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Learning Analytics in Higher Education


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Presents an overview of the learning analytics field touching on the status of the technology, the challenges it faces, the arrival of predictive analytics to education and the best approach towards a successful implementation.

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Learning Analytics in Higher Education

  1. 1. 1 Learning Analytics Your next movement towards the future of education Jose A Omedes
  2. 2. 2 What raises interest in LA area? Analysis of around 300 posts on the topic 20.11% 11.41% 9.24% 8.70%8.15% 4.89%
  3. 3. 3 Index • Defining Learning Analytics • The three elements of analytics: data, analysis and action • Learning Analytics maturity and the predictive bridge. • Learning Analytics benefits and experiences • A new learning era • Implementing learning analytics • Learning analytics ethics • Key take aways
  4. 4. 4 Learning Analytics Defined “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” International conference on learning analytics
  5. 5. 5 Learning Analytics Defined 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 DATA Basic asset. Raw material to be transformed into analytical insights. ANALYSIS Process to add intelligence to data using algorithms. ACTION Critical step towards achieving the purpose: Understanding & optimizing learning International conference on learning analytics
  6. 6. 6 Learning Analytics and EDM Educational Data Mining (EDM) EDM focuses on the development of methods for exploring the unique types of data that come from an educational context. […] the objective of data mining in education is largely to improve learning […] Handbook of educational data mining Educational Data Mining (EDM) ≈ Learning Analytics
  7. 7. 7 LA and EDM on Google
  8. 8. 8 Data Types Internal External PROVIDED / OBSERVED Learning Analytics INFERRED DERIVED Learning Analytics Algorithms PROVIDED: Consciously given OBSERVED: Recorded automatically DERIVED: Produced from other data INFERRED: Produced using analytics Usually based on correlation between data sets
  9. 9. 9 Data Sources Demographic Data Not Sensitive Sensitive Name, birthdate, Sex Ethnicity, Disability, Scholarship Academic Data Prior Performance Current Performance Learner Content Maths: A, Physics: B, Electromagnetism: B Course 1: Assignment 1: 89% Assignment 2: 56% Course 2: Assignment 1: 35%, Assignment 2: 64% Essay 1, Group Report B, Chat 1 … Learning Activity Data Activity Records 2017/10/02- 10:50 Logged into LMS 2017/10/02- 11:50 Accessed Library Catalog 2017/10/02- 12:00 Check out library book “Human body anatomy” Educational Context Data Context Info Course 1: start: 2017/09/02, duration: 10 weeks, instructor: Allan Green Course 2: start: 2017/08/05, duration: 15 weeks, instructor: Mike Brown External Data Social Account Profiles Other apps info Facebook, Twitter, Google + eBook apps, xAPI enabled apps …
  10. 10. 10 Data Sources SIS (Student Information Systems) LMS / LRS (Learning Management System) Other Internal Systems External Systems Learning Analytics go beyond the LMS !!!
  11. 11. 11 Analysis Process of obtaining insights from data based on a set of statistical and machine learning based algorithms ANALYSIS Data Learning Analytics
  12. 12. 12 Analytics maturity model analysis complexity (imposes demands on data: volume, type, timeframe, etc.) Diagram Source:
  13. 13. 13 Predictive Analytics Bridge Descriptive Diagnostic Predictive Algorithms Data Reactive Understand the past Proactive Influence the present
  14. 14. 14 Action Is the overall target of the learning analytics process No action = Failure Having in place the internal processes that lead to action is critical CultureLeadership
  15. 15. 15 Is it worth? Benefits Reduce drop out rates Increase learners’ performance Targeted proactive tutoring Increase retention Targeted proactive tutoring Offer personalized learning experiences Adaptive Learning / Adaptive content Understand content consumption patterns & quality issues Improve content & course quality Instructional design
  16. 16. 16 Is it worth? Benefits Cost efficient allocation Understand which resources work and which don`t Data-driven investment decisions Identify and promote student success factors Create student structured pathways towards graduation Proactively drive success Curriculum design
  17. 17. 17 Source: Does it work?
