Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.
Definition Although a majority of Learning Analytics literature has started to adopt the aforementioned definition, the definition and aims of Learning Analytics are still contested.
Learning analytics as a prediction model One earlier definition discussed by the community suggested that Learning Analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning.Siemens, George. "What Are Learning Analytics?" Elearnspace, August 25, 2010.
Learning analytics as a generic design framework Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. It uses a general morphological analysis (GMA) to divide the domain into six "critical dimensions".
Learning analytics as data-driven decision making The broader term "Analytics" has been defined as the science of examining data to draw conclusions and, when used in decision-making, to present paths or courses of action. From this perspective, Learning Analytics has been defined as a particular case of Analytics, in which decision-making aims to improve learning and education. During the 2010s, this definition of analytics has gone further to incorporate elements of operations research such as decision trees and strategy maps to establish predictive models and to determine probabilities for certain courses of action.
Learning analytics as an application of analytics Another approach for defining Learning Analytics is based on the concept of Analytics interpreted as the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data.Powell, Stephen, and Sheila MacNeill. Institutional Readiness for Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, December 2012. . From this point of view, Learning Analytics emerges as a type of Analytics (as a process), in which the data, the problem definition and the insights are learning-related.
In 2016, a research jointly conducted by the New Media Consortium (NMC) and the EDUCAUSE Learning Initiative (ELI) -an EDUCAUSE Program- describes six areas of emerging technology that will have had significant impact on higher education and creative expression by the end of 2020. As a result of this research, Learning analytics was defined as an educational application of web analytics aimed at learner profiling, a process of gathering and analyzing details of individual student interactions in online learning activities.
Learning analytics as an application of data science
In 2017, Gašević, Kovanović, and Joksimović proposed a consolidated model of learning analytics. The model posits that learning analytics is defined at the intersection of three disciplines: data science, theory, and design. Data science offers computational methods and techniques for data collection, pre-processing, analysis, and presentation. Theory is typically drawn from the literature in the learning sciences, education, psychology, sociology, and philosophy. The design dimension of the model includes: learning design, interaction design, and study design. In 2015, Gašević, Dawson, and Siemens argued that computational aspects of learning analytics need to be linked with the existing educational research in order for Learning Analytics to deliver its promise to understand and optimize learning.
Learning analytics versus educational data mining Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics,G. Siemens, D. Gasevic, C. Haythornthwaite, S. Dawson, S. B. Shum, R. Ferguson, E. Duval, K. Verbert, and R. S. J. D. Baker. Open Learning Analytics: an integrated & modularized platform. 2011. the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that "...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior". They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though BrooksC. Brooks. A Data-Assisted Approach to Supporting Instructional Interventions in Technology Enhanced Learning Environments. PhD Dissertation. University of Saskatchewan, Saskatoon, Canada 2012. questions whether this distinction exists in the literature. Brooks instead proposes that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems (e.g. learning content management systems).
Regardless of the differences between the LA and EDM communities, the two areas have significant overlap both in the objectives of investigators as well as in the methods and techniques that are used in the investigation. In the MS program offering in learning analytics at Teachers College, Columbia University, students are taught both EDM and LA methods.
Historical contributions Learning Analytics, as a field, has multiple disciplinary roots. While the fields of artificial intelligence (AI), statistical analysis, machine learning, and business intelligence offer an additional narrative, the main historical roots of analytics are the ones directly related to human interaction and the education system. More in particular, the history of Learning Analytics is tightly linked to the development of four Social Sciences' fields that have converged throughout time. These fields pursued, and still do, four goals:
Definition of Learner, in order to cover the need of defining and understanding a learner. Knowledge trace, addressing how to trace or map the knowledge that occurs during the learning process. Learning efficiency and personalization, which refers to how to make learning more efficient and personal by means of technology. Learner – content comparison, in order to improve learning by comparing the learner's level of knowledge with the actual content that needs to master.()
A diversity of disciplines and research activities have influenced in these 4 aspects throughout the last decades, contributing to the gradual development of learning analytics. Some of most determinant disciplines are Social Network Analysis, User Modelling, Cognitive modelling, Data Mining and E-Learning. The history of Learning Analytics can be understood by the rise and development of these fields.
Social Network Analysis
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Social network analysis is prominent in Sociology, and its development has had a key role in the emergence of Learning Analytics.
One of the first examples or attempts to provide a deeper understanding of interactions is by Austrian-American Sociologist Paul Lazarsfeld. In 1944, Lazarsfeld made the statement of "who talks to whom about what and to what effect". That statement forms what today is still the area of interest or the target within social network analysis, which tries to understand how people are connected and what insights can be derived as a result of their interactions, a core idea of Learning Analytics.
Citation analysis
American linguist Eugene Garfield was an early pioneer in analytics in science. In 1955, Garfield led the first attempt to analyse the structure of science regarding how developments in science can be better understood by tracking the associations (citations) between articles (how they reference one another, the importance of the resources that they include, citation frequency, etc). Through tracking citations, scientists can observe how research is disseminated and validated. This was the basic idea of what eventually became a "page rank", which in the early days of Google (beginning of the 21st century) was one of the key ways of understanding the structure of a field by looking at page connections and the importance of those connections. The algorithm PageRank -the first search algorithm used by Google- was based on this principle. American computer scientist Larry Page, Google's co-founder, defined PageRank as "an approximation of the importance" of a particular resource. Educationally, citation or link analysis is important for mapping knowledge domains.
The essential idea behind these attempts is the realization that, as data increases, individuals, researchers or business analysts need to understand how to track the underlying patterns behind the data and how to gain insight from them. And this is also a core idea in Learning Analytics.
Digitalization of Social network analysis
During the early 1970s, pushed by the rapid evolution in technology, Social network analysis transitioned into analysis of networks in digital settings.
Milgram's 6 degrees experiment. In 1967, American social psychologist Stanley Milgram and other researchers examined the average path length for social networks of people in the United States, suggesting that human society is a small-world-type network characterized by short path-lengths. Weak ties. American Sociologist Mark Granovetter's work on the strength of what is known as weak ties; his 1973 article "The Strength of Weak Ties" is one of the most influential and most cited articles in Social Sciences. Networked individualism. Towards the end of the 20th century, Sociologist Barry Wellman's research extensively contributed the theory of social network analysis. In particular, Wellman observed and described the rise of "networked individualism" – the transformation from group-based networks to individualized networks.Barry Wellman, "Physical Place and Cyber Place: The Rise of Networked Individualism." International Journal of Urban and Regional Research 25,2 (June, 2001): 227-52.