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.     

Personalization in an interactive learning environment through a virtual character

Traditional hypermedia applications present the same content and provide identical navigational support to all users. Adaptive Hypermedia Systems (AHS) make it possible to construct personalized presentations to each user, according to preferences and needs identified. We present in this paper an alternative approach to educational AHS where a virtual character personalizes the interaction with the user through the use of a particular recommender system. The character has natural language communication abilities; it can learn students’ profiles and use this knowledge to recommend appropriate contents and activities. Through its interaction with the user, the character is able to collect and organize information about students in order to identify appropriate suggestions of contents. The recommender system employs a knowledge representation scheme that is easy to understand and to modify, enabling teachers/tutors to explore the types of recommendations being made and to appreciate why they are made. An experiment with computer science students was carried out in order to validate the approach proposed. The results of the experiment showed that the presentation of personalized links through a virtual character had a positive impact in the users’ perception of the system as a learning tool. The combination of the virtual character with a recommender system proved to be a good alternative for the delivery of personalized contents without making constant changes in the main user interface. This approach provides mechanisms to guide users through paths of study followed by students with similar profiles, without violating the human–computer interaction principle of perceived stability.     

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.     

ADOL: a novel framework for automatic domain ontology learning

Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational Ontology to describe systematic knowledge of different subjects automatically. Currently, most of the ontologies are developed and extended manually, which requires the developers to possess certain professional knowledge and is time-consuming. In this paper, a framework to construct and extend Educational Ontology automatically is proposed.2 The proposed ontology learning framework, called ‘ADOL,’ can convert domain textbooks into a corresponding ontology automatically and efficiently. A case study on High School Physics shows that our approach is feasible and efficient.

     

Integrating learning styles and adaptive e-learning system: Current developments,problems and opportunities

Learning styles which refer to students’ preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students’ learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches.     

Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations

The electronic learning (e-learning) has gradually become more and more important in today’s school in Taiwan. Many colleges and universities offer distance e-learning courses or programs for students. An effective teaching or learning through a distance web e-learning system depends on many factors (or criteria). The analytic hierarchy process (AHP) model is suitable for dealing with the multi-criteria problems. This paper utilizes the consistent fuzzy preference relations (CFPR) in AHP model to evaluate these factors. The CFPR is computational simplicity and guarantees the consistence of decision matrices. Rating the criteria is important. An empirical example using CFPR in AHP model to find the weights is presented. The weight can point out which factor is important, especially when the time, manpower, and financial support are limited. The rating results can be directly used to evaluate the distance e-learning effectiveness and can provide teachers and decision-makers in schools important information for improving e-learning practice in the future.     

Utilizing learning process to improve recommender system for group learning support

With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains, it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained mainly by two mechanisms: the learners’ learning processes and the analysis of social interaction. Therefore, this study proposes a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners’ learning activities and learning processes so that we can correctly estimate the rating which also include learners’ social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified recommendation.     

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.     

An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students’ usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.     

Determining factors of the use of e-learning environments by university teachers

E-learning environments increasingly serve as important infrastructural features of universities that enable teachers to provide students with different representations of knowledge and to enhance interaction between teachers and students and amongst students themselves. This study was designed to identify factors that can explain teachers’ use of e-learning environments in higher education. A questionnaire was completed by 178 teachers from a wide variety of departments at Wageningen University in the Netherlands. We found that 43% of the total variance in teacher use of e-learning environments could be explained by their opinions about web-based activities and their opinions about computer-assisted learning (predictors) and the perceived added value of e-learning environments (mediating variable). In other words, teachers’ use of e-learning environments can be explained to a high extent by their perceptions of the added value of these environments, which in turn are substantially influenced by their opinions about web-based activities and computer-assisted learning.     

Content-free collaborative learning modeling using data mining

Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future.     

