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In the World Declaration on Higher Education, the concept of higher education is defined as “all types of studies, training or research training at the postsecondary level, provided by universities or other educational establishments that are approved as institutions of higher education by the competent state authorities”
Aleksandra Klašnja-Milićević; Mirjana Ivanović. E-learning Personalization Systems and Sustainable Education. Sustainability 2021, 13, 6713 .
AMA StyleAleksandra Klašnja-Milićević, Mirjana Ivanović. E-learning Personalization Systems and Sustainable Education. Sustainability. 2021; 13 (12):6713.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Mirjana Ivanović. 2021. "E-learning Personalization Systems and Sustainable Education." Sustainability 13, no. 12: 6713.
This paper contributes to the research on explainable educational recommendations by investigating explainable recommendations in the context of personalized practice system for introductory Java programming. We present the design of two types of explanations to justify recommendation of next learning activity to practice. The value of these explainable recommendations was assessed in a semester-long classroom study. The paper analyses the observed impact of explainable recommendations on various aspects of student behavior and performance.
Jordan Barria-Pineda; Kamil Akhuseyinoglu; Stefan Želem-Ćelap; Peter Brusilovsky; Aleksandra Klasnja Milicevic; Mirjana Ivanovic. Explainable Recommendations in a Personalized Programming Practice System. Algorithms and Data Structures 2021, 64 -76.
AMA StyleJordan Barria-Pineda, Kamil Akhuseyinoglu, Stefan Želem-Ćelap, Peter Brusilovsky, Aleksandra Klasnja Milicevic, Mirjana Ivanovic. Explainable Recommendations in a Personalized Programming Practice System. Algorithms and Data Structures. 2021; ():64-76.
Chicago/Turabian StyleJordan Barria-Pineda; Kamil Akhuseyinoglu; Stefan Želem-Ćelap; Peter Brusilovsky; Aleksandra Klasnja Milicevic; Mirjana Ivanovic. 2021. "Explainable Recommendations in a Personalized Programming Practice System." Algorithms and Data Structures , no. : 64-76.
Adapting the presentation of learning material to the specific student?s characteristics is useful to improve the overall learning experience and learning styles can play an important role to this purpose. In this paper, we investigate the possibility to distinguish between Visual and Verbal learning styles from gaze data. In an experiment involving first year students of an engineering faculty, content regarding the basics of programming was presented in both text and graphic form, and participants? gaze data was recorded by means of an eye tracker. Three metrics were selected to characterize the user?s gaze behavior, namely, percentage of fixation duration, percentage of fixations, and average fixation duration. Percentages were calculated on ten intervals into which each participant?s interaction time was subdivided, and this allowed us to perform timebased assessments. The obtained results showed a significant relation between gaze data and Visual/Verbal learning styles for an information arrangement where the same concept is presented in graphical format on the left and in text format on the right. We think that this study can provide a useful contribution to learning styles research carried out exploiting eye tracking technology, as it is characterized by unique traits that cannot be found in similar investigations.
Nahumi Nugrahaningsih; Marco Porta; Aleksandra Klasnja-Milicevic. Assessing learning styles through eye tracking for e-learning applications. Computer Science and Information Systems 2021, 35 -35.
AMA StyleNahumi Nugrahaningsih, Marco Porta, Aleksandra Klasnja-Milicevic. Assessing learning styles through eye tracking for e-learning applications. Computer Science and Information Systems. 2021; (00):35-35.
Chicago/Turabian StyleNahumi Nugrahaningsih; Marco Porta; Aleksandra Klasnja-Milicevic. 2021. "Assessing learning styles through eye tracking for e-learning applications." Computer Science and Information Systems , no. 00: 35-35.
This paper describes an investigative study about the sense of smell, taste, hearing and vision to assist process of learning. This will serve to start the design and construction of a multisensory human-computer interface for different educational applications. The most important part is understanding the ways in which learners’ senses process learning and memorize information and in which relation these activities are. Though sensory systems and interfaces have developed significantly over the last few decades, there are still unfulfilled challenges in understanding multisensory experiences in Human Computer Interaction. The researchers generally rely on vision and listening, some of them focus on touch, but the taste and smell senses remain uninvestigated. Understanding the ways in which human’s senses influence the process, learning effects and memorizing information may be important to e-learning system and its higher functionalities. In order to analyze these questions, we carried out several usability studies with students to see if visual or audio experiences, different smells or tastes can assist and support memorizing information. Obtained results have shown improvement of learning abilities when using different smells, tastes and virtual reality facilities.
