Personalizing learning programs with X-Learn, an XML-based, “user-device” adaptive multi-agent system

In this paper we present X-Learn, an XML-based, multi-agent system for supporting “user-device” adaptive e-learning, i.e. e-learning activities which take into account the profile, past behaviour, preferences and needs of users, as well as the characteristics of the devices they use for these activities. X-Learn is characterized by the following features: (i) it is highly subjective, since it handles quite a rich and detailed user profile that plays a key role during the learning activities; (ii) it is dynamic and flexible, i.e. it is capable of reacting to variations in user exigencies and objectives; (iii) it is device-adaptive, since it decides the learning objects to present to the user on the basis of the device he is currently using; (iv) it is generic, i.e. it is capable of operating in a large variety of learning contexts; (v) it is XML based, since it exploits many facilities of XML technology for handling and exchanging information related to e-learning activities. The paper also reports various experimental results, as well as a comparison between X-Learn and other related e-learning management systems already presented in the literature.     

A study on e-learning take-up intention from an innovation adoption perspective: A case in China

This research aims to examine, from an innovation adoption perspective, Chinese students’ intention of taking up e-learning degrees. A survey of Chinese students was conducted to reveal their perceptions concerning innovation attributes relevant to e-learning and their intentions of taking e-learning programmes provided by UK universities. Given the rapid development in e-learning and its potential impact on how learning takes place, this research argues that e-learning take-up represents adoption of an innovation in educational services, rather than just an IT technology. It therefore examined e-learning adoption using Rogers’s relational model of perceived innovation attributes. Rogers’s model was adapted to the e-learning context. A questionnaire survey was developed to collect data from a sample of Chinese students (n = 215). Prior to final analysis the dimensionality and validity of the implementation of Rogers relational model was assessed. Findings suggested that only perceived compatibility and trialability have significant influence on e-learning adoption intention.     

Learning style as a factor which affects the quality of e-learning

With the aid of the Internet, many organizations and schools have adopted the idea of applying the e-learning system, which is considered as one of the most important services provided by the Internet. The purpose of this paper is to investigate the factors affecting the acceptance and use of e-learning system. There are a number of implicit and explicit frameworks designed to inform e-learning practice. Some of them suggest key components that influence the quality of the e-learning experience: technology, pedagogy, organizational context and creativity. Instructor feedback and student learning styles, significantly affect the perceived learning outcomes of e-learning students. Namely, quality of education will significantly be enhanced if instructors modify their teaching styles to accommodate the learning styles of all students in their classes. When the teacher creates the lesson plan, it is desirable that he or she puts as many activities as possible which will reflect different learning styles. Whereas, students have diverse backgrounds, abilities, and knowledge bases, teachers who are able to use various instructional strategies have been shown to be more effective than those who just use single strategies.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

An empirical study on m-learning adaptation: Learning performance and learning contexts

M-learning enables students to learn everywhere and at any time. But mobility also brings a new challenge. Students may now be constantly moving and the context from which they learn has to be adjusted dynamically. Therefore adaptation is becoming increasingly important when it comes to m-learning. This paper presents an empirical study to assess the learning performance and attitude of graduate students when they use an adaptive mobile system that tailors learning contents to their skills, their device and current context. Results suggest that mobile adaptation had a limited impact in learning performance of practical skills when compared to an e-learning approach. Information about the context of use of the mobile system was also collected and compared with traditional computer accesses. Results suggest that students learned in similar context independently of the way that they used to access learning contents. This may challenge current assumptions about the mobility of students.     

