Application of machine learning techniques to analyse student interactions and improve the collaboration process

In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006–2007, 2007–2008 and 2008–2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.     

Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades

Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions/registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students’ marks. Students’ key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student’s performance. Finally, a prototype version of software support tool for tutors has been constructed.     

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.     

A natural language approach to content-based video indexing and retrieval for interactive e-learning

As a powerful and expressive nontextual media that can capture and present information, instructional videos are extensively used in e-learning (Web-based distance learning). Since each video may cover many subjects, it is critical for an e-learning environment to have content-based video searching capabilities to meet diverse individual learning needs. In this paper, we present an interactive multimedia-based e-learning environment that enables users to interact with it to obtain knowledge in the form of logically segmented video clips. We propose a natural language approach to content-based video indexing and retrieval to identify appropriate video clips that can address users' needs. The method integrates natural language processing, named entity extraction, frame-based indexing, and information retrieval techniques to explore knowledge-on-demand in a video-based interactive e-learning environment. A preliminary evaluation shows that precision and recall of this approach are better than those of the traditional keyword based approach.     

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.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

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.     

Performance evaluation of an online argumentation learning assistance agent

Recent research indicated that students’ ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners’ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.     

An activity-theoretical approach to investigate learners’ factors toward e-learning systems

The Internet and World Wide Web have provided opportunities of developing e-learning systems. The development of e-learning systems has started a revolution for instructional content delivering, learning activities, and social communication. Based on activity theory, the purpose of this research is to investigate learners’ attitude factors toward e-learning systems. A total 168 participants were asked to answer a questionnaire. After factor analysis, learners’ attitudes can be grouped four different factors – e-learning as a learner autonomy environment, e-learning as a problem-solving environment, e-learning as a multimedia learning environment, and teachers as assisted tutors in e-learning. In addition, this research approves that activity theory is an appropriate theory for understanding e-learning systems. Furthermore, this study also provides evidence that e-learning as a problem-solving environment can be positively influenced by three other factors.     

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.     

An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning

Learning management system (LMS) is playing a major role in higher academic institutions worldwide. Even though full e-learning is becoming a feasible strategy for a number of institutions in the world, some institutions, especially those in developing countries, are resisting a full e-learning environment. Consequently, these academic institutions initially adopt LMS for blended learning to assess their readiness for full e-learning transformation. There are a number of studies that investigate the determinants of full e-learning, but very limited studies investigate the link between learners’ perception of blended learning and full e-learning. The objective of this study was to link learners’ adoption (perceived ease of use, perceived usefulness (PU) and satisfaction) of LMS in blended learning and their personal characteristics (self-efficacy, technology experience and personal innovativeness) to their intention to use full e-learning. Data were collected through a questionnaire from 512 learners in Oman. The study found that personal innovativeness, PU and satisfaction of LMS in blended learning are significant to learners’ intention to engage in full e-learning. Thus, learners’ adoption of LMS in blended learning boosts their intention to full e-learning. The results provide useful insights for practitioners and researchers on full e-learning planning and strategy.     

Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support

Current endeavors to integrate competency-based learning approaches with e-learning systems designed for delivery of training to adult learners in the workplace are growing. However, academic efforts in examining learners’ perceptions of, and reactions toward, this technology-delivered pedagogical innovation are limited. Drawing together perspectives from the literature on training and instructional design and technology acceptance, this research proposed a conceptual model to examine the influences of perceived individual and social learning support on employees’ acceptance of competency-based e-learning systems. Structural equation modeling and multi-group structural equation modeling techniques were applied to sample data collected from work settings in Mainland China. The results show the positive effects of perceived individual learning support and perceived support for promoting a norm of cooperation on employees’ intention to use the competency-based e-learning system. It was also found that perceived support for enhancing social ties had a negative effect on employees’ behavioral intention. The gender, age, prior experience, and work experience differences in the main effects were also investigated. Besides, qualitative data collected via semi-structured in-depth interview provide complementary evidence to the findings.     