  18. 18. 18 Does it work? Source: Case studies show: • Validity of the predictive models applied to learning analytics systems • Interventions with at-risk students are effective • There are other benefits to taking a data-driven approach
  19. 19. 19 A new learning era … or not? Learning Analytics: The coming third wave Malcolm Brown, Director, EDUCAUSE Learning Initiative The Predictive Learning Analytics Revolution EDUCASE ECAR Working Group Paper Adaptive Learning Holds Promise for the Future of Higher Education BARNES6NOBLE at EDUCATION DIVE
  20. 20. 20 Reality … The world is more and more data-driven … Education is no exception. “Learning analytics” is an important tool to improve education and to make high education institutions more competitive. “Learning analytics” are successful only if there is action as a result of its implementation. Institutions must cross the predictive analytics bridge to benefit from a new way of driving students success.
  21. 21. 21 The decision automation leap Prescriptive Prescriptive Human supported Fully automated Learning Analytics
  22. 22. 22 The decision automation leap Impressive and completely automated Really impressive !!! 80% Unknown … Never forget the human factor in education !!!
  23. 23. 23 Implementing Learning Analytics How do I implement my learning analytics project?
  24. 24. 24 Implementing Learning Analytics 1 Start small Duration Scope 2 Don’t focus on technology Focus on specific problems you want to address 3 Go after quick wins Show there are positive outcomes and possible impact 4 Involve from day one all the critical stakeholders Don’t forget the people that would use the technology in the end
  25. 25. 25 Learning Analytics Readiness Understand whether institutions are ready for learning analytics from multiple dimensions LEARNING ANALYTICS READINESS JISC Culture & Vision Ethics & Legal Issues Strategy & Investment Structure & Governance Technology & Data LEARNING ANALYTICS READINESS INSTRUMENT Culture & Processes Data Management Expertise Data Analysis Expertise Governance / Infrastructure Readiness perception Kimberly E. Arnold Steven Lonn Matthew D. Pistilli ANALYTICS MATURITY INDEX
  26. 26. 26 What is Readiness about? Strategy & Vision Culture Governance and Processes Ethics and Legal Issues Investment Technology Data ACTION ANALYTICS MATURITY INDEX
  27. 27. 27 Don’t get trapped by the readiness loop Strategy & Vision Culture Governance and Processes Ethics and Legal Issues Investment Technology Data ACTION Readiness Assessment 1 Start small 2 Don’t focus on technology 3 Go after quick wins 4 Involve from day one all the critical stakeholders Start your seed project!
  28. 28. 28 Technology Challenges Data Capture Not all educational interactions happen in a digital environment Predictions Accuracy All statistical processes draw conclusions subject to an estimated error Partial View Learning processes go beyond what data tell us Data Literacy Analytics consumers need the skills to interpret analytics properly Data Variety Combine data coming from multiple sources and systems Comparable Analytics Not all systems use same algorithms or measure the same way Open standards?
  29. 29. 29 Learning Analytics Ethics Source: Accenture Tecnology: Data Supply Chain DATA ANALISYS ACTION
  30. 30. 30 Learning Analytics Ethics DATA ANALISYS ACTION • Consent • Privacy • Access • Validity • Right to opt out • Safety • Transparency • Accuracy • Validation • Systematic & random errors • Obligation to act • Failure to act • Adverse impact • Abuse / Gaming • Discrimination /social status • Pedagogical impact Complete ethical issues taxonomy by Sclater:
  31. 31. 31 Key Take Aways • Learning analytics are an important asset for institutions providing important benefits but also competitiveness • Institutions must cross the predictive analytics bridge and start influencing the future by changing the present. • Learning analytics are about action. No action = Failure. Get ready to act and change ! • Launch your analytics seed project: start small, don’t focus on technology, go after quick wins, involve stakeholders. • Develop your code of conduct and run analytics protecting all the stakeholders and specially the students.
  32. 32. 32 What raises interest in LA area? Analysis of around 300 posts on the topic 20.11% 11.41% 9.24% 8.70%8.15% 4.89%
  33. 33. 33 For more info … Thanks a lot! Jose A Omedes Research and Development Director