LMS (Moodle) usage in University of Computer Studies, Mandalay (UCSM). Quantitative data are collected through a questionnaire that is answered by undergraduate students and teachers in UCSM. The collected data was analyzed using the SPSS (Statistical Package for the Social Sciences). A total of 318 respondents answered the questionnaire. The results of this study indicate that Moodle is a real support for teaching and assessment evoluation in UCSM. However, there is no significant effect on improving students’ semester grades and the interaction between students and teachers before and after using Moodle.

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, June, 2018 In this paper, we present our attempt to investigate student’s attitude towards a technology-enhanced learning. We are interested in exploring how learners react to changes in training methodology due to the transition to e-learning. This investigation employs survey research to assess the extent to which students are ready for changes in the training methodology regarding e-forms. Data have been collected over 200 students across different bachelor programs at New Bulgarian University in both full-time and distance forms of studies. The analysis is based on our experience in delivering courses via Moodle for both distance and full-time education. Our results concerning the students’ readiness for e-learning are presented in this paper. Association for the Development of the Information Society, Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Plovdiv University "Paisii Hilendarski"

To reach an adaptive eLearning course, it is crucial to control and monitor the student behaviour dynamically to implicitly diagnose the student learning style. Eye tracing can serve that purpose by investigate the gaze data behaviour to the learning content. In this study, we conduct an eye tracking experiment to analyse the student pattern of behaviour to output his learning style as an aspect of personalisation in an eLearning course. We use the electroencephalography EEG Epoc that reflects users emotions to improve our result with more accurate data. Our objective is to test the hypothesis whether the verbal and visual learning Styles reflect actual preferences according to Felder and Silverman Learning Style Model in an eLearning environment. Another objective is to use the outcome presented in this experiment as the starting point for further exhaustive experiments. In this paper, we present the actual state of our experiment, conclusions, and plans for future development.

Introducing TV conference systems into classes of distance learning in English education together with foreign countries, we have two classes of cyber seminars at Waseda University, i.e., the class of "Cross-Cultural Distance Learning" (CCDL), and that of "World Englishes and Miscommunications" (WEM)[1][2]. To realize the stable and comfortable learning environment, the influence of the quality of service (QoS) of the Internet on the classes is investigated. First, we measure the QoS such as the round-trip delay time (RTT), and the throughput (TP) of the communication lines between Waseda University and the interchange foreign universities. Next, the quality of experiment (QoE) as the subjective evaluation by the student questionnaires is measured. Finally, the relationship between the QoS and the QoE is analyzed, and we clarify the conditions to keep the service level agreement for the class management.

E-learning, an online learning mode, completely transforms the traditional learning and teaching modes and brings learners new learning experience. However, it also has its challenges in leaning practice; especially in Big Data era, the learning pattern meets new challenges. The article, in the background of Big Data, introduces the Production-oriented Approach into the study of foreign language E-learning modes. It first designs the output goal according to the analyzed data, and the output goal is not only the aim, but also the driving force of foreign language learning. Then through collecting and analyzing the learning behaviors, the E-learning system provides the according input material of the foreign language. Driven by the inner motivation, learners will study the foreign language effectively with the help of input material and advanced tools or systems provided by Big Data. Besides the presenting and learning strategies, a successful E-learning mode also needs the collaboration of all its operating facilitators in Big Data era.

This paper describes a new method for helping students improve their ability to develop proofs, a skill necessary for comprehending and appreciating the foundational topics of computer science. Our method transforms ordinary pen-and-paper homework problems into a puzzle-like game, where students connect dots to justify assertions, in a quest to reach a desired goal. We have implemented a software tutoring system using this method, for students to use at home as an optional study aid. Potentially, our system could one day become a full replacement for traditional hand-written homework, which has the additional benefit for course instructors of automating the grading of student work. Our system is also easy to adapt to any class that requires students to write proofs, and it is easy for instructors to create new problems to use with this system. This stands in contrast to many other educational tools for teaching proofs, which are limited to specific topic domains. We have demonstrated the versatility of our system by testing it in two computer science classes at a large public university. One was a Sophomore-level discrete mathematics course where the students were learning first-order prepositional logic, and the other was a Junior-level algorithms course where students were being first exposed to the concept of NP-completeness. Students from our experiments reported that they would like our system to be used in more of their classes.

North Central Province is the largest province in Sri Lanka. But gaining poor results for Mathematics in General Certificate in ordinary level (G.C.E. - O/L) is an intense problem in the province. (Table 01) Many reports including reports from the Department of Examination national Evaluation Testing Services show that more than fifty percent of students have obtained poor (W) grade for the mathematics subject in the G.C.E. - O/L Examination. Therefore, it is conducted several surveys to find the root courses for this situation and suggested a remedy to overcome the situation. From these surveys, it is revealed that poor knowledge of primary level mathematics concepts is one of leading factor for this situation. So, it is suggested an Agent based E learning system as a remedy. In any E learning system, taking psychometric measurements and data clustering are very important. In this paper, it is shown the new Item response Theory model that suit for E learning and clustering probability values that calculated from new IRT model using k means algorithm.

The e-learning is a recent development that has emerged in the educational system due to the growth of the information technology. The common challenges involved in The e-learning platform include the collection and annotation of the learning materials, organization of the knowledge in a useful way, the retrieval and discovery of the useful learning materials from the knowledge space in a more significant way, and the delivery of the adaptive and personalized learning materials. In order to handle these challenges, the proposed system is developed using five different steps of knowledge input such as the annotation of the learning materials, creation of knowledge space, indexing of learning materials using the multi-dimensional knowledge and XML structure to generate a knowledge grid and the retrieval of learning materials performed by matching the user query with the indexed database and ontology. The process is carried out in two modules such as the server module and client module. The proposed approach is evaluated using various parameters such as the precision, recall and F-measure. Comprehensive results are achieved by varying the keywords, number of documents and the K-size. The proposed approach has yielded excellent results by obtaining the higher evaluation metric, together with an average precision of 0.81, average recall of 1 and average F-measure of 0.86 for K = 2.

In this paper we present Community Assessment (COMAS), a trust-based assessment service that helps compute group opinion from the perspective of a specific community member. We apply COMAS in the context of communities of learners, and we compute the group opinion from the perspective of the teacher. Specifically, our model relies on \emph{teacher assessments}, aggregations of \emph{student assessments} and \emph{trust measures} derived from student assessments to suggest marks to assignments that have not been assessed by the teacher. The proposed model intends to support intelligent online learning applications by 1) encouraging students to assess one another, and 2) benefiting from students' assessments. We believe the task of assessing massive numbers of students is of special interest to online learning communities, such as Massive Open Online Courses (MOOCs). Experimental results were conducted on a real classroom datasets as well as simulated data that considers different social network topologies (where we say students assess some assignments of socially connected students). Results show that our method 1) is sound, i.e. the error of the suggested assessments decreases for increasing numbers of teacher assessments; and 2) scales for large numbers of students. 2015 Elsevier Ltd. All rights reserved, This work is supported by the CollectiveMind project (Spanish Ministry of Economy and Competitiveness, under grant number TEC2013-49430-EXP), the MILESS project (Spanish Ministry of Economy and Competitiveness, under grant num- ber TIN2013-45039-P) and the PRAISE project (funded by the European Commission, under grant number 388770)