User sensitive research in e-learning: exploring the role of individual user characteristics

The increasing need for active and accessible learning in the inclusive knowledge society drives the demand for e-learning that engages users much more effectively than ever before. In this context, it is crucial to conduct research that embraces innovation in user sensitive design, or else influential individual user differences may be overlooked. The objective of this paper is to explore the creation of successful e-learning systems that are able to increase users’ learning performance and enhance their personal learning experiences. The paper reports two converging and complimentary approaches, namely case studies and experimentation. First, case studies are used to explore the extent to which effective e-learning systems comply with eight specific factors. Of the eight, accessibility, individual differences and student modeling turn out to be the weakest points in current practice. Second, an empirical study investigates the influences of user individual user differences on users’ learning outcomes in an e-learning environment. The experiment found that individual differences in motivation to learn and expectations about e-learning significantly impacted users’ learning achievements. Third, based on these studies, improvements in research methodology are identified towards greater consideration of user sensitive research issues, thus enabling us to outline improved experimental procedures. Further experiment results should provide us with better insights into the arguments needed to carefully assess benefits of developing and involving a user model in an e-learning application. Consequently, evaluation and justification could now encompass both system performance as well as user performance.     

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.     

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.     

Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries

As in the Web, the growing of information is the main problem of the academic digital libraries. Thus, similar tools could be applied in university digital libraries to facilitate the information access by the students and teachers. In [46] we presented a fuzzy linguistic recommender system to advice research resources in university digital libraries. The problem of this system is that the user profiles are provided directly by the own users and the process for acquiring user preferences is quite difficult because it requires too much user effort. In this paper we present a new fuzzy linguistic recommender system that facilitates the acquisition of the user preferences to characterize the user profiles. We allow users to provide their preferences by means of incomplete fuzzy linguistic preference relation. We include tools to manage incomplete information when the users express their preferences, and, in such a way, we show that the acquisition of the user profiles is improved.     

Rapid prototyping for a virtual medical campus interface

The Virtual Medical Campus Graz uses e-learning to make the human medicine curriculum digitally accessible to a target population of 4500 students and teachers. This project occurred parallel to the development of a new curriculum and thus involved all medical teachers and students. The VMC system architecture includes a multimedia repository for reusable learning objects; middleware containing the VMC logic that arranges learning objects into lectures, themes, and modules; and user interface. To ensure the interface suited the target population, we used the user-centered design method.     

CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

For a software information agent, operating on behalf of a human owner and belonging to a community of agents, the choice of communicating or not with another agent becomes a decision to take, since communication generally implies a cost. Since these agents often operate as recommender systems, on the basis of dynamic recognition of their human owners’ behaviour and by generally using hybrid machine learning techniques, three main necessities arise in their design, namely (i) providing the agent with an internal representation of both interests and behaviour of its owner, usually called ontology; (ii) detecting inter-ontology properties that can help an agent to choose the most promising agents to be contacted for knowledge-sharing purposes; (iii) semi-automatically constructing the agent ontology, by simply observing the behaviour of the user supported by the agent, leaving to the user only the task of defining concepts and categories of interest. We present a complete MAS architecture, called connectionist learning and inter-ontology similarities (CILIOS), for supporting agent mutual monitoring, trying to cover all the issues above. CILIOS exploits an ontology model able to represent concepts, concept collections, functions and causal implications among events in a multi-agent environment; moreover, it uses a mechanism capable of inducing logical rules representing agent behaviour in the ontology by means of a connectionist ontology representation, based on neural-symbolic networks, i.e., networks whose input and output nodes are associated with logic variables.     

Personalized web-based tutoring system based on fuzzy item response theory

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner’s behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner’s cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner’s crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner’s non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner’s response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner’s uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.     

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.     

Adapting to changing expectations: Post-graduate students’ experience of an e-learning tax program

In response to the impact of information and communication technology on traditional business and commerce practices, and the empowerment of individuals by the growth of information available on the Internet, educators are challenged to adapt the curricula and delivery modes of educational programs for knowledge workers, such as tax accountants. Today’s tax accountants are expected to be computer and information literate, and are valued for their critical thinking skills based on subject knowledge. This article evaluates the implementation of an e-learning Masters Program in Taxation in South Africa based on the feedback from students. Three criteria are used: the use of technology, the learning design, and flexibility of the tax program. Students reacted positively to the use of technology, but expressed concern about the lack of interaction between students, and between students and teachers. The author argues that the choice of technologies, the outsourcing of teaching services, and the program facilitator’s teaching approach emulate the face-to-face tax program and therefore still support a teacher-centered teaching and learning approach. Students and teachers who are used to a traditional teacher-centered teaching and learning approach often measure the quality of a learning experience by the amount of information transmitted by a subject expert. Getting students to take responsibility for their own learning poses a bigger challenge than improving students’ computer and information skills. Several recommendations are made to improve e-learning programs that will produce self-directed, lifelong learners who can be productive in an information-based economy.