Tagging diversity in personal learning environments

Cao, Yiwei; Kovachev, Dejan; Klamma, Ralf; Jarke, Matthias; Lau, Rynson W. H.

doi:10.1007/s40692-015-0027-0

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Tagging serves both individual and collective pedagogical purposes. Individually, tagging activities promote self-regulation within the learning process (Cao, Kovachev, Klamma, Jarke, & Lau, 2015), whereas collaborative tagging activities are useful in classrooms because they promote peer engagement around content artifacts, and the act of tagging requires reflection (Bateman, Brooks, Mccalla, & Brusilovsky, 2007). Additionally, students working in the same topical area can learn from each other's processes and paths when they are documented by tagging activities .…”

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

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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.

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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

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“…It is important to note that a PLE is not just about the technology employed but is also about the ability to personalise the learning, the organisation of the platform and the establishment of clear goals. In this regards, [14] upholds that that PLEs allow learners to gather information, to plan how to learn, to reflect on it, and to learn with other learners in addition to setting their own learning goals, as well as manage, retrieve, and share learning resources. As part of planning, the following should be considered:…”

Section: Design Consideration For the Use Of A Plementioning

confidence: 99%

“…The chosen platform has to fit for the purpose, since it can impact the learners' extrinsic motivation. If the platform is not user-friendly, easy to navigate or broken down frequently, learners may be discouraged in using it which may result in them refusing to engage and can eventually drop out [14].…”

Section: Design Consideration For the Use Of A Plementioning

confidence: 99%

RETRACTED：Design and Development of a Personal Learning Environment for Corporate Self-Regulated Learning

Ndongfack¹

2016

JCC

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Retraction type (multiple responses allowed):  Unreliable findings   Lab error  Inconsistent data  Analytical error  Biased interpretation   Other:  Irreproducible results  Failure to disclose a major competing interest likely to influence interpretations or recommendations  Unethical research   Fraud   Data fabrication  Fake publication  Other: × Plagiarism  Self plagiarism  Overlap  Redundant publication *  Copyright infringement  Other legal concern:  Editorial reasons   Handling error  Unreliable review(s)  Decision error  Other:  Other: Results of publication (only one response allowed): × are still valid.  were found to be overall invalid. Author's conduct (only one response allowed):  honest error × academic misconduct  none (not applicable in this casee.g. in case of editorial reasons) * Also called duplicate or repetitive publication. Definition: "Publishing or attempting to publish substantially the same work more than once." History Expression of Concern:  yes, date: yyyy-mm-dd × no Correction:  yes, date: yyyy-mm-dd × no Comment: Free style text with summary of information from above and more details that can not be expressed by ticking boxes. This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. Aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

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“…By highlighting the most important part of a text, learners can remember it better. Furthermore, tagging activities could inspire learners to more thoroughly engage themselves in a learning process and could help them understand learning content [4]. Gathering of learners' opinions about specific resources, could give more comprehensible recommendations about the resources for other learners.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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 tagbased recommendations has been used and evaluated in a programming tutoring system.

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Tagging diversity in personal learning environments

Cited by 14 publications

References 30 publications

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

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

RETRACTED：Design and Development of a Personal Learning Environment for Corporate Self-Regulated Learning

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

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