Aleksandra Klašnja-Milićević; Zoran Marošan; Mirjana Ivanović; Ninoslava Savić; Boban Vesin. The Future of Learning Multisensory Experiences: Visual, Audio, Smell and Taste Senses. Advances in Intelligent Systems and Computing 2018, 213 -221.
AMA StyleAleksandra Klašnja-Milićević, Zoran Marošan, Mirjana Ivanović, Ninoslava Savić, Boban Vesin. The Future of Learning Multisensory Experiences: Visual, Audio, Smell and Taste Senses. Advances in Intelligent Systems and Computing. 2018; ():213-221.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Zoran Marošan; Mirjana Ivanović; Ninoslava Savić; Boban Vesin. 2018. "The Future of Learning Multisensory Experiences: Visual, Audio, Smell and Taste Senses." Advances in Intelligent Systems and Computing , no. : 213-221.
This paper presents different approaches, experiences and perspectives of using technologies in higher education institutions. Particular case studies of application of social media (especially wikis), game-based learning and various technology-enhanced learning tools in different courses at several Serbian institutions are presented. In-house developed intelligent tutoring system Protus and possibilities to enhance it by software agents and eye-tracking are also shown in detail. Our experiences of using different technology-enhanced learning tools and mechanisms showed that educational processes must be modernized and enhanced by technological progress.
Mirjana Ivanović; Aleksandra Klasnja-Milicevic; Veljko Aleksić; Brankica Bratić; Milinko Mandić. Experiences and perspectives of Technology-enhanced learning and teaching in higher education – Serbian case. Procedia Computer Science 2018, 126, 1351 -1359.
AMA StyleMirjana Ivanović, Aleksandra Klasnja-Milicevic, Veljko Aleksić, Brankica Bratić, Milinko Mandić. Experiences and perspectives of Technology-enhanced learning and teaching in higher education – Serbian case. Procedia Computer Science. 2018; 126 ():1351-1359.
Chicago/Turabian StyleMirjana Ivanović; Aleksandra Klasnja-Milicevic; Veljko Aleksić; Brankica Bratić; Milinko Mandić. 2018. "Experiences and perspectives of Technology-enhanced learning and teaching in higher education – Serbian case." Procedia Computer Science 126, no. : 1351-1359.
Aleksandra Klašnja-Milićević; Mirjana Ivanović. Learning Analytics - New Flavor and Benefits for Educational Environments. Informatics in Education 2018, 17, 285 -300.
AMA StyleAleksandra Klašnja-Milićević, Mirjana Ivanović. Learning Analytics - New Flavor and Benefits for Educational Environments. Informatics in Education. 2018; 17 (2):285-300.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Mirjana Ivanović. 2018. "Learning Analytics - New Flavor and Benefits for Educational Environments." Informatics in Education 17, no. 2: 285-300.
Aleksandra Klasnja-Milicevic; Boban Vesin; Mirjana Ivanović. Social tagging strategy for enhancing e-learning experience. Computers & Education 2018, 118, 166 -181.
AMA StyleAleksandra Klasnja-Milicevic, Boban Vesin, Mirjana Ivanović. Social tagging strategy for enhancing e-learning experience. Computers & Education. 2018; 118 ():166-181.
Chicago/Turabian StyleAleksandra Klasnja-Milicevic; Boban Vesin; Mirjana Ivanović. 2018. "Social tagging strategy for enhancing e-learning experience." Computers & Education 118, no. : 166-181.
Boban Vesin; Aleksandra Klasnja-Milicevic; Katerina Mangaroska; Mirjana Ivanović; Rodi Jolak; Dave Stikkolorum; Michel Chaudron. Web-based educational ecosystem for automatization of teaching process and assessment of students. Proceedings of the 8th International Conference on Digital Arts 2018, 33 .
AMA StyleBoban Vesin, Aleksandra Klasnja-Milicevic, Katerina Mangaroska, Mirjana Ivanović, Rodi Jolak, Dave Stikkolorum, Michel Chaudron. Web-based educational ecosystem for automatization of teaching process and assessment of students. Proceedings of the 8th International Conference on Digital Arts. 2018; ():33.
Chicago/Turabian StyleBoban Vesin; Aleksandra Klasnja-Milicevic; Katerina Mangaroska; Mirjana Ivanović; Rodi Jolak; Dave Stikkolorum; Michel Chaudron. 2018. "Web-based educational ecosystem for automatization of teaching process and assessment of students." Proceedings of the 8th International Conference on Digital Arts , no. : 33.
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.
