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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. A related field is educational data mining.

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 defined 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. But this definition has been criticised by George Siemens and Mike Sharkey.

Learning Analytics defined as a generic design framework
A more holistic view than a mere definition was provided by the framework of learning analytics by Dr. Wolfgang Greller and Dr. Hendrik Drachsler, proposing 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".

The "What - Who - Why - How" Approach
In 2012, a systematic overview on learning analytics and its key concepts was provided by Professor Mohamed Chatti and colleagues through a reference model based on four dimensions, namely:


 * data, environments, context (what?),
 * stakeholders (who?),
 * objectives (why?), and
 * methods (how?).

Learning Analytics as a 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 decade of 2010, this definition of analytics has gone further, however, 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 a process based on educational data and statistical model
Another approach for defnining 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. 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.

Learning Analytics definition to include computational aspects
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 is to deliver to its promise to understand and optimize learning.

Learning Analytics as an application of Web Analytics
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.

History
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:


 * 1) Definition of Learner, in order to cover the need of defining and understanding a learner.
 * 2) Knowledge trace, addressing how to trace or map the knowledge that occurs during the learning process.
 * 3) Learning efficiency and personalization, which refers to how to make learning more efficient and personal by means of technology.
 * 4) 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 diciplines are Social Network Analysis, User Modeling, 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: historical contributions
]Social network analysis is prominent in Sociology, and its development has had a key role in the emergence of Learning Analytics.

The relevance of interactions
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 (begining 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 thise 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.


 * 1) 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.
 * 2) 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.
 * 3) 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 Modeling: historical contributions
The main goal of user modeling 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 customization and personalization is an important goal.

User modeling has become important in research in human-computer interactions as it helps researchers to design better systems by understanding how users interact with software. Recognizing unique traits, goals, and motivations of individuals remains an important activity in learning analytics.

Adaptive hypermedia
Personalization and adaptation of learning content is an important present and future direction of learning sciences, and its history within education has contributed to the development of learning analytics.

Hypermedia is a nonlinear medium of information that includes graphics, audio, video, plain text and hyperlinks. The term was first used in a 1965 article written by American Sociologist Ted Nelson. Adaptive hypermedia builds on user modeling 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: historical contributions
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 modeling has contributed to the rise in popularity of intelligent or cognitive tutors. Once cognitive processes can be modeled, 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.

Data Mining: historical contributions
Data Mining, in particular Knowledge Discovery in Databases (KDD) has been a research interest since at least the early 1990s. As with analytics today, KDD was concerned with the development of methods and techniques for making sense of data. The EDM community has been heavily influenced by the vision of early KDD.

E-learning: historical contributions
The growth of online learning during the 1990s, 2000s y 2010s, 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.

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:


 * 1) Statistics, which are a well established means to address hypothesis testing.
 * 2) 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.
 * 3) Web analytics, tools such as Google analytics report on web page visits and references to websites, brands and other keyterms 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.).
 * 4) 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.
 * 5) 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.
 * 6) 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).

History of learning analytics in higher education
The first graduate program focused specifically on learning analytics was created by Ryan S. Baker and launched in the Fall 2015 semester at Teachers College, Columbia University. The program description states that"'(...)data about learning and learners are being generated today on an unprecedented scale. The fields of learning analytics (LA) and educational data mining (EDM) have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators. As one of world's leading teaching and research institutions in education, psychology, and health, we are proud to offer an innovative graduate curriculum dedicated to improving education through technology and data analysis.'"

Applications
Learning Applications can be and has been appied in a noticeable number of contexts.

Types of applications per organisational business objectives
There is a broad awareness of analytics across educational institutions for various stakeholderst, but that the way learning analytics is defined and implemented may vary, including:


 * 1) for individual learners to reflect on their achievements and patterns of behaviour in relation to others. Particularly, the following areas can be set out for measuring, monitoring, analyzing and changing to optimize student performance:
 * 2) Monitoring individual student performance
 * 3) Disaggregating student performance by selected characteristics such as major, year of study, ethnicity, etc.
 * 4) Identifying outliers for early intervention
 * 5) Predicting potential so that all students achieve optimally
 * 6) Preventing attrition from a course or program
 * 7) Identifying and developing effective instructional techniques
 * 8) Analyzing standard assessment techniques and instruments (i.e. departmental and licensing exams)
 * 9) Testing and evaluation of curricula.
 * 10) as predictors of students requiring extra support and attention;
 * 11) to help teachers and support staff plan supporting interventions with individuals and groups;
 * 12) for functional groups such as course teams seeking to improve current courses or develop new curriculum offerings; and
 * 13) for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.

Some motivations and implementations of analytics may come into conflict with others, for example highlighting potential conflict between analytics for individual learners and organisational stakeholders.

General purposes
Analytics have been used for:


 * Prediction purposes, for example to identify "at risk" students in terms of drop out or course failure.
 * Personalization & adaptation, to provide students with tailored learning pathways, or assessment materials.
 * Intervention purposes, providing educators with information to intervene to support students.
 * Information visualization, typically in the form of so-called learning dashboards which provide overview learning data through data visualisation tools.