A development of learning widget on m-learning and e-learning environments

This article describes the development of learning widget on m-learning and e-learning environments. A widget is a small, simple and useful application supporting user-oriented contents. The user may select and install widgets that are convenient as well as an auto-updating application including weather or calendar. These widgets are especially more useful, because they are able to be installed on a mobile device, a website or a desktop computer. If we take advantage of widgets for education, we may use this learning tool for delivering and pulling learning contents, essences of lessons or word learning. To that end, we developed an effective learning widget and then verified its usability, usefulness and effectiveness for m-learning and e-learning. That is, we evaluated the learning widget with a heuristic evaluation method. We identified 72 interface problems by using a set of 10 usability criteria or heuristics. In addition, we considered how to design the learning widget with consideration given to devices on m-learning and e-learning. Moreover, we experimented by conducting a pilot test with 34 students, a field test with 60 teachers and technology acceptance model (TAM) analysis with 15 teachers. We verified the effectiveness and usefulness of learning with a questionnaire, a quiz and TAM, where the subjects, after using the learning widget in real learning activities, rated the widget's efficacy. The result shows that the learning widget is useful for m-learning and e-learning environments.     

Virtual reality for collaborative e-learning

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.     

PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming

In this paper an evaluation method for assessing the effectiveness, accuracy and validity of a student model was presented. Our method is called PeRSIVA and is a combination of the well-known evaluation method of Kirkpatrick and the layered evaluation framework. These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system. Furthermore, PeRSIVA method was used to evaluate the hybrid student model, which combines an overlay model with stereotypes and fuzzy logic techniques, of an e-learning system. Particularly, PeRSIVA assesses the results of student modeling in students' satisfaction, performance, progress, behavior and state, as well as the validity of the conclusions drawn by the student model and the validity of the adaptation decision making. The e-learning system was used by the students of a postgraduate program in the field of informatics in the University of Piraeus and the evaluation results demonstrated learning improvements in students and adaptation success to students' needs.     

基于学习对象的网络学习内容组织方法

本文在研究学习对象和学习对象元数据及参考IMS学习资源元数据标准和内容包装规范的基础上,提出了一种基于学习对象的网络学习内容组织方案,该方案利用元数据模型描述学习对象的"模式"信息,以支持对网络学习内容的访问和搜索;提出了一个学习对象包装模型对网络学习内容进行组织和包装,以支持对网络学习内容的重用和交换。     

The role of experience and innovation characteristics in the adoption and continued use of e-learning websites

With the advent of e-learning technologies in the past decade, the accessibility of training, teaching, and learning has drastically increased. The challenge for the education enterprise now is how to attract learners to their e-learning services. In this study, a technology adoption model is developed to predict the users’ intention of adoption and their continued use behavior. The results show significant evidence in support of the hypothesis. The findings indicate that perceptions of relative advantage and compatibility are significantly related to users’ intention to use e-learning. Also, the intention is significantly related to their actual use of e-learning. Furthermore, the technology adoption of learners with prior e-learning experience is different from those without prior e-learning experience. These findings may contribute to deeper understanding of e-learning users’ perceptions in terms of adoption and continued use behavior.     

Investigating e-learning system usage outcomes in the university context

This paper investigates the outcomes of e-learning systems adoption and use by conceptualizing three e-learning systems adoption outcome constructs namely perceived learning assistance, perceived community building assistance and perceived academic performance. Utilizing these constructs, the paper proposes a research model for assessing the possible outcomes of e-learning systems adoption and use. The study collected longitudinal survey data from 249 university students participating in hybrid courses using a popular learning management system, Moodle. Partial least squares (PLS) approach was then used to test the research model.The findings suggest that beliefs about perceived usefulness and perceived ease of use, and how an e-learning system is used influence students' perceived learning assistance and perceived community building assistance. In turn, perceived learning assistance and perceived community building assistance influence the students' perceived academic performance.     

Guest Editors' Introduction: Emerging Internet Technologies for E-Learning

As a flourishing field, e-learning at large (including distance learning, Web-based learning, and digital game-based learning) has attracted increasing attention from both industry and academic sectors. To facilitate the development of effective e-learning systems, scalable technologies that support an arbitrary number of users while providing them with a good learning environment are needed. In this introduction, we provide an overview of e-learning system development with respect to a layered reference architecture, including the Internet infrastructure layer, the conceptual/modeling layer, and the application layer. In addition, the three articles included in this special issue cover the issues of managing the learning objects in an open and scalable architecture, incorporation of learners' pedagogical features in Web-based learning environments, and support of digital game-based learning. All these issues, though not exhaustive, are important to ensure successful development of e-learning systems on an Internet platform.