Introduction to Learning Analytics - Framework and Implementation Concerns
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Invited lecture at East China Normal University 12 November 2014
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Introduction to Learning Analytics - Framework and Implementation Concerns
- 1. Introduction to Learning Analytics – Overall Framework and Implementation Concerns Lecture @ ECNU, Shanghai 2014-11-12 Tore Hoel Oslo and Akershus University College of Applied Sciences, Norway
- 2. Creative Commons Attribution-ShareAlike This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
- 3. 3 Largest state university college in Norway. I work mainly with European projects on Learning Analytics and Open Education
- 4. 1. Why Learning Analytics (LA) now? What is LA? LA in Universities, Schools, Workplace Framework model of LA Implementation Concerns About LACE project – more information 4 Outline of my lecture
- 5. Why Learning Analytics now? 5
- 6. The Global Data Race 6 Mastering Big Data will.. • create jobs and new markets • better health-care • lower energy consumption • … … and change education?
- 7. MOOCs (Massive Online Open Courses) 7
- 8. “Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” – Dan Ariely, Facebook, 6 Jan 2013 … and the world of education seems obsessed about it, but the little that does go on is often done badly, and leaves people disillusioned.
- 9. ECNU Professor said to me at lunch: «We have all this data. You have to tell us how to make use of it to improve our teaching!» 9
- 10. First task: 10 What data could be used to tell us more about how you learn? picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853
- 19. What is Learning Analytics? 19
- 20. Photo (CC)-BY-NC-SA tim_d https://www.flickr.com/photos/tim_d/184018928 20 “The most important single factor influencing learning is what the learner already knows. Ascertain this and teach [them] accordingly.” – David Ausubel, 1968
- 21. Learning Analytics defined «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.» Society for Learning Analytics Research (SoLAR) 21 Actionable intelligence! Not Theoretical Insights! Not Reporting!
- 22. “collecting traces that learners leave behind and using those traces to improve learning” - Erik Duval http://erikduval.wordpress.com/2012/01/30/learning-analytics- and-educational-data-mining/ Photo public domain: http://commons.wikimedia.org/wiki/File:DESYNebelkammer.jpg
- 23. “feeding back the data exhaust” Big Data in Education Photo (CC)-BY Iain Watson http://www.flickr.com/photos/dagoaty/3329699788/
- 25. Levels of Learning Analytics (UNESCO Policy Brief, November 2012) 25
- 26. (cc) Doug Clow http://dougclow.org
- 27. LA in Universities 27
- 28. • Predictive modeling – Data mining of Learning Management system (Blackboard) • Place students in one of three risk groups – traffic light / signal / robot • Trigger for intervention emails • Dramatic retention improvements • Consistent grade performance improvement
- 29. “The predictive model was used as a trigger for intervention emails to the student.” Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/ 29
- 30. From: DONOTREPLY@mail.example.com You are in trouble. The computer predictive model gives you a 87.4322% chance of failing this course. You must see a tutor immediately. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/ 30
- 31. From: DONOTREPLY@mail.example.com You are in trouble. The computer predictive model gives you a 87.4322% chance of failing this course. You must see a tutor immediately. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/ 31 Hi Alex Are you Ok? I noticed you haven’t logged on this week, and I know you struggled with the last assessment. We can work through this together - let’s have a chat as soon as possible. Pat.
- 32. LA in Schools 32
- 33. Context • National Curriculum • National testing • League tables • Analytics for tracking and monitoring 33 Photo (CC)-BY Thomas Galvez on Flickr https://www.flickr.com/photos/togawanderings/14212266277
- 34. Example of School dashboards • Maybe chop the first slide about this. 34
- 35. 35
- 36. final words
- 37. 37
- 38. Dispositions analytics • Learning dispositions – Resilience • 7 dimensions of ‘Learning power’: • Resilience • Critical Curiosity • Strategic Awareness • Creativity • Meaning Making • Learning Relationships • Changing and learning 38 Buckingham Shum and Deakin Crick, 2012 (LAK12)
- 39. Teacher view 39 Buckingham Shum and Deakin Crick, 2012 (LAK12)
- 40. LA in the Workplace 40
- 41. 41
- 42. From Data to Insights – a Framework Model of LA 42
- 43. From Data to Insights Data
- 44. From Data to Insights Data Analytics
- 45. From Data to Insights Data Analytics Insight
- 46. From Data to Insights Data Analytics Insight Who? Institution Tutor Self … Educational Commercial
- 47. From Data to Insights Data Analytics Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration
- 48. From Data to Insights Data Analytics Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration What? Platform Service … Availability Access
- 49. From Data to Insights Data Analytics Insight Who? Institution Tutor Self … Educational Commercial How? Social network Discourse Content Disposition Context … Administration What? Platform Service … Availability Access
- 50. 2nd task: Why?
