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

Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Vesin, Boban; Budimac, Zoran

doi:10.1007/s10489-017-1051-8

Cited by 85 publications

(57 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Protus (PRogramming TUtoring System) [41] is an intelligent web-based programming tutoring system. The system integrates collaborative tagging technique to provide personalized recommended learning resources.…”

Section: Comparison Of Personalized E-learning With Social Collabmentioning

confidence: 99%

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Al-Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2019

IJACSA

View full text Add to dashboard Cite

With the emergence of Web 2.0 technologies, interaction and collaboration support in the educational field have been augmented. These types of support embrace researchers to enrich the e-learning environment with personalized characteristics with the utilization of the collaboration support outputs. Achieving this requires understanding the existing environments and highlights their eminence. As a result, there are many attempts to state the current status of personalized e-learning environment from different perspectives. However, these attempts targeted a specific view and direction which failed to provide us with the general view of the adoption of personalized e-learning environment with the support of social collaboration tools. This paper provides a classified view of the current status of personalized e-learning environments which incorporate social collaboration tools for providing the personalization feature. The classification adopts four different views to carry out the classification; these views are subject, purpose, method, and tool. The findings show that the utilization of the user-generated contents and social interaction functionalities for personalization is tight and not fully consumed. In short, the potential of providing personalized learning with social interaction and collaboration features remains not fully explored.

show abstract

Section: Comparison Of Personalized E-learning With Social Collabmentioning

confidence: 99%

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Al-Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…In other words, tags provide evidence of what materials others have seen and what sense they made out of these materials. Looking to the future, learner-generated tags, shared in a collaborative system, have been suggested as one means of generating personalized recommendations within an e-learning system (Klašnja-Milićević, Ivanović, Vesin, & Budimac, 2018).…”

Section: Social Bookmarking and Tagging In The Educational Settingmentioning

confidence: 99%

Effective Tagging Practices for Online Learning Environments: An Exploratory Study of Tag Approach and Accuracy

Dennen

Bagdy

Cates

2018

OLJ

View full text Add to dashboard Cite

This exploratory study examines student tagging activity within a five-week social bookmarking unit. Students in six sections of a course were tasked with locating, tagging, and then highlighting and discussing course-related materials using Diigo, a social bookmarking tool. Three different tagging approaches were tested: dictionary only, freestyle only, and dictionary + freestyle. Analysis focused on accuracy and rates of student tagging, popularity of different tag types Findings show that most students were able to tag with high rates of accuracy after a single brief lesson. The dictionary-only approach led to fewer tags overall as well as fewer single-use tags than freestyle tagging. It also resulted in students applying useful classes of tags, such as type of content, that did not emerge within the freestyle tag groups’ folksonomies. However, freestyle tagging was not without its merits, and provided opportunities for students to include tags that reflect relevant interests and more specific topics that were not addressed in the tag dictionary. The combined approach, if carefully taught and applied, appears to have the greatest potential for supporting student information literacy skills.

show abstract

“…Many universities have established that LA can signi¯cantly improve the promotion of the institution itself in terms of allocation of resources, learner achievements,¯nance and administration. This paper, based on comprehensive analysis of the above-mentioned literature and our long-term experience [35][36][37][38] with implementation, use and evaluation of a personalized, recommendation-based education system, proposes a personalized LA framework that can have valuable implications on learning success prediction.…”

Section: Introductionmentioning

confidence: 99%

Designing Personalized Learning Environments — The Role of Learning Analytics

Klašnja-Milićević

Ivanović

Stantić

2020

Vietnam J. Comp. Sci.

Self Cite

View full text Add to dashboard Cite

Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. It is an attempt to articulate the characterizing properties that reveals the association between learning analytics and personalized learning environment. In order to verify data analysis approaches and to determine the validity and accuracy of a learning analytics, and its corresponding to learning profiles, a case study was performed. The findings indicate that educational data for learning analytics are context specific and variables carry different meanings and can have different implications on learning success prediction.

show abstract

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

Cited by 85 publications

References 36 publications

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Effective Tagging Practices for Online Learning Environments: An Exploratory Study of Tag Approach and Accuracy

Designing Personalized Learning Environments — The Role of Learning Analytics

Contact Info

Product

Resources

About