The trends of the learning analytics community being presented in this paper are in terms of authors, their affiliation and geographical location. Thus the most influential authors, institutes, and countries who have been actively contributing to this field are brought out. In addition, this paper identifies collaborations among authors, institutes, and countries. The paper also tries to explore the research themes followed by the learning analytics community.
Unique edges (edge is loop for single author articles & straight line otherwise) Edges with duplicates (i.e., edge weight is greater than 1) (These edges show joint authorship in more than one publication )
Connected components (authors forming a cluster based on authorship)
2008 74 100 17 2009 79 106 18 124 1 31 22 0 7 2010 151 208 50 258 3 61 38 3 15 2011 193 251 42 293 10 75 53 8 29 2012 Total 281 435 48 483 8 96 79 7 22 623 938 337 1275 26 27 140 14 113
Collaboration as defined in Oxford dictionary [8] is the „action of working with someone to produce something‟ and in current context it represents co-authorship of an article by two or more researchers. This term can be extended to institutes and even countries and hence extended collaboration patterns will be extracted between and within institutes and countries respectively. Table 2 shows that there have been 938 pairs of authors who collaborated just once (this number includes single author articles - since in that case a self-loop serves as an edge to itself). Alternatively, it can be stated that 73.57% of all articles have been written by the authors who have collaborated just once. It could either mean that new collaborations are forming or that the authors published just once and then they started working in other research areas, with other authors or they started targeting other venues. Therefore, initiatives such as LAK Data challenge will attract more researchers towards this field and hence may help in further growth and development of authorship networks.
Diversity in this context is the count of distinct researchers – a given author may have worked with. Table 4 aims at identifying the contributors who have worked with most diverse group of authors e.g., K.R. Koedinger has worked with 34 distinct authors and Ryan Baker has worked with 25 distinct authors. We also extracted the graph of these top contributors (based on degree) i.e., a graph which includes these top authors and all of their collaborators; and it was found that this new graph consists of 128 authors (roughly 21% of the total authors). This percentage shows the significance of the top authors towards EDM, LAK, JETS and in general towards learning analytics.
Next, the geographical analysis of this dataset is presented which
aims to explore the countries that have been extending this field
especially through contributions to the venues: EDM, LAK and
JETS. There have been contributions from 41 different countries.
For extracting this information, all aliases of a country‟s name
were merged e.g., Netherland, Netherlands, The_Netherlands etc.
were all merged together. The top countries that have had
international collaborations are provided in table 5. Clearly, USA
and UK are on top of the list. To illustrate the collaboration
patterns between countries figure 1 is drawn using „NetDraw‟. In
this figure an edge between two countries depicts the
coauthorship between the researchers from these countries. The edge
width (also represented by a number) shows the strength of such
collaboration. Also, different symbols have been used for different
nodes based on their „betweeness‟ values. „Betweenness
centrality‟ is the “number of times a node acts as a bridge along
the shortest path between two other nodes” [9]. Clearly, USA, UK
and Germany are on top of this list based on degree and centrality
measures. It is apparent that most of the nodes have „betweenness‟
value of zero as depicted with a „+‟ symbol. It indicates the
peripheral nature of these nodes and thus depicts the birth or
growth of this field – in that newer nodes are being added and the
graph is currently sparse. Figure 2 illustrates geographical
diversity of collaborators. The smaller circles show lesser
diversity in terms of collaboration (with researchers from other
countries). Similarly, larger circles are indicative of the countries
whose researchers have more diverse group of co-authors (from
across the world). In this figure a small table at the bottom depicts
the count of papers from each continent. Thus it brings out the
most active region for research in the area of learning analytics.
Clearly, North America and Europe are at the top of this list
(complete geographical mapping is available at [
Next, the institutional affiliation of authors was analyzed and it was found that there have been contributions from 200 different institutes world-wide. The ranking of the top few institutes in terms of collaboration with other institutes is provided in table 6. The term degree represents count of unique institutes that a given institute may have worked with. This term can be influenced by both the „article counts‟ and the „coauthor counts‟. Table 7 provides the institutes with highest count of intra-institute collaboration and table 8 provides the „institute – pairs‟ that have had highest collaboration. Such analysis is beneficial to research institutes and organizations so that they may collaborate and extend further studies in the field of learning analytics. Figure 3 illustrates trends of collaboration between institutes.
Carnegie Mellon University University of Cordoba Stanford University Fraunhofer Institute for Applied Information Technology Dept. Computer wetenschappen, KU Leuven Worcester Polytechnic Institute Open University of the Netherlands University of Pittsburgh
In order to track the research themes being followed by learning
analytics society and to see their emergence over time, the
authors conducted a keyword based analysis. The information
for this analysis has been extracted from the keyword (subject)
section of the data provided by Society for Learning Analytics
Research (SoLAR) website [
In this paper the data of past five years of publications related to learning analytics are analyzed. The trends show increasing number of authors and more collaboration between authors as well as institutes. Geographical analysis of authors shows that scholars from different countries have been collaborating and contributing towards this field. Top authors, collaborators, and institutes are identified in this paper. The authors also attempted to bring out the research themes followed by the learning analytics community based on the frequency of the usage of keywords.
The authors plan to extend this study based on author‟s disciplinary diversity and on the association between authors and their explored research areas within learning analytics.
Claremont Graduate University University of Belgrade Northern Illinois University Hochschule fur Wirtschaft und Recht Beuth Hochschule fur Technik Berlin Universidade Federal de Alagoas Fraunhofer Institute for Applied Information Technology
Carnegie Mellon University University of Memphis Simon Fraser University University of Memphis Hochschule fur Technik und Wirtschaft Hochschule fur Technik und Wirtschaft Carnegie Mellon University Saarland University Edge weight [8] OXFORD DICTIONARIES, 2013. Oxford dictionary collaboration. http://oxforddictionaries.com/definition/english/collaborati on [9] WIKIPEDIA, 2013. Wikipedia centrality. http://en.wikipedia.org/wiki/Betweenness#Betweenness_ce ntrality [10] Nawaz, S., Strobel, J., 2013. IEEE Transactions on Education – authorship and content analysis, under preparation [11] HERMETIC, 2013. Hermetic Word Frequency Counter. http://www.hermetic.ch/wfc/wfc.htm Figure 4: Keyword analysis for research theme extraction