Aleksandra Klašnja-Milićević

Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

With the development of the Semantic web the use of ontologies as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration and construction of knowledge rich and intensive systems. Semantic web provides huge potential and opportunities for developing the next generation of e-learning systems. In previous work, we presented tutoring system named Protus (PRogramming TUtoring System) that is used for learning the essence of Java programming language. It uses principles of learning style identification and content recommendation for course personalization. This paper presents new approach to perform effective personalization highly based on Semantic web technologies performed in new version of the system, named Protus 2.0. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. Functionality, structure and implementation of a Protus 2.0 ontology as well as syntax of SWRL rules implemented for on-the-fly personalization will be presented in this paper.

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

Social tagging strategy for enhancing e-learning experience

Abstract Success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of a specific learner. Learning experience and dynamic choice of educational material that is presented to learners can be enhanced using different recommendation techniques. As popularity of collaborative tagging systems grows, users’ tags could provide useful information to improve recommender system algorithms in e-learning environments. In this paper, we present an approach for implementation of collaborative tagging techniques into online tutoring system. The implemented approach combines social tagging and sequential patterns mining for generating recommendations of learning resources to learners. Several experiments were carried out in order to verify usability of the proposed hybrid method within e-learning environment and analyze selected social tagging techniques.

Recommender Systems in E-Learning Environments

Recommender system can be defined as a platform for providing recommendations to users based on their personal likes and dislikes. These systems use a specific type of information filtering technique that attempt to recommend information items (movies, music, books, news, Web pages, learning objects, and so on.) to the user. Recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take a recommendation strategy from one context and transfer it to another context or domain. Personalized recommendation can help learners to overcome the information overload problem, by recommending learning resources according to learners’ habits and level of knowledge. The first challenge for designing a recommender component for e-learning systems is to define the learners and the purpose of the specific context or domain in a proper way. This chapter provides an overview of techniques for recommender systems, folksonomy and tag-based recommendation to assist the reader in understanding the material which follows in subsequent chapters.

Data science in education: Big data and learning analytics

This paper considers the data science and the summaries significance of Big Data and Learning Analytics in education. The widespread platform of making high‐quality benefits that could be achieved by exhausting big data techniques in the field of education is considered. One principal architecture framework to support education research is proposed.

Folksonomy and Tag-Based Recommender Systems in E-Learning Environments

Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users’ preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.

Personalization and Adaptation in E-Learning Systems

Personalization is a feature that occurs separately within each system that supports some kind of users’ interactions with the system. Generally speaking term “Personalization” means the process of deciding what the highest value of an individual is if (s)he has a set of possible choices. These choices can range from a customized home page “look and feel” to product recommendations or from banner advertisements to news content. In this monograph we are interested in personalization in educational settings. The topic of personalization is strictly related to the shift from a teacher-centred perspective of teaching to a learner-centred, competency-oriented one. Two main approaches to the personalization can be distinguished: user-profile based personalization and rules-based personalization. In the first case this is the process of making decisions based upon stored user profile information or predefined group membership. In the second case this is the process of making decisions based on pre-defined business rules as they apply to a segmentation of users. This chapter presents the most popular adaptation forms of educational materials to learners.

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.

Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System

The success of intelligent tutoring system depends on the retrieval of relevant learning material according to the learner’s requirements. Therefore, the ultimate goal is development of the intelligent tutoring system that provides learning materials considering the requirements and understanding capability of the specific learner. In our previous research, we implemented a tutoring system named Protus 2.1 (PROgramming TUtoring System) that is used for learning basic concepts of Java programming language. It directs the learner’s activities and recommends relevant actions during the learning process based on the personal profile of each learner. This paper presents the implementation of collaborative tagging technique for content recommendation in Protus 2.1. This technique can be applied in intelligent tutoring systems for providing tag-enabled recommendations of concepts and resources. We investigated and identified tagging practices of students and their evolution over time.

Personalization in Protus 2.1 System

The ultimate goal of developing Protus 2.1 system has been increasing the learning opportunities, challenges and efficiency. Two important ways of increasing the quality of Protus 2.1 service are to make it intelligent and adaptive. Different techniques need to be implemented to adapt content delivery to individual learners according to their learning characteristics, preferences, styles, and goals. Protus 2.1 provides two general categories of personalization in system based on adaptive hypermedia and recommender systems: content adaptation and adaptation of user interface. Several approaches are used to personalize the material presented to the learner. Programming course in Protus 2.1 offers three types of personalization to each individual learner: (1) use of recommender systems, (2) learning styles personalization and (3) personalization based on resource sequencing. This chapter presents Protus 2.1 functionalities as well as personalization options from the end-user perspective.