A bibliometric perspective of learning analytics research landscape

Waheed, Hajra; Hassan, Saeed‐Ul; Aljohani, Naif Radi; Wasif, Muhammad

doi:10.1080/0144929x.2018.1467967

Cited by 58 publications

(38 citation statements)

References 40 publications

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“…Student retention has become a standard strategic imperative for institutions, and the learning analytics phenomena cumulatively aids in retaining students, consequently resulting in accumulated graduation rates (Palmer, 2013). Academic and learning analytics consistently overlap in formulating the 'Educational Analytics' paradigm -learning analytics is associated with the learner's experience and academic analytics implicitly incorporates the overall institute and its performance (Waheed et al, 2018). Moreover, a semantic mapping of deep learning with educational data science is presented in Fig.…”

Section: Deep Learning For Educational Data Sciencementioning

confidence: 99%

“…Various data mining techniques are deployed on educational datasets to predict students' performance, assessing slow learners and dropouts (Abu-Oda & El-Halees, 2015; Kaur, 2015;Hardman et al, 2013;Yadav, 2012). The techniques employed on these learning analytics datasets aid in data-driven decision making (Waheed et al, 2018). Early prediction is a new phenomenon in this domain, encompassing methods to timely assess the students in order to retain them, by suggesting suitable corrective strategies and policies, subsequently managing and reducing attrition rates.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

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271

124

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Deep Learning For Educational Data Sciencementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

Self Cite

271

124

View full text Add to dashboard Cite

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“…A number of studies have used social networking analysis for social sciences and medical science research to find the most significant collaborating entities (Savić et al 2017;Wagner et al 2017;Didegah and Thelwall 2018;Borgatti et al 2009;Waheed et al 2018), using social network analysis on generally social media data and altmetric data (Hassan et al 2017b). Social media analysis has not been used to determine the communities in computer networking research due to which we do not yet have complete insights into the collaborating patterns that exist in computer networking research.…”

Section: Related Workmentioning

confidence: 99%

A bibliometric analysis of publications in computer networking research

et al. 2019

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Computer networking is a major research discipline in computer science, electrical engineering, and computer engineering. The field has been actively growing, in terms of both research and development, for the past hundred years. This study uses the article content and metadata of four important computer networking periodicals) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIG-COMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify

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“…Academics and researchers currently explore the capabilities of emergent technologies to support the learning process in an interactive learning environment. Table 1 summarizes recent relevant research on several dimensions of social networks and smart learning [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]:…”

Section: Literature Review On the Exploitation Of Social Networking Tmentioning

confidence: 99%

Social Networks Research for Sustainable Smart Education

et al. 2018

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Social networks research has grown exponentially over the past decade. Subsequent empirical and conceptual advances have been transposed in the field of education. As the debate on delivering better education for all gains momentum, the big question is how to integrate advances in social networks research, corresponding advances in information and communication technology (ICT) and effectively employ them in the domain of education. To address this question, this paper proposes a conceptual framework (maturity model) that integrates social network research, the debate on technology-enhanced learning (TEL) and the emerging concept of smart education.

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A bibliometric perspective of learning analytics research landscape

Cited by 58 publications

References 40 publications

Predicting academic performance of students from VLE big data using deep learning models

Predicting academic performance of students from VLE big data using deep learning models

A bibliometric analysis of publications in computer networking research

Social Networks Research for Sustainable Smart Education

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