Current state and future trends: a citation network analysis of the learning analytics field

By Shane Dawson, Dragan Gasevic, George Siemens, Srecko Joksimovic. International Conference on Learning Analytics and Knowledge, 2014

358 citations recorded by the source corpus.

Subfields: Computer Science Applications, Management Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b03bb0b13e3a5aca742b4fd7de417aa02e8f7254

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

In Management Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Tourism and its economic impact: A literature review using bibliometric tools (2018, background): “Related to this, Dawson et al. (2014) previously suggested that a high number of citations is not a necessary synonym of high-quality research.”
Where is research on massive open online courses headed? A data analysis of the MOOC research initiative (2014, methodology): “Vol 15 | No 5 Creative Commons Attribution 4.0 International License Nov/14 In this study, we followed a method for citation network analysis suggested by Dawson et al. (2014) in their citation network analysis of the field of learning analytics.”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, background): “two commonly reported tasks in the learning analytics and educational data mining literature (Dawson et al., 2014).”
Let’s not forget: Learning analytics are about learning (2014, methodology): “The Gašević et al. (2014) analysis of the results of a regression model created by combining data from nine undergraduate courses in an Australian university showed that only three variables – number of logins and number of operations performed on discussion forums and resources were significant…”
Literature review on the ‘Smart Factory’ concept using bibliometric tools (2017, background): “As Dawson et al. (2014) argued, notwithstanding highly cited papers are not necessarily presenting impactful and high quality research; it is realistic to assume that a large number of received citations reflects a high level of impact and quality.”