Aleksandra Klasnja-Milicevic; Mirjana Ivanović; Boban Vesin; Zoran Budimac. Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence 2017, 48, 1519 -1535.
AMA StyleAleksandra Klasnja-Milicevic, Mirjana Ivanović, Boban Vesin, Zoran Budimac. Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence. 2017; 48 (6):1519-1535.
Chicago/Turabian StyleAleksandra Klasnja-Milicevic; Mirjana Ivanović; Boban Vesin; Zoran Budimac. 2017. "Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques." Applied Intelligence 48, no. 6: 1519-1535.
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.
Aleksandra Klasnja-Milicevic; Mirjana Ivanović; Zoran Budimac. Data science in education: Big data and learning analytics. Computer Applications in Engineering Education 2017, 25, 1066 -1078.
AMA StyleAleksandra Klasnja-Milicevic, Mirjana Ivanović, Zoran Budimac. Data science in education: Big data and learning analytics. Computer Applications in Engineering Education. 2017; 25 (6):1066-1078.
Chicago/Turabian StyleAleksandra Klasnja-Milicevic; Mirjana Ivanović; Zoran Budimac. 2017. "Data science in education: Big data and learning analytics." Computer Applications in Engineering Education 25, no. 6: 1066-1078.
E-learning is becoming more and more important in contemporary education. It allows learners to learn at their own pace, when their schedule permits it. However, learners have individual needs and disparate traits such as learning styles, knowledge levels, motivation and cognitive abilities. So, a need for personalized learning has been made clear. Two ways of personalized learning are discussed in this paper: the first is Protus 2.1. - a tutoring system that allows personalization based on learning styles and collaborative tagging and the second one is PLeMSys - a model of a Moodle plug-in where personalization is based on learning styles and knowledge level.
Natasha Blazheska-Tabakovska; Mirjana Ivanovic; Aleksandra Klasnja-Milicevic; Jovana Ivkovic. Comparison of E-Learning Personalization Systems: Protus and PLeMSys. International Journal of Emerging Technologies in Learning (iJET) 2017, 12, 57 .
AMA StyleNatasha Blazheska-Tabakovska, Mirjana Ivanovic, Aleksandra Klasnja-Milicevic, Jovana Ivkovic. Comparison of E-Learning Personalization Systems: Protus and PLeMSys. International Journal of Emerging Technologies in Learning (iJET). 2017; 12 (1):57.
Chicago/Turabian StyleNatasha Blazheska-Tabakovska; Mirjana Ivanovic; Aleksandra Klasnja-Milicevic; Jovana Ivkovic. 2017. "Comparison of E-Learning Personalization Systems: Protus and PLeMSys." International Journal of Emerging Technologies in Learning (iJET) 12, no. 1: 57.
Eye tracking technologies can support several types of perception processes and verbal/visual user performances. Eye tracking methods have opened many new possibilities in examining cognitive processing and providing a comprehension of reasoning and mental imagery, so additional research in this area is fully justified. This paper examines an e-learning environment where eye tracking technologies can be used to observe user behaviour in order to adapt content presentation in real-time, to identify a method by which real time psychopathological response data can be collected, analysed, and implemented without compromising the learning experience.
Mirjana Ivanović; Aleksandra Klašnja-Milićević; Jovana Ivković; Marco Porta. Integration of Eye Tracking Technologies and Methods in an E-learning System. Proceedings of the 8th Balkan Conference in Informatics 2017, 1 -4.
AMA StyleMirjana Ivanović, Aleksandra Klašnja-Milićević, Jovana Ivković, Marco Porta. Integration of Eye Tracking Technologies and Methods in an E-learning System. Proceedings of the 8th Balkan Conference in Informatics. 2017; ():1-4.
Chicago/Turabian StyleMirjana Ivanović; Aleksandra Klašnja-Milićević; Jovana Ivković; Marco Porta. 2017. "Integration of Eye Tracking Technologies and Methods in an E-learning System." Proceedings of the 8th Balkan Conference in Informatics , no. : 1-4.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. E-Learning Systems. Springer Texts in Business and Economics 2017, 1 .
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. E-Learning Systems. Springer Texts in Business and Economics. 2017; ():1.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2017. "E-Learning Systems." Springer Texts in Business and Economics , no. : 1.
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.
Boban Vesin; Aleksandra Klasnja-Milicevic; Mirjana Ivanović. Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 236 -245.
AMA StyleBoban Vesin, Aleksandra Klasnja-Milicevic, Mirjana Ivanović. Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():236-245.
