Learning Analytics Research in Relation to Educational Technology: Capturing Learning Analytics Contributions with Bibli
- PDF / 606,985 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 39 Downloads / 208 Views
ORIGINAL PAPER
Learning Analytics Research in Relation to Educational Technology: Capturing Learning Analytics Contributions with Bibliometric Analysis Tanner Phillips 1
&
Gamze Ozogul 1
# Association for Educational Communications & Technology 2020
Abstract In this study the authors conducted an empirical, bibliometric analysis of current literature in learning analytics. The authors performed a citation network analysis and found three dominant clusters of research. A qualitative thematic review of publications in these clusters revealed distinct context, goals, and topics. The largest cluster focused on predicting student success and failure, the second largest on using analytics to inform instructional design, and the third on concerns in implementing learning analytics systems. The authors suggest that further collaboration with educational technology researchers and practitioners may be necessary for learning analytics to reach its interdisciplinary goal. The authors also note that learning analytics currently does not often take place in K-12 settings, and that the burden of creating learning interventions still seemed to reside mainly with practitioners. Keywords Bibliometrics . Citation network analysis . Educational technology . Learning analytics
Learning analytics is the use of student generated digital data to improve learning and teaching (Sclater et al. 2016). There has been massive growth in the field of learning analytics over the last decade due to the new availability of student generated digital data (Viberg et al. 2018). However, due to factors such as government regulation and lack of uniformity in systems of data collection, learning analytics systems have been slow to penetrate the formal educational landscape (Manyika 2011). Learning analytics research also originally grew out of collaborations between computer science and learning science researchers (Dawson et al. 2014), and recent reviews of the literature are designed with current learning analytics researchers in mind (Seimens 2013). These and other factors suggest that there is not an easy point-of-entry for researchers and practitioners in related fields who wish to explore the discipline of learning analytics.
* Tanner Phillips [email protected] 1
Indiana University Instructional Systems Technology, 2222 Wright Education Building, 107 S Indiana Ave, Bloomington, IN 47405, USA
This lack of easy entry is not unique to learning analytics. Fields that were contrived specifically to be interdisciplinary can be successful in creating a discovering new and novel theories that would otherwise have gone unnoticed. However, maintaining the cohesion of these fields is not as simple a task. Cognitive science shares many similarities with learning analytics as both were explicitly defined and organized as interdisciplinary fields (Miller 2003), and both combine social science and computer science to create rigorous, quantitative, scientific theories (Gardner 1987). While cognitive science produced an explosion of new theory in the 70’s an
Data Loading...