Open corpus architecture for personalised ubiquitous e-learning

As the e-learning area matures, there are a growing number of e-learning content providers that produce and distribute material that covers a large range of topics, differs in quality and is represented in various formats. Lately, different devices and various network technologies allow extensive user access to educational content almost anywhere, anytime and from any device. Ubiquitous e-learning has the potential to provide continuous and context-based, educational material to human learners anytime, anywhere and on any device. Since each person has different expectations related to the content, the performance of the delivery and display of that content, it is desirable for an ubiquitous e-learning environment to provide user-oriented personalisation of e-learning material. However very often there are multiple sources of e-learning material at various web locations (open corpus resources) that cover the same topic, but differ in terms of quality, formatting and even cost. It is very difficult for learners to select the content that best suits their interests and goals, characteristics of the device used and delivery network as well as their cost budget. This paper proposes an innovative ubiquitous e-learning environment called Performance-based E-learning Adaptive Cost-efficient Open Corpus frameworK (PEACOCK) that provides support for the selection and distribution of personalised e-learning rich media content (e.g. multimedia, pictures, graphics and text) to e-learners such as it will best suit users’ interests and goals, meet their formatting preferences and cost constraints, while considering the limitations introduced by the end-user devices and the delivery networks to the user. PEACOCK’s main goal is to maximise the users’ e-learning experience and increase their learning satisfaction and learning outcome.     

Accessible e-learning material: a no-frills avenue for didactical experts

Different organizations adopt accessibility for various and diverse reasons. Interesting and inspiring as such reasons may be, it is important to ensure that the motivation for supporting accessibility is that it is the right thing to do, i.e. a commitment to the provision of equal opportunities for accessing resources for people with special needs. Among the various efforts for supporting the development of accessible e-learning material, most of them propose guidelines that prevalently address technical accessibility issues (such as the format and navigation of learning material) with little or no consideration for the didactical experts, and thus their didactical experience, in developing learning material. Moreover, the aforementioned guidelines tend to provide generic indications on alternative forms of didactical content for equivalent access to it. None the less, the sole provision of equivalent forms does not guarantee the retention of desirable user interface aspects and may therefore have a negative impact on learning effectiveness. While this paper acknowledges the role of such guidelines, it does propose that the didactical experts be provided with a non-technical recourse, improving their development of accessible e-learning content. By tapping into the experience of the didactical experts, this work provides them with an avenue leading to enhance the accessibility of e-learning material.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

Investigating students’ perceived satisfaction,behavioral intention,and effectiveness of e-learning: A case study of the Blackboard system

Although the benefits of e-learning have been discussed in various previous studies; it is a critical issue of better understanding the reasons why some learners are dissatisfied with the e-learning experience. Therefore, this research investigates learners’ satisfaction, behavioral intentions, and the effectiveness of the Blackboard e-learning system. A total of 424 university students were surveyed using a standard questionnaire. The results showed that perceived self-efficacy is a critical factor that influences learners’ satisfaction with the Blackboard e-learning system. Perceived usefulness and perceived satisfaction both contribute to the learners’ behavioral intention to use the e-learning system. Furthermore, e-learning effectiveness can be influenced by multimedia instruction, interactive learning activities, and e-learning system quality. This research proposes a conceptual model for understanding learners’ satisfaction, behavioral intention, and effectiveness of using the e-learning system.     

A comprehensive adaptive system for e-learning of foreign languages

The article presents a proposal, design and implementation of a new approach to adaptive e-learning systems. First, a proposal of a model is presented. This model aims at introducing adaptivity to current e-learning systems, which are rigid and limited in offering a truly personalised learning to individual students. Many of current e-learning systems enable personalised learning. However, in this paper, there is a new, innovative approach proposed for an adaptive personalised e-learning system. The primary area of our research is English as a second language (ESL). Adaptivity in our view is considered as an ability of the system to adapt to student's knowledge and characteristics. This pedagogical perspective requires introduction of such processes that enable to work the pedagogical aspects of teaching/learning. The required processes are of informatics nature. The proposed model was subsequently designed into a real application. Finally, the application was implemented and verified on a real data set. The results are also provided.     

