Ontology-based recommender system in higher education

International audienceAcademic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one

IMPLEMENTATIONOF WEB SEMANTIC ON KNOWLEDGE MANAGEMENT SYSTEM

Abstract—Hitherto, previous research of string matching techniques in knowledge sharing of explicit knowledge have shown a great success. However, their implementation in a knowledge management system is still underexplored. The aim of this paper is to propose an implementation of web semantic techniques for supporting all processes in knowledge management systems and producing a better accuracy of searching knowledge within an organization. A web-based application prototype of web semantic is built and several experiments are performed in order to prove the correctness of our implementation.. Index Terms— Web Semantic; knowledge management syste

A Recommender Model for the Personalized Adaptive CHUNK Learning System

Recommender systems attempt to influence one’s behavior based on explicit and implicit information provided by the users of the system. Users who take part in e-commerce or watch cat videos online will be familiar with this concept. Different algorithms exist that determine what objects or concepts to recommend to users, but every one of them has the similar goal of providing a good recommendation. In this context, good means that the recommendation will be user relevant suggesting accurate topics, and will influence the user’s behavior. Additionally, a good recommendation system is adaptive, consistently seeking feedback from the user. Feedback is then used to make the next recommendation better. In this work, we develop a recommendation methodology for an existing personalized learning system, where both content and teaching methodology options are presented to the user. Our methodology provides solutions to both the user and the network coldstart problems, where little up-front information is available in order to make good recommendations. Using real system data, we show how our method recommends the most relevant learning topics and styles and incorporates user feedback to improve future recommendations.Do

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts