We were asked to review this article in order to expand our understanding of social learning analytics. Rather than examining interactions by students in an online learning environment such as an institutional LMS, however, or another trail of start-up, demographic, disciplinary, course, log in, data, commonly analyzed, we focussed on research network analytics of some of the leaders in the field. A very different subject to what we had been looking at previously. Another usual thing was the way we approached the reading. We begin by looking at the abstract of this paper: Dawson et al. (2014), 'Current state and future trends: a citation network analysis of the learning analytics field' and noted the aims of the paper. That was the end of the conventional approach to reading, as we then were asked to skip to the fourth section of the paper that listed its practical implications (Section 4.3). These were that the analysis ...
provides an understanding of how key papers, thematics, and authors influencing a field emerge
raises awareness about the structure and attributes of knowledge in a discipline and the development of curriculum in the growing number of academic programs that include learning analytics as a topic
promotes under-represented groups and research methods to the learning analytics community
fosters the development of empirical work and decreased reliance on founding, overview and conceptual papers
improves connections to sister organizations such as the International Educational Data Mining Society
(Dawson et al 2014, 238)
The interesting thing about the paper was its crossover between an example of learning analytics and a paper about learning analytics.
Some of its figures and tables thankfully were understandable even for a non-expert. Table 1 identifies the ten most-cited papers in the field. Interestingly, the numbers of citations in the learning analytics literature ranged between 10 and 16, whilst the Google Scholar citation counts vastly differed. This can be attributed to the equal currency placed on both old and new publications among specialist members of the field compared with researchers from a much wider range of fields and interests among the Google scholar audience.
In sum, the article is a reminder of how much more complex the learning analytics landscape is than a means to improve teaching and learning. In this case, it was used to aid in a complex understanding of how research gains prominence. A systemic and integrated response is required for the approach to do justice to its subject. As the authors note: 'while it is helpful to note that (more active) students...perform better than their less active peers, this information is not suitable for developing a focused response to poor-performing students. (p. 231)'
A more in-depth reading of the article would certainly have made the basis of that point much clearer to the reader. However, what I did gain from reading a paper in this way was an impression I could take with me to other readings and to my general knowledge of the breadth of the learning analytics field.
Dawson, S., Gašević, D., Siemens, G. and Joksimovic, S. (2014) Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 231-240) [Online]. Available at: file:///C:/Users/robin/Desktop/Current%20State%20and%20Future%20Trends%20A%20Citation%20Network%20Analysis%20of%20the%20Learning%20Analytics%20Field.pdf (Accessed July 20 2020).