A tag based learning approach to knowledge acquisition for constructing prior knowledge and enhancing student reading comprehension

Chen, Junming; Chen, Meng-Chang; Sun, Yeali S.

doi:10.1016/j.compedu.2013.09.002

Cited by 24 publications

(7 citation statements)

References 23 publications

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“…Blended learning approach gradually played an important role in modern education, which was considered more effective than traditional learning approach [24]. • Language acquisition: On one hand, most of the CAEL studies were dedicated to improving learners' basic language skills (listening, speaking, reading and writing) [1,23,26,27]. Speech communication and human-computer interaction were emphasized.…”

Section: Discussionmentioning

confidence: 99%

A Bibliometric Analysis of Computer-assisted English Learning from 2001 to 2020

Liu

Zhang

2021

Int. J. Emerg. Technol. Learn.

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The aim of this study was to reveal hotspots and frontiers of computer-assisted English learning (CAEL) studies indexed by EI Compendex database from 2001 to 2020 via bibliometric analysis. The publication output has exponentially grown in the past two decades and is likely to progress in the next several years. China occupied the leading position， while Lecture Notes in Computer Science was the most prolific journal, and Deyi Xiong was the most productive author. Keyword analysis was assisted by VOSviewer software. Our results show that “computer aided instruction”, “computer aided language translation” and “learning systems” were the most frequently used keywords in documents. CAEL studies were mainly conducted from five dimensions (technology, learners, teaching, English acquisition and testing). The findings of this study have implications for English language instructors. Teaching methods and modes should be adjusted according to technology development.

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Section: Discussionmentioning

confidence: 99%

A Bibliometric Analysis of Computer-assisted English Learning from 2001 to 2020

Liu

Zhang

2021

Int. J. Emerg. Technol. Learn.

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“…Those recommendations are based on previous learners' activities or on the learning styles of the learners that are discovered from their navigation patterns. To design an effective RS in e-learning environments, it is important to understand specific learners' characteristics (García et al 2009;Chen et al 2014):…”

Section: The Most Important Requirements and Challenges For Designingmentioning

confidence: 99%

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

2015

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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.

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“…Within the literature covering collaborative tagging in e-learning, there are several existing works that have investigated the expected benefits of tagging when applied in describing educational resources: PLEM (Verpoorten, Chatti, Westera, & Specht, 2010), CROKODIL (Anjorin et al, 2011), SOAF (Cernea, Del Moral, & Labra Gayo, 2008), TaCS (Lavoué, 2012) and TAK system (Chen, Chen, & Sun, 2014).…”

Section: Related Workmentioning

confidence: 99%

Social tagging strategy for enhancing e-learning experience

Klašnja-Milićević

Vesin

Ivanović

2018

Computers & Education

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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 elearning environment and analyze selected social tagging techniques.

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A tag based learning approach to knowledge acquisition for constructing prior knowledge and enhancing student reading comprehension

Cited by 24 publications

References 23 publications

A Bibliometric Analysis of Computer-assisted English Learning from 2001 to 2020

A Bibliometric Analysis of Computer-assisted English Learning from 2001 to 2020

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

Social tagging strategy for enhancing e-learning experience

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