- 51. Critical dimensions of learning analytics (Greller & Drachsler, 2012) 51
- 52. 52
- 53. The LA feedback loop 53
- 54. «Very little online learning takes place in the Learning Management System (LMS), we need to be able to track learning activities everywhere.»
- 55. Some Concerns about Implementation 55
- 56. Some issues have surfaced Glasswinged butterfly, ? Greta oro • Privacy • Data protection and User control • Transparency • Ethics Photo (CC)-BY-NC-ND by Greg Foster on Flickr http://www.flickr.com/photos/gregfoster/3365801458/
- 57. Some ethical challenges • Data Protection • Privacy • Transparency (related to Subject Access requests) • Whether students should be able to opt in/out • De-identification of data • Timeliness and Duty of Care (keeping data up to date) • Access to data (who should have access to the data, etc.) • Students abusing the system by misinformation • The use of student data outside university systems (Social Media) • Analysis of the data and the methods used (what assumptions are used to create the algorithm for the predictive model, should there be an independent audit?) • Purpose of applying a learning analytics approach • Profiling of students • How will it be done? • What do we tell students? • Should we tell students? – Students may feel ‘at-risk’/labelled Glasswinged butterfly, ? Greta oro cc licensed ( BY NC ND ) flickr photo by Greg Foster: http://www.flickr.com/photos/gregfoster/3365801458/
- 58. Chinese - European cooperation – new opportunities 58
- 62. An Open Learning Analytics platform • Open: processes, algorithms, technologies • Transparently validated • Modular (core engines: adaptation, learning, interventions, dashboards) • Standard-based 62
- 63. • 3 years project (2 years to go) funded by European Union Support and coordinate the European LA community Share knowledge and best practices We want to work with you! www.laceproject.eu
- 64. Not everything that can be counted counts. Not everything that counts can be counted. – William Bruce Cameron Not everything that can be counted counts. Not everything that counts can be counted. – William Bruce Cameron 64 Photo (CC)-BY Paul Stainthorp https://www.flickr.com/photos/pstainthorp/5497004025
- 65. Photo (CC)-BY kaybee07 on Flickr https://www.flickr.com/photos/kurtbudiarto/7026555821
- 66. Thanks to: LACE project partner Doug Clow for sharing his slides, found at http://www.slideshare.net/dougclow/learning-analytics-a-general-introduction-and-perspectives- from-the-uk Individual slides are also from other LACE project partners, in particular Rebecca Fergusson and Fabrizio Cardinali. Funders: • LACE: European Commission 619424-FP7-ICT-2013-11
- 67. www.laceproject.eu @laceproject Hoel, T. (2014). «Introduction to Learning Analytics – Overall Framework and Implementation Concerns» – lecture at East China Normal University, Shanghai, China, November 2014 @tore about.me/torehoel tore.hoel@hioa.no This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424. These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms. 67
Hinweis der Redaktion
- I work at the largest state university college in Norway, affiliated with the University Library. I mainly participate in European projects. I coordinate a project on Open Educational Resources (OER) in the Nordic countries. I work with a European Union project on learning analytics, and with another EU project on Open Education. And I have been working with learning technology standardization for more than ten years.
- The backdrop of Learning Analytics is Big Data. This month the European Commission announced a big investment «to strengthen the data sector and put Europe at the forefront of the global data race». The focus is much broader than education, but is is clear that the educational sector will be influence by this focus on Big Data.
- Massive Online Open Courses may be one (but not the only) answer to the need to change education. The European MOOCs Scoreboard shows a strong growth in MOOC offerings worldwide, with Europe slowly catching up. The MOOCs movement had been an important driver for Learning Analytics.
- It is all about learning! If we only could find out how learning works! The American educational psychologist, David Ausubel pointed to the importance of pre-knowledge: What the learner already knows. We should relate to this knowledge. What if you could find that out? May be we could do that if we had Data! The good news: We are starting to have a LOT of data! Learning analytics is new technologies, but it is not a new idea.