A distributed system for learning programming on-line

Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the “UVA On-line Judge”, an existing on-line programming trainer with an important number of problems and users, into an effective educational environment consisting of the e-learning platform Moodle and the competitive learning tool QUESTOURnament. The result is the EduJudge system which allows teachers to apply different pedagogical approaches using a proven e-learning platform, makes problems easy to search through an effective search engine, and provides an automated evaluation of the solutions submitted to these problems. The final objective is to provide new learning strategies to motivate students and present programming as an easy and attractive challenge. EduJudge has been tried and tested in three algorithms and programming courses in three different Engineering degrees. The students’ motivation and satisfaction levels were analysed alongside the effects of the EduJudge system on students’ academic outcomes. Results indicate that both students and teachers found that among other multiple benefits the EduJudge system facilitates the learning process. Furthermore, the experiment also showed an improvement in students’ academic outcomes. It must be noted that the students’ level of satisfaction did not depend on their computer skills or their gender.     

A learning style classification mechanism for e-learning

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student’s learning style. Hence, the first step for achieving adaptive learning environments is to identify students’ learning styles. This paper proposes a learning style classification mechanism to classify and then identify students’ learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students’ learning styles.     

Student’s perceptions towards using e-learning via Facebook

Research on utilising social networks for teaching and learning is relatively scarce in the context of information systems. There is far more emphasis on studying the usage of social networks towards fulfilling individuals’ basic social needs. This study uses the unified theory of acceptance and use of technology (UTAUT2) to analyse students’ intention to use and use of e-learning via Facebook. It incorporates playfulness into the UTAUT2 model and categorises the determinants of intention to use e-learning via Facebook into three categories, namely, hedonic values, utilitarian values, and communication values. The data were collected in a two-stage survey from 170 undergraduate students, and the model was tested using structural equation modelling. We found that hedonic motivation, perceived playfulness, and performance expectancy were strong determinants of students’ intention to use e-learning, while habit and facilitating conditions all positively affected students’ use of e-learning via Facebook. The results of this study report new knowledge that academic institutions can utilise to create appropriate e-learning environments for teaching and learning. A number of theoretical and managerial implications for universities’ implementation technologies were also identified.     

The student experience of a collaborative e-learning university module

The aim of this paper is to present a picture of student experience of a collaborative e-learning module in an asynchronous e-learning environment. A distance learning module on music education worth five credit points for a bachelor online degree for primary school educating teachers was assessed using a self-evaluation questionnaire that gathered quantitative and qualitative data about student satisfaction of the collaborative e-learning activity. The quantitative part of the questionnaire consisted of 27 closed questions on a 10-point Likert scale and offered data about satisfaction with the module. The qualitative part of the questionnaire provided an insight into the participant perspective of the online collaborative experience. General open questions on satisfaction and dissatisfaction were analyzed with an inductive analysis which showed the evaluation criteria used by 92 students. Results of the analysis showed five themes of the participants’ perspectives, which were interpreted by the researcher as: teamwork, cognitive, operating, organizing, and emotive/ethic for the positive aspects and teamwork, operating, organizing, and emotive/ethic for the aspects to be improved. The aspects that were associated with satisfaction include: collaborating, comparing ideas, sharing knowledge and skills to support each other, peer learning, analyzing and integrating different points of view, the usability of the platform, group planning and workload management. Aspects of the student learning experience that should inform the improvements of e-learning include: more collaboration between students since some students engage differently; more coordination and organization, the workload management in the group activities, some technical problems such as updating modifications. The participants’ results in the module increased their didactic potential as primary school teachers. The findings are discussed in relation to their potential impact on developing collaborative activities addressed to teacher education in distance learning. Implications for future research are also considered.     

e-Learning process management and the e-learning performance: Results of a European empirical study

The aim of this paper was to design and assess a comprehensive model for managing the e-learning process and to define the relationship between systematic implementation of the model, outcomes of certain e-learning aspects and subject of e-learning. The validation of the model was performed by using two questionnaires sent via e-mail to teachers and field experts from the chosen sample of 14 European schools participating in an EU-funded project. Research results imply the existence of a clear link between planning and controlling of the e-learning process and its learning outcomes. On the other hand, no empirical relationship between the e-learning outcomes and the subject of learning has been established. It is believed that the model and its practical implications can be used by institutions engaged in e-learning, or as a process model for introducing e-learning related activities.     

Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need some centroid for the cluster initialization. Furthermore, the other researcher tried to apply ontology for the cluster on social network domain. This paper tries to reveal the behavior of students from all activities in Moodle e-learning system by putting ontology on domain social learning network (Moodle) which is not explored in the prior study. The activities such as forum, quiz, assignment, etc. are placed as clustering parameter according to the ontology model. The ontology of Moodle e-learning system is created to capture the activities of the student inside Moodle e-learning. Five meaningful attributes are used as group cluster for classifying the students' behaviour. According to the result, most of the students belong to the intentional group while some of the students belong to the constructive and active group. The constructed cluster is calculated based on the e-learning hits during the learning process inside Moodle e-learning. The result on the classification of students' behaviour using ontology cluster is comparable to their final achievement grade. It is believed that this study can bring immense benefit to the development of e-learning system in the future.     

Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness

Interactive video in an e-learning system allows proactive and random access to video content. Our empirical study examined the influence of interactive video on learning outcome and learner satisfaction in e-learning environments. Four different settings were studied: three were e-learning environments—with interactive video, with non-interactive video, and without video. The fourth was the traditional classroom environment. Results of the experiment showed that the value of video for learning effectiveness was contingent upon the provision of interactivity. Students in the e-learning environment that provided interactive video achieved significantly better learning performance and a higher level of learner satisfaction than those in other settings. However, students who used the e-learning environment that provided non-interactive video did not improve either. The findings suggest that it may be important to integrate interactive instructional video into e-learning systems.     

Recommending suitable learning paths according to learners’ preferences: Experimental research results

The paper deals with the problem of personalising learning units with the main focus on finding personalised learning paths in learning units. Finding suitable learning paths is based on students’ needs in terms of their learning styles. It has been shown that learning path in static and dynamic learning units can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model, mainly by adapting ant colony optimisation method based on collaboration and pheromones. In the paper, experimental results of applying the proposed approach in practise are presented. The results of empirical experiment have shown that learning in the proposed prototype of e-learning system applying created recommending method improves students’ learning results and saves their learning time. This fact indicates that the developed adaptive method for personalising learning units is practically applicable in e-learning and enhances the learning quality.     

Analyzing the effects of emotion management on time and self-management in computer-based learning

Emotional learning involves the acquisition of skills to recognize and manage emotions, develop care and concern for others, make responsible decisions, establish positive relationships, and handle challenging situations effectively. Time is an important variable in learning context and especially in the analysis of teaching-learning processes that take place in collaborative learning, whereas time management is crucial for effective learning. The aim of this work has been to analyze the effects of emotion management on time and self-management in e-learning and identify the competencies in time and self-management that are mostly influenced when students strive to achieve effective learning. To this end, we run an experiment with a class of high school students, which showed that increasing their ability to manage emotions better and more effectively enhances their competency to manage the time allocated to the learning practice more productively, and consequently their learning performance in terms of behavioral engagement and achievement and partly, in terms of cognitive engagement and self-regulation. Teacher affective feedback was proved to be a crucial factor to enhance cognitive engagement.     

Behavioral intention,use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Widespread implementation of e-learning systems – learning management systems, virtual learning environments – across higher education institutions has aroused great interest on the study of e-learning acceptance. Acceptance studies focus on the predictors of system adoption and use, with behavioral intention to use the system as a proxy for actual use. This study proposes a TAM3-based model – with the inclusion of two additional variables: personal innovativeness in the domain of information technology and perceived interaction – to study the factors influencing the acceptance of e-learning systems. Attention is also brought towards the role of behavioral intention, especially in its relation with use behavior. In order to do so, two different settings were considered: higher education and lifelong learning; data was gathered from a survey administrated to Spanish graduate and lifelong learning students, and partial least squares analysis was used to test the research model. Results supported TAM relations, except for the intention-behavior linkage, and unveiled a dual nature of perceived usefulness – with one component related to efficiency and performance, and another component related to flexibility. The adequacy of applying TAM3-based models in educational contexts and suitability of actual system usage measures are also discussed.