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. 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. 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. •
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. During the first decade of the century, Professor
Caroline Haythornthwaite explored the impact of
media type on the development of
social ties, observing that
human interactions can be analyzed to gain novel insight not from
strong interactions (i.e. people that are strongly related to the subject) but, rather, from
weak ties. This provides Learning Analytics with a central idea: apparently un-related data may hide crucial information. As an example of this phenomenon, an individual looking for a job will have a better chance of finding new information through weak connections rather than strong ones. ('''') Her research also focused on the way that different
types of media can impact the
formation of networks. Her work highly contributed to the development of
social network analysis as a field. Important ideas were inherited by Learning Analytics, such that a range of metrics and approaches can define the importance of a particular node, the value of
information exchange, the way that clusters are connected to one another, structural gaps that might exist within those networks, etc.
User modelling The main goal of
user modelling is the customization and
adaptation of systems to the user's specific needs, especially in their
interaction with computing systems. The importance of computers being able to respond individually to into people was starting to be understood in the decade of 1970s. Dr
Elaine Rich in 1979 predicted that "computers are going to treat their users as individuals with distinct personalities, goals, and so forth". This is a central idea not only educationally but also in general web use activity, in which
personalization is an important goal. Recognizing unique traits, goals, and motivations of individuals remains an important activity in learning analytics.
Adaptive hypermedia builds on
user modelling by increasing personalization of content and interaction. In particular, adaptive hypermedia systems build a model of the goals, preferences and knowledge of each user, in order to adapt to the needs of that user. From the end of the 20th century onwards, the field grew rapidly, mainly due to that the
internet boosted research into adaptivity and, secondly, the accumulation and consolidation of research experience in the field. In turn, Learning Analytics has been influenced by this strong development.
Education/cognitive modelling Education/cognitive modelling has been applied to tracing how learners develop knowledge. Since the end of the 1980s and early 1990s, computers have been used in education as learning tools for decades. In 1989,
Hugh Burns argued for the adoption and development of
intelligent tutor systems that ultimately would pass three levels of "intelligence":
domain knowledge, learner knowledge evaluation, and
pedagogical intervention. During the 21st century, these three levels have remained relevant for researchers and educators. In the decade of 1990s, the academic activity around cognitive models focused on attempting to develop systems that possess a computational model capable of solving the problems that are given to students in the ways students are expected to solve the problems. Cognitive modelling has contributed to the rise in popularity of intelligent or
cognitive tutors. Once cognitive processes can be modelled, software (tutors) can be developed to support learners in the learning process. The research base on this field became, eventually, significantly relevant for learning analytics during the 21st century.
Other contributions In a discussion of the history of analytics,
Adam Cooper highlights a number of communities from which learning analytics has drawn techniques, mainly during the first decades of the 21st century, including: •
Statistics, which are a well established means to address hypothesis testing. •
Business intelligence, which has similarities with learning analytics, although it has historically been targeted at making the production of reports more efficient through enabling data access and summarising performance indicators. •
Web analytics, tools such as
Google Analytics report on web page visits and references to websites, brands and other key terms across the internet. The more "fine grain" of these techniques can be adopted in learning analytics for the exploration of student trajectories through learning resources (courses, materials, etc.). •
Operational research, which aims at highlighting design optimisation for maximising objectives through the use of mathematical models and statistical methods. Such techniques are implicated in learning analytics which seek to create models of real world behaviour for practical application. •
Artificial intelligence methods (combined with
machine learning techniques built on
data mining) are capable of detecting patterns in data. In learning analytics such techniques can be used for
intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as "suggested course" systems modelled on
collaborative filtering techniques. •
Information visualization, which is an important step in many analytics for
sensemaking around the data provided, and is used across most techniques (including those above). == Learning analytics programs==