- This is a much used definition of LA. The key point here is that we are not doing LA to report on results or to do research, but to get insights that could help us to help the learner to improve his or her learning.
- Photo: Cloud Chamber at the German Electron Synchrotron DESY
- In education – at least now – our data is not big. Most fits in a spreadsheet on your laptop computer! Small data = spreadsheet Medium = laptop with R or other statistics applications Big = need special servers/cloud services
- Without interventions, it may still be good stuff coming out of data analysis: computer science, educational research, business intelligence. But only LA if fed back (actionable intelligence) to change learning og learning environment it is LA. And that is what good teachers always have been doing, but now we have more data, and better techniques.
- LA gives actionable insights to the learner, the institution, as well as to the national level. However, the everything starts (and ends) with the learner.
- Speed, scale, quality of response Get it to the learners and teachers
- This is an example of a LA tool («Signals» from the American Purdue University): Signals is used to give alerts via email from tutor (human connection). It connects to existing support systems.
- “the predictive model was used as a trigger for intervention emails to the student”
- I don’t think a student would have been much motivated by this mail!
- This email or text message feels better, and will probably have much better effect!
- Schools are governed by national curricula; there is national testing; regional authorities compare school performance, etc. Analytics is used for tracking and monitoring – and for coming up with actions.
- There is a massive investment by educational software vendors in analytics tools for schools. One driver is the parents. The schools need an efficient way to communicate with the parents that have an increasing say in the teaching of the children. LMS vendors all have an analytics product.
- Some quick slides to show some screenshots of a specific tool to show what the teacher dashboard may look like. This one show attendance in the class.
- Attainment – the results of the learning process.
- One click on a specific student and get his results.
- The philosopher and educational reformer, John Dewey (who I just learnt was greatly influential in China having his works translated into Chinese nearly 100 years ago), once said: «Knowledge of methods alone will not suffice: there must be the desire, the will, to employ them. This desire is an affair of personal disposition.» Another American learning theorist, John Seely Brown, said: «Dispositions are now at least as important as Knowledge and Skills… They cannot be taught. They can only be cultivated.» With LA we might have got tools to do that cultivations, through what is called Dispositions analytics, applicable in Schools education.
- Cohort dispositional analytics. Building critical self-awareness. Correlations with success measures, but complex relationship. Learning power goes down over time in school!
- In the European LA project we also focus on workplace learning, in particular on the training needs of Smart Manufacturing. With new and always changing production methods we need new training methods; and we need to find the right training mix. 10% in the training room; 20% coaching; and 70% at the workplace seems to be a perfect mix. Then we need to have good analytics tools supporting the work process, fed by realtime performance data.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments: social network analytics — interpersonal relationships define social platforms discourse analytics — language is a primary tool for knowledge negotiation and construction content analytics — user-generated content is one of the defining characteristics of Web 2.0 disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics — mobile computing is transforming access to both people and content.
- Objectives: reflection - prediction Data: Open - Protected Stakeholders: Learners, Teachers, Institutions, Other Internal limitations: Competences, Acceptance External limitations: Conventions, Norms Instruments: Technology, Algorithms, Theories, Other
- Objectives: reflection - prediction Data: Open - Protected Stakeholders: Learners, Teachers, Institutions, Other Internal limitations: Competences, Acceptance External limitations: Conventions, Norms Instruments: Technology, Algorithms, Theories, Other
- In the LACE project, the European community support project, we have asked stakeholders of what might be barriers to adoption of LA. One cluster of issues has emerged, characterised with the words Privacy, Data protection, and Transparency. Privacy – education makes space to fail, make mistakes, and learn from them – and not have that held against you. Data protection and User control – longstanding European legislation, which may prevent schools and universities to introduce new solutions. Perhaps the solutions should be designed with privacy in mind in the first place?! Transparency – to learners, but also to the outside. The goal is to create shared processes. Ethics – it has all to do with ethics!
- Sharon Slade
- We are working with numbers, but the numbers are people. Remember that compassionate, human connection.
- Swan metaphor: There’s a lot that looks impressive, but less elegant beneath the surface. Learning analytics is a new field. You can move forward! Don’t be overawed. Make things better for your learners.