Chicago/Turabian StyleBoban Vesin; Aleksandra Klasnja-Milicevic; Mirjana Ivanović. 2016. "Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 236-245.
Recently e-learning systems are experiencing rapid development. The advantages of learning through a global network are manifold and obvious: the independence of time and space, learners can learn at their own pace, learning materials can be organized in one place and used-processed all around the world. One of the most important segments in today’s development and use of the e-learning system is the personalization of content and building of user profiles based on the learning behaviour of each individual user. The personalization options increase efficiency of e-learning, thus justifying the higher initial cost of their construction. In order to personalize the learning process and adapt content to each learner, e-learning systems can use strategies that have the ability to meet the needs of learners. Also, these systems have to use different technologies to change the environment and perform the adaptation of teaching materials based on the needs of learners. The process of adaptation can be in the form of adaptation of content, learning process, feedback or navigation. This chapter introduces the motivation and objectives studied in the subsequently presented research, and presents major standards and specifications in e-learning.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Introduction to E-Learning Systems. Springer Texts in Business and Economics 2016, 3 -17.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Introduction to E-Learning Systems. Springer Texts in Business and Economics. 2016; ():3-17.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Introduction to E-Learning Systems." Springer Texts in Business and Economics , no. : 3-17.
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.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Personalization and Adaptation in E-Learning Systems. Springer Texts in Business and Economics 2016, 21 -25.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Personalization and Adaptation in E-Learning Systems. Springer Texts in Business and Economics. 2016; ():21-25.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Personalization and Adaptation in E-Learning Systems." Springer Texts in Business and Economics , no. : 21-25.
A recent trend in the field of e-learning and tutoring systems is to utilize agent technology and develop and use different kinds of agents in virtual learning environments. Software agents, or simply agents, are usually defined as autonomous software entities, with various degrees of intelligence, capable of exhibiting both reactive and pro-active behaviour in order to satisfy their design goals. From the point of e-learning and tutoring systems harvester and pedagogical agents are of the special research interest. Harvester agents are in charge of collecting learning material from online, often heterogeneous repositories and success depends on the quality and standards of teaching material representation. The main goals of pedagogical agents are to motivate and guide students through the learning process, by asking questions and proposing solutions. This chapter presents a possible trend in use of intelligent agents for personalised learning within tutoring system. Some possibilities of the use of several kinds of agents in a stand-alone e-learning architecture are proposed.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Agents in E-Learning Environments. Springer Texts in Business and Economics 2016, 43 -49.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Agents in E-Learning Environments. Springer Texts in Business and Economics. 2016; ():43-49.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Agents in E-Learning Environments." Springer Texts in Business and Economics , no. : 43-49.
E-learning is an important segment of educational environments. It represents a unique opportunity to learn independently, regardless of time and place, to acquire knowledge without interruption and customized to the individual and based on the principles of traditional education. Today, the most popular forms of e-learning are: web-based e-learning systems, virtual classrooms or tutoring systems. This monograph presents how the Semantic web technologies, ontologies and adaptation rules can be used to improve the performance of an existing tutoring system. The architecture of a personalized tutoring system that relies entirely on Semantic Web technologies and standards is presented. Ontologies that correspond to the components of the traditional tutoring system are shown in detail. This chapter concludes the monograph, summarizing the main contributions and discussing the possibilities for future work.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Conclusions and Future Directions. Springer Texts in Business and Economics 2016, 287 -294.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Conclusions and Future Directions. Springer Texts in Business and Economics. 2016; ():287-294.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Conclusions and Future Directions." Springer Texts in Business and Economics , no. : 287-294.
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.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Recommender Systems in E-Learning Environments. Springer Texts in Business and Economics 2016, 51 -75.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Recommender Systems in E-Learning Environments. Springer Texts in Business and Economics. 2016; ():51-75.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Recommender Systems in E-Learning Environments." Springer Texts in Business and Economics , no. : 51-75.
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.
Aleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. Folksonomy and Tag-Based Recommender Systems in E-Learning Environments. Springer Texts in Business and Economics 2016, 77 -112.
AMA StyleAleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain. Folksonomy and Tag-Based Recommender Systems in E-Learning Environments. Springer Texts in Business and Economics. 2016; ():77-112.
Chicago/Turabian StyleAleksandra Klašnja-Milićević; Boban Vesin; Mirjana Ivanović; Zoran Budimac; Lakhmi C. Jain. 2016. "Folksonomy and Tag-Based Recommender Systems in E-Learning Environments." Springer Texts in Business and Economics , no. : 77-112.