词条 | Learning analytics |
释义 |
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.[1] A related field is educational data mining. DefinitionAlthough 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 modelOne 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.[4] But this definition has been criticised by George Siemens[5]{{Primary source inline|date=October 2017}} and Mike Sharkey.[6]{{Primary source inline|date=October 2017}} Learning Analytics defined as a generic design frameworkA 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".[7] The "What - Who - Why - How" ApproachIn 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:
Learning Analytics as a data-driven decision makingThe 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.[10] From this perspective, Learning Analytics has been defined as a particular case of Analytics, in which decision making aims to improve learning and education.[11] 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.[10] Learning Analytics as a process based on educational data and statistical modelAnother 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.[12][13] 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 aspectsIn 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.[14] Learning Analytics as an application of Web AnalyticsIn 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.[15] Differentiating learning analytics and educational data miningDifferentiating 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,[16] 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".[17] They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks[18] questions whether this distinction exists in the literature. Brooks[18] 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.[19] HistoryLearning 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.[20] 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:
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.[20] Social Network Analysis: historical contributionsSocial network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory.[21] 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.{{Citation needed|date=March 2019}}]] Social network analysis is prominent in Sociology, and its development has had a key role in the emergence of Learning Analytics. The relevance of interactionsOne 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".[22] 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.[20] Citation analysisAmerican 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.[23][24] American computer scientist Larry Page, Google's co-founder, defined PageRank as “an approximation of the importance” of a particular resource.[25] Educationally, citation or link analysis is important for mapping knowledge domains.[20] 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.[20] Digitalization of Social network analysisDuring the early 1970s, pushed by the rapid evolution in technology, Social network analysis transitioned into analysis of networks in digital settings.[20]
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.[32] ({{Cite AV media|url=https://www.youtube.com/watch?v=KqETXdq68vY|title=Intro to Learning Analytics|date=2013-03-17|last=Siemens|first=George|type=|language=English|series=LAK13 open online course for University of Texas at Austin & Edx|access-date=2018-11-01|minutes=11}}) 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.[20] User modelling: historical contributionsThe 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".[33] This is a central idea not only educationally but also in general web use activity, in which personalization is an important goal.[20] User modelling has become important in research in human-computer interactions as it helps researchers to design better systems by understanding how users interact with software.[34] Recognizing unique traits, goals, and motivations of individuals remains an important activity in learning analytics.[20]Adaptive hypermedia{{See also|Adaptive hypermedia#History}}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.[20] 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.[35] 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.[36]Education/cognitive modelling: historical contributionsEducation/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.[37]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.[38] 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.[20][39][40] Data Mining: historical contributionsData 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.[41] The EDM community has been heavily influenced by the vision of early KDD.[20]E-learning: historical contributions{{Main|E-learning}}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.[42][43][44] 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.[20][45] Other contributionsIn 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:[46]
History of learning analytics in higher educationThe 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."[47] Analytic methodsMethods for learning analytics include:
ApplicationsLearning Applications can be and has been applied in a noticeable number of contexts. Types of applications per organisational business objectivesThere is a broad awareness of analytics across educational institutions for various stakeholders,[10] but that the way learning analytics is defined and implemented may vary, including:[13]
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.[13] General purposesAnalytics have been used for:
Software{{See also|Social network analysis software|Student information system}}Much of the software that is currently used for learning analytics duplicates functionality of web analytics software, but applies it to learner interactions with content. Social network analysis tools are commonly used to map social connections and discussions. Some examples of learning analytics software tools include:
Ethics and privacyThe ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for learning analytics,[7][59][60] with concerns raised regarding:
As Kay, Kom and Oppenheim point out, the range of data is wide, potentially derived from:[62]
Thus the legal and ethical situation is challenging and different from country to country, raising implications for:[62]
In some prominent cases like the inBloom disaster,[63] even full functional systems have been shut down due to lack of trust in the data collection by governments, stakeholders and civil rights groups. Since then, the learning analytics community has extensively studied legal conditions in a series of experts workshops on "Ethics & Privacy 4 Learning Analytics" that constitute the use of trusted learning analytics.[64]{{Primary source inline|date=October 2017}} Drachsler & Greller released an 8-point checklist named DELICATE that is based on the intensive studies in this area to demystify the ethics and privacy discussions around learning analytics.[65]
It shows ways to design and provide privacy conform learning analytics that can benefit all stakeholders. The full DELICATE checklist is publicly available.[66] Open learning analyticsChatti, Muslim and Schroeder[67] note that the aim of open learning analytics (OLA) is to improve learning effectiveness in lifelong learning environments. The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model.[8] See also
Further readingFor general audience introductions, see:
Notes{{Notelist}}References1. ^{{cite web | url=https://tekri.athabascau.ca/analytics | title=Call for Papers of the 1st International Conference on Learning Analytics & Knowledge (LAK 2011) | accessdate=12 February 2014}} 2. ^Siemens, G., Connectivism: A learning theory for the digital age, International Journal of Instructional Technology and Distance Learning 2 (10), 2005. 3. ^1 {{Cite book|title=Knowing knowledge|last=George.|first=Siemens|date=2006|publisher=[publisher not identified]|isbn=9781430302308|location=[Place of publication not identified]|pages=|oclc=123536429}} 4. ^Siemens, George. "What Are Learning Analytics?" Elearnspace, August 25, 2010. http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/ 5. ^"I somewhat disagree with this definition—it serves well as an introductory concept if we use analytics as a support structure for existing education models. I think learning analytics—at an advanced and integrated implementation—can do away with pre-fab curriculum models." George Siemens in the Learning Analytics Google Group discussion, August 2010 6. ^"In the descriptions of learning analytics we talk about using data to "predict success". I've struggled with that as I pore over our databases. I've come to realize there are different views/levels of success." Mike Sharkey, Director of Academic Analytics, University of Phoenix, in the Learning Analytics Google Group discussion, August 2010 7. ^1 {{cite journal|last=Greller|first=Wolfgang|last2=Drachsler|first2=Hendrik|date=2012|title=Translating Learning into Numbers: Toward a Generic Framework for Learning Analytics.|url=https://pdfs.semanticscholar.org/d1bd/219962defaeb326c3b51fb4fb1086c5b7b28.pdf|journal=Educational Technology and Society|volume=15|issue=3|pages=42–57|doi=|pmid=|access-date=2018-11-01|via=|accessdate=}} 8. ^1 Mohamed Amine Chatti, Anna Lea Dyckhoff, Ulrik Schroeder and Hendrik Thüs (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL), 4(5/6), pp. 318-331. 9. ^Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10. http://eleed.campussource.de/archive/10/4035 10. ^1 2 {{Cite journal|last=Picciano|first=Anthony G.|date=2012|title=The Evolution of Big Data and Learning Analytics in American Higher Education.|url=https://www.researchgate.net/publication/258206917|format=pdf|journal=Journal of Asynchronous Learning Networks|volume=16|pages=9–20}} 11. ^{{Cite journal|last=Elias|first=Tanya|date=January 2011|title=Learning Analytics: Definitions, Processes and Potential|url=https://pdfs.semanticscholar.org/732e/452659685fe3950b0e515a28ce89d9c5592a.pdf|journal=Unpublished Paper|language=English|volume=|pages=19|access-date=2018-11-02|lay-url=https://www.researchgate.net/publication/327220025_Learning_Analytics_Definitions_Processes_and_Potential|via=}} 12. ^{{cite journal|url=https://pdfs.semanticscholar.org/98ab/3fbde3c583d30adf8e660a30e840ebaf2bf0.pdf|title=What is